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2315
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101
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14
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2026-05-07T11:10:33.829950+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152233829_m1.jpg...
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PhpStorm
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faVsco.js – HS_local [jiminny@localhost]
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True
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NULL
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monitor_1
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NULL
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NULL
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NULL
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NULL
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelplahl= Support Daily • in 50 mA100% C8DEV (docker)DOCKERLast login: Thu May'*1DEV (docker)7 09:44:56 on ttys006182APP (-zsh)-zsh|Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# 0• 84screenpipe*•$5-zshThu 7 May 14:10:34T₴1₴6DEV...
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NULL
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-1091675985352515552
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NULL
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click
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ocr
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NULL
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelplahl= Support Daily • in 50 mA100% C8DEV (docker)DOCKERLast login: Thu May'*1DEV (docker)7 09:44:56 on ttys006182APP (-zsh)-zsh|Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# 0• 84screenpipe*•$5-zshThu 7 May 14:10:34T₴1₴6DEV...
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NULL
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NULL
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2317
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102
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17
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2026-05-07T11:10:36.652364+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152236652_m2.jpg...
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iTerm2
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DEV (docker)
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True
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NULL
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monitor_2
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NULL
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NULL
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NULL
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NULL
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Last login: Thu May 7 09:44:56 on ttys006
Poetry Last login: Thu May 7 09:44:56 on ttys006
Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents
Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ dev
root@docker_lamp_1:/home/jiminny#
DOCKER
Close Tab
DEV (docker)
Close Tab
APP (-zsh)
Close Tab
-zsh
Close Tab
screenpipe"
Close Tab
-zsh
Close Tab
⌥⌘1
DEV (docker)...
|
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|
2060141590138155039
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-4430738654534144879
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click
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accessibility
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NULL
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Last login: Thu May 7 09:44:56 on ttys006
Poetry Last login: Thu May 7 09:44:56 on ttys006
Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents
Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ dev
root@docker_lamp_1:/home/jiminny#
DOCKER
Close Tab
DEV (docker)
Close Tab
APP (-zsh)
Close Tab
-zsh
Close Tab
screenpipe"
Close Tab
-zsh
Close Tab
⌥⌘1
DEV (docker)...
|
NULL
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NULL
|
NULL
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NULL
|
|
2319
|
101
|
16
|
2026-05-07T11:11:00.788028+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152260788_m1.jpg...
|
iTerm2
|
DEV (docker)
|
True
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NULL
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monitor_1
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NULL
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NULL
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NULL
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NULL
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Last login: Thu May 7 09:44:56 on ttys006
Poetry Last login: Thu May 7 09:44:56 on ttys006
Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents
Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ dev
root@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00'
Syncing opportunity for Hubspot
Your HubSpot account has become disconnected. Please login to Jiminny to reconnect. skipping...
root@docker_lamp_1:/home/jiminny#
DOCKER
Close Tab
DEV (docker)
Close Tab
APP (-zsh)
Close Tab
-zsh
Close Tab
screenpipe"
Close Tab
-zsh
Close Tab
⌥⌘1
DEV (docker)...
|
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Please login to Jiminny to reconnect. skipping...\nroot@docker_lamp_1:/home/jiminny#","depth":4,"bounds":{"left":0.0,"top":0.08777778,"width":1.0,"height":0.9122222},"on_screen":true,"lines":[{"char_start":0,"char_count":43,"bounds":{"left":0.0034722222,"top":0.08777778,"width":0.23888889,"height":0.02}},{"char_start":43,"char_count":1,"bounds":{"left":0.0034722222,"top":0.107777774,"width":0.0055555557,"height":0.02}},{"char_start":44,"char_count":87,"bounds":{"left":0.0034722222,"top":0.12777779,"width":0.48333332,"height":0.02}},{"char_start":131,"char_count":1,"bounds":{"left":0.0034722222,"top":0.14777778,"width":0.0055555557,"height":0.02}},{"char_start":132,"char_count":87,"bounds":{"left":0.0034722222,"top":0.16777778,"width":0.48333332,"height":0.02}},{"char_start":219,"char_count":114,"bounds":{"left":0.0034722222,"top":0.18777777,"width":0.6333333,"height":0.02}},{"char_start":333,"char_count":107,"bounds":{"left":0.0034722222,"top":0.20777778,"width":0.59444445,"height":0.02}},{"char_start":440,"char_count":32,"bounds":{"left":0.0034722222,"top":0.22777778,"width":0.17777778,"height":0.02}},{"char_start":472,"char_count":97,"bounds":{"left":0.0034722222,"top":0.24777777,"width":0.5388889,"height":0.02}},{"char_start":569,"char_count":33,"bounds":{"left":0.0034722222,"top":0.26777777,"width":0.18333334,"height":0.02}}],"value":"Last login: Thu May 7 09:44:56 on ttys006\n\nPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents\n\nPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ dev\nroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00'\nSyncing opportunity for Hubspot\nYour HubSpot account has become disconnected. Please login to Jiminny to reconnect. skipping...\nroot@docker_lamp_1:/home/jiminny#","is_focused":true},{"role":"AXRadioButton","text":"DOCKER","depth":2,"bounds":{"left":0.0,"top":0.05888889,"width":0.16458334,"height":0.026666667},"on_screen":true,"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.004166667,"top":0.06333333,"width":0.011111111,"height":0.017777778},"on_screen":true,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"DEV (docker)","depth":2,"bounds":{"left":0.16458334,"top":0.05888889,"width":0.16458334,"height":0.026666667},"on_screen":true,"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.16875,"top":0.06333333,"width":0.011111111,"height":0.017777778},"on_screen":true,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"APP (-zsh)","depth":2,"bounds":{"left":0.32916668,"top":0.05888889,"width":0.16423611,"height":0.026666667},"on_screen":true,"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.33333334,"top":0.06333333,"width":0.011111111,"height":0.017777778},"on_screen":true,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"-zsh","depth":2,"bounds":{"left":0.49340278,"top":0.05888889,"width":0.16423611,"height":0.026666667},"on_screen":true,"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.49756944,"top":0.06333333,"width":0.011111111,"height":0.017777778},"on_screen":true,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"screenpipe\"","depth":2,"bounds":{"left":0.6576389,"top":0.05888889,"width":0.16423611,"height":0.026666667},"on_screen":true,"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.66180557,"top":0.06333333,"width":0.011111111,"height":0.017777778},"on_screen":true,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"-zsh","depth":2,"bounds":{"left":0.821875,"top":0.05888889,"width":0.16423611,"height":0.026666667},"on_screen":true,"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.82604164,"top":0.06333333,"width":0.011111111,"height":0.017777778},"on_screen":true,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"⌥⌘1","depth":1,"bounds":{"left":0.95763886,"top":0.032222223,"width":0.03888889,"height":0.018888889},"on_screen":true,"automation_id":"_NS:8","role_description":"text"},{"role":"AXStaticText","text":"DEV (docker)","depth":1,"bounds":{"left":0.47013888,"top":0.033333335,"width":0.0625,"height":0.017777778},"on_screen":true,"role_description":"text"}]...
|
5309715140879840894
|
4792633382030961947
|
visual_change
|
accessibility
|
NULL
|
Last login: Thu May 7 09:44:56 on ttys006
Poetry Last login: Thu May 7 09:44:56 on ttys006
Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents
Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ dev
root@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00'
Syncing opportunity for Hubspot
Your HubSpot account has become disconnected. Please login to Jiminny to reconnect. skipping...
root@docker_lamp_1:/home/jiminny#
DOCKER
Close Tab
DEV (docker)
Close Tab
APP (-zsh)
Close Tab
-zsh
Close Tab
screenpipe"
Close Tab
-zsh
Close Tab
⌥⌘1
DEV (docker)...
|
NULL
|
NULL
|
NULL
|
NULL
|
|
2321
|
101
|
18
|
2026-05-07T11:11:09.938493+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152269938_m1.jpg...
|
iTerm2
|
DEV (docker)
|
True
|
NULL
|
monitor_1
|
NULL
|
NULL
|
NULL
|
NULL
|
Last login: Thu May 7 09:44:56 on ttys006
Poetry Last login: Thu May 7 09:44:56 on ttys006
Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents
Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ dev
root@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00'
Syncing opportunity for Hubspot
Your HubSpot account has become disconnected. Please login to Jiminny to reconnect. skipping...
root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 36116
DOCKER
Close Tab
DEV (docker)
Close Tab
APP (-zsh)
Close Tab
-zsh
Close Tab
screenpipe"
Close Tab
-zsh
Close Tab
⌥⌘1
DEV (docker)...
|
[{"role":"AXTextArea","text [{"role":"AXTextArea","text":"Last login: Thu May 7 09:44:56 on ttys006\n\nPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents\n\nPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ dev\nroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00'\nSyncing opportunity for Hubspot\nYour HubSpot account has become disconnected. Please login to Jiminny to reconnect. skipping...\nroot@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 36116","depth":4,"bounds":{"left":0.0,"top":0.08777778,"width":1.0,"height":0.9122222},"on_screen":true,"lines":[{"char_start":0,"char_count":43,"bounds":{"left":0.0034722222,"top":0.08777778,"width":0.23888889,"height":0.02}},{"char_start":43,"char_count":1,"bounds":{"left":0.0034722222,"top":0.107777774,"width":0.0055555557,"height":0.02}},{"char_start":44,"char_count":87,"bounds":{"left":0.0034722222,"top":0.12777779,"width":0.48333332,"height":0.02}},{"char_start":131,"char_count":1,"bounds":{"left":0.0034722222,"top":0.14777778,"width":0.0055555557,"height":0.02}},{"char_start":132,"char_count":87,"bounds":{"left":0.0034722222,"top":0.16777778,"width":0.48333332,"height":0.02}},{"char_start":219,"char_count":114,"bounds":{"left":0.0034722222,"top":0.18777777,"width":0.6333333,"height":0.02}},{"char_start":333,"char_count":107,"bounds":{"left":0.0034722222,"top":0.20777778,"width":0.59444445,"height":0.02}},{"char_start":440,"char_count":32,"bounds":{"left":0.0034722222,"top":0.22777778,"width":0.17777778,"height":0.02}},{"char_start":472,"char_count":97,"bounds":{"left":0.0034722222,"top":0.24777777,"width":0.5388889,"height":0.02}},{"char_start":569,"char_count":73,"bounds":{"left":0.0034722222,"top":0.26777777,"width":0.40555555,"height":0.02}}],"value":"Last login: Thu May 7 09:44:56 on ttys006\n\nPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents\n\nPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents\nlukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ dev\nroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00'\nSyncing opportunity for Hubspot\nYour HubSpot account has become disconnected. Please login to Jiminny to reconnect. skipping...\nroot@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 36116","is_focused":true},{"role":"AXRadioButton","text":"DOCKER","depth":2,"bounds":{"left":0.0,"top":0.05888889,"width":0.16458334,"height":0.026666667},"on_screen":true,"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.004166667,"top":0.06333333,"width":0.011111111,"height":0.017777778},"on_screen":true,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"DEV (docker)","depth":2,"bounds":{"left":0.16458334,"top":0.05888889,"width":0.16458334,"height":0.026666667},"on_screen":true,"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.16875,"top":0.06333333,"width":0.011111111,"height":0.017777778},"on_screen":true,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"APP (-zsh)","depth":2,"bounds":{"left":0.32916668,"top":0.05888889,"width":0.16423611,"height":0.026666667},"on_screen":true,"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.33333334,"top":0.06333333,"width":0.011111111,"height":0.017777778},"on_screen":true,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"-zsh","depth":2,"bounds":{"left":0.49340278,"top":0.05888889,"width":0.16423611,"height":0.026666667},"on_screen":true,"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.49756944,"top":0.06333333,"width":0.011111111,"height":0.017777778},"on_screen":true,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"screenpipe\"","depth":2,"bounds":{"left":0.6576389,"top":0.05888889,"width":0.16423611,"height":0.026666667},"on_screen":true,"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.66180557,"top":0.06333333,"width":0.011111111,"height":0.017777778},"on_screen":true,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXRadioButton","text":"-zsh","depth":2,"bounds":{"left":0.821875,"top":0.05888889,"width":0.16423611,"height":0.026666667},"on_screen":true,"role_description":"radio button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Close Tab","depth":3,"bounds":{"left":0.82604164,"top":0.06333333,"width":0.011111111,"height":0.017777778},"on_screen":true,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"⌥⌘1","depth":1,"bounds":{"left":0.95763886,"top":0.032222223,"width":0.03888889,"height":0.018888889},"on_screen":true,"automation_id":"_NS:8","role_description":"text"},{"role":"AXStaticText","text":"DEV (docker)","depth":1,"bounds":{"left":0.47013888,"top":0.033333335,"width":0.0625,"height":0.017777778},"on_screen":true,"role_description":"text"}]...
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1995314925648012511
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4792633377753032979
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visual_change
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accessibility
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NULL
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Last login: Thu May 7 09:44:56 on ttys006
Poetry Last login: Thu May 7 09:44:56 on ttys006
Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents
Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parents
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ dev
root@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00'
Syncing opportunity for Hubspot
Your HubSpot account has become disconnected. Please login to Jiminny to reconnect. skipping...
root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 36116
DOCKER
Close Tab
DEV (docker)
Close Tab
APP (-zsh)
Close Tab
-zsh
Close Tab
screenpipe"
Close Tab
-zsh
Close Tab
⌥⌘1
DEV (docker)...
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NULL
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NULL
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NULL
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NULL
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2324
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101
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20
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2026-05-07T11:11:24.976079+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152284976_m1.jpg...
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PhpStorm
|
faVsco.js – HS_local [jiminny@localhost]
|
True
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NULL
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monitor_1
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NULL
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NULL
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NULL
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NULL
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Project: faVsco.js, menu
master, menu
Start Listen Project: faVsco.js, menu
master, menu
Start Listening for PHP Debug Connections
AskJiminnyReportActivityServiceTest
Run 'AskJiminnyReportActivityServiceTest'
Debug 'AskJiminnyReportActivityServiceTest'
More Actions
JetBrains AI
Search Everywhere
IDE and Project Settings
Execute
Explain Plan
Browse Query History
View Parameters
Open Query Execution Settings…
In-Editor Results
Tx: Auto
Cancel Running Statements
Playground
jiminny
Sync Changes
Hide This Notification
Code changed:
Hide
6
1
6
Previous Highlighted Error
Next Highlighted Error
# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] RateLimitInterface $rateLimit */
foreach ($provider->getRateLimits() as $rateLimit) {
$key = $rateLimit->getKey();
if ($this->rateLimiter->tooManyAttempts($key, $rateLimit->getQuota())) {
return false;
}
}
return true;
}
public function requestAvailableIn(RateLimited $provider): int
{
return $provider->getRateLimits()->isNotEmpty()
? $provider->getRateLimits()
->map(fn (RateLimitInterface $rateLimit): int => $this->rateLimiter->availableIn($rateLimit->getKey()))
->max()
: 0
;
}
public function incrementRequestCount(RateLimited $provider): void
{
/** @var RateLimitInterface $rateLimit */
foreach ($provider->getRateLimits() as $rateLimit) {
$this->rateLimiter->hit($rateLimit->getKey(), $rateLimit->getWindow());
}
}
}
Project
Project
New File or Directory…
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|
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Error","depth":4,"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXTextArea","text":"# **************************** HS **************************************\n\nselect * from teams where id = 2; # 2\nselect * from features; # 2\nselect * from team_features where team_id = 2; # 2\nselect * from crm_configurations where id = 2; # 2\nselect * from users where team_id = 2; #\nselect * from playbooks where team_id = 2; # event 38\nselect * from playbook_categories where playbook_id = 38; #\n\nSELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;\nhttps://app.hubspot.com/contacts/4392066/deal/16964514951/?engagement=96069102624\n https://app.staging.jiminny.com/playback/d5df34dc-bd66-4ff5-a7b3-8d3be30322a0\n\nSELECT * FROM activities WHERE uuid_to_bin('04fdcd0d-818f-4c53-92dc-6f18bc753ffd') = uuid;\n# 609126 softphone tr. 11241\n\nSELECT * FROM activities WHERE uuid_to_bin('6521bfcd-5a30-46e5-9f74-5440fd48befd') = uuid;\n# 608874 conference tr. 11226 crmId: 103422236596\n\nselect * from ai_prompts where transcription_id IN (11241, 11226);\nselect * from activity_summary_logs where activity_id = 608874;\n\nselect * from sidekick_settings;\nselect * from default_activity_types;\n\nselect * from crm_field_data where activity_id = 1223;\n\nselect * from crm_layouts where crm_configuration_id = 2;\nSELECT * FROM crm_layout_entities WHERE crm_layout_id IN (554);\nselect * from crm_fields where crm_configuration_id = 11 and object_type = 'event';\nSELECT * FROM crm_field_values WHERE crm_field_id IN (1455,1450);\n\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id = 971;\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id IN (6494,6495,6496,6497,6498,6499);\n\nSELECT\n CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,\n u.email,\n sa.*,\n t.owner_id FROM social_accounts sa\nJOIN users u\n on u.id = sa.sociable_id\nJOIN teams t on t.id = u.team_id\nWHERE u.team_id = 2 and sa.provider = 'hubspot';\n\nselect * from social_accounts where id = 1499;\n\nselect * from opportunities where team_id = 2\nand crm_provider_id IN ('51317301383');\n\nselect * from contacts where id = 85;\n\nselect * from opportunities where team_id = 2 order by id desc;\nselect * from opportunities where team_id = 2 and crm_provider_id = '51317301383'; # 5112\nselect * from opportunities where team_id = 2 and crm_provider_id = '55976759904'; # 5112\nselect * from opportunity_contacts where opportunity_id = 5117;\nselect * from crm_field_data where object_id = 1365;\nSELECT * FROM crm_fields WHERE id IN (1405, 1407, 1972, 2128);\n\nselect * from features;\nselect * from team_features where team_id IN (1);\nselect * from team_features where feature_id IN (36);\n\nSHOW CREATE TABLE opportunity_contacts;\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '111751';\n\n# $slug = 'HUBSPOT_WEBHOOK_SYNC';\n# $team = Jiminny\\Models\\Team::find(2);\n# $feature = Feature::query()->where('slug', $slug)->first();\n# TeamFeature::query()->create(['feature_id' => $feature->getId(),'team_id' => $team->getId()]);\n\n# hubspot_webhook_metrics\n\nselect * from opportunities where team_id = 2 and crm_provider_id IN ('374720564','14527423589','49908861993','50435771779'); # 1365\nSELECT * FROM opportunity_contacts WHERE opportunity_id = '414';\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '131501';\nselect * from contacts where id in (414, 464);\n\nselect * from activities where crm_configuration_id = 2;\n\nselect settings from crm_configurations where id = 11;\n\nselect * from teams; # 1, 2\nselect * from users;\nselect * from crm_configurations where id = 39;\nselect * from team_features where team_id = 2;\nselect * from features;\n# SELECT * FROM opportunities WHERE crm_configuration_id = 2\n# order by id desc;\n# and crm_provider_id = '49908861993';\n\n\nselect * from activity_providers where id IN (443, 202, 203, 227);\n\nselect * from activity_imports where id = 795889;\n\nselect c.id, c.provider, c.settings, t.* from teams t join crm_configurations c on t.id = c.team_id\nwhere c.provider = 'hubspot';\n\nselect * from crm_configurations crm JOIN teams t on crm.team_id = t.id\nwhere provider = 'hubspot';\nSELECT * FROM teams WHERE id = 31;\nSELECT * FROM users WHERE id = 257;\nSELECT * FROM opportunities WHERE team_id = 2;\n\nselect * from opportunity_contacts where opportunity_id = 5124;\nselect * from contacts where id IN (3850,3853,3851,4073,4140,4155,4480,4530,4623,5986,513,687,1806,1523,3613)\n\nselect * from activities where crm_configuration_id = 13;\n\nSELECT * FROM activities WHERE uuid_to_bin('826619ce-ec8e-4e59-8467-a01f5f6ad71e') = uuid; # 418141\n\n\nselect id, team_id, crm_provider_id from crm_configurations where provider = 'hubspot' and crm_provider_id IS NOT NULL;\nSELECT * FROM accounts WHERE team_id = 2 and crm_provider_id = '1212213464' order by id desc;\nSELECT * FROM contacts WHERE team_id = 2 and account_id = 5189 order by id desc;\nSELECT * FROM contacts WHERE team_id = 2 order by id desc;\nselect * from opportunity_contacts where contact_id = 6223;\nSELECT * FROM opportunities WHERE team_id = 2 and account_id = 5189 order by id desc;\n\nselect * from crm_profiles where crm_configuration_id = 2;\n\nselect * from activities where account_id = 46;","depth":4,"on_screen":true,"value":"# **************************** HS **************************************\n\nselect * from teams where id = 2; # 2\nselect * from features; # 2\nselect * from team_features where team_id = 2; # 2\nselect * from crm_configurations where id = 2; # 2\nselect * from users where team_id = 2; #\nselect * from playbooks where team_id = 2; # event 38\nselect * from playbook_categories where playbook_id = 38; #\n\nSELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;\nhttps://app.hubspot.com/contacts/4392066/deal/16964514951/?engagement=96069102624\n https://app.staging.jiminny.com/playback/d5df34dc-bd66-4ff5-a7b3-8d3be30322a0\n\nSELECT * FROM activities WHERE uuid_to_bin('04fdcd0d-818f-4c53-92dc-6f18bc753ffd') = uuid;\n# 609126 softphone tr. 11241\n\nSELECT * FROM activities WHERE uuid_to_bin('6521bfcd-5a30-46e5-9f74-5440fd48befd') = uuid;\n# 608874 conference tr. 11226 crmId: 103422236596\n\nselect * from ai_prompts where transcription_id IN (11241, 11226);\nselect * from activity_summary_logs where activity_id = 608874;\n\nselect * from sidekick_settings;\nselect * from default_activity_types;\n\nselect * from crm_field_data where activity_id = 1223;\n\nselect * from crm_layouts where crm_configuration_id = 2;\nSELECT * FROM crm_layout_entities WHERE crm_layout_id IN (554);\nselect * from crm_fields where crm_configuration_id = 11 and object_type = 'event';\nSELECT * FROM crm_field_values WHERE crm_field_id IN (1455,1450);\n\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id = 971;\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id IN (6494,6495,6496,6497,6498,6499);\n\nSELECT\n CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,\n u.email,\n sa.*,\n t.owner_id FROM social_accounts sa\nJOIN users u\n on u.id = sa.sociable_id\nJOIN teams t on t.id = u.team_id\nWHERE u.team_id = 2 and sa.provider = 'hubspot';\n\nselect * from social_accounts where id = 1499;\n\nselect * from opportunities where team_id = 2\nand crm_provider_id IN ('51317301383');\n\nselect * from contacts where id = 85;\n\nselect * from opportunities where team_id = 2 order by id desc;\nselect * from opportunities where team_id = 2 and crm_provider_id = '51317301383'; # 5112\nselect * from opportunities where team_id = 2 and crm_provider_id = '55976759904'; # 5112\nselect * from opportunity_contacts where opportunity_id = 5117;\nselect * from crm_field_data where object_id = 1365;\nSELECT * FROM crm_fields WHERE id IN (1405, 1407, 1972, 2128);\n\nselect * from features;\nselect * from team_features where team_id IN (1);\nselect * from team_features where feature_id IN (36);\n\nSHOW CREATE TABLE opportunity_contacts;\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '111751';\n\n# $slug = 'HUBSPOT_WEBHOOK_SYNC';\n# $team = Jiminny\\Models\\Team::find(2);\n# $feature = Feature::query()->where('slug', $slug)->first();\n# TeamFeature::query()->create(['feature_id' => $feature->getId(),'team_id' => $team->getId()]);\n\n# hubspot_webhook_metrics\n\nselect * from opportunities where team_id = 2 and crm_provider_id IN ('374720564','14527423589','49908861993','50435771779'); # 1365\nSELECT * FROM opportunity_contacts WHERE opportunity_id = '414';\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '131501';\nselect * from contacts where id in (414, 464);\n\nselect * from activities where crm_configuration_id = 2;\n\nselect settings from crm_configurations where id = 11;\n\nselect * from teams; # 1, 2\nselect * from users;\nselect * from crm_configurations where id = 39;\nselect * from team_features where team_id = 2;\nselect * from features;\n# SELECT * FROM opportunities WHERE crm_configuration_id = 2\n# order by id desc;\n# and crm_provider_id = '49908861993';\n\n\nselect * from activity_providers where id IN (443, 202, 203, 227);\n\nselect * from activity_imports where id = 795889;\n\nselect c.id, c.provider, c.settings, t.* from teams t join crm_configurations c on t.id = c.team_id\nwhere c.provider = 'hubspot';\n\nselect * from crm_configurations crm JOIN teams t on crm.team_id = t.id\nwhere provider = 'hubspot';\nSELECT * FROM teams WHERE id = 31;\nSELECT * FROM users WHERE id = 257;\nSELECT * FROM opportunities WHERE team_id = 2;\n\nselect * from opportunity_contacts where opportunity_id = 5124;\nselect * from contacts where id IN (3850,3853,3851,4073,4140,4155,4480,4530,4623,5986,513,687,1806,1523,3613)\n\nselect * from activities where crm_configuration_id = 13;\n\nSELECT * FROM activities WHERE uuid_to_bin('826619ce-ec8e-4e59-8467-a01f5f6ad71e') = uuid; # 418141\n\n\nselect id, team_id, crm_provider_id from crm_configurations where provider = 'hubspot' and crm_provider_id IS NOT NULL;\nSELECT * FROM accounts WHERE team_id = 2 and crm_provider_id = '1212213464' order by id desc;\nSELECT * FROM contacts WHERE team_id = 2 and account_id = 5189 order by id desc;\nSELECT * FROM contacts WHERE team_id = 2 order by id desc;\nselect * from opportunity_contacts where contact_id = 6223;\nSELECT * FROM opportunities WHERE team_id = 2 and account_id = 5189 order by id desc;\n\nselect * from crm_profiles where crm_configuration_id = 2;\n\nselect * from activities where account_id = 46;","role_description":"text entry area","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Sync Changes","depth":4,"bounds":{"left":0.0,"top":0.0,"width":0.018055556,"height":0.026666667},"on_screen":false,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Hide This Notification","depth":4,"bounds":{"left":0.0,"top":0.0,"width":0.018055556,"height":0.026666667},"on_screen":false,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Code changed:","depth":4,"bounds":{"left":0.0,"top":0.0,"width":0.088194445,"height":0.027777778},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Hide","depth":4,"bounds":{"left":0.0,"top":0.0,"width":0.018055556,"height":0.026666667},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXTextArea","text":"<?php\n\ndeclare(strict_types=1);\n\nnamespace Jiminny\\Component\\Utility\\Service;\n\nuse Illuminate\\Cache\\RateLimiter;\nuse Jiminny\\Contracts\\Http\\RateLimited;\nuse Jiminny\\Contracts\\Http\\RateLimitInterface;\n\nclass ProviderRateLimiter\n{\n protected RateLimiter $rateLimiter;\n\n public function __construct(RateLimiter $rateLimiter)\n {\n $this->rateLimiter = $rateLimiter;\n }\n\n public function canMakeRequest(RateLimited $provider): bool\n {\n /** @var RateLimitInterface $rateLimit */\n foreach ($provider->getRateLimits() as $rateLimit) {\n $key = $rateLimit->getKey();\n\n if ($this->rateLimiter->tooManyAttempts($key, $rateLimit->getQuota())) {\n return false;\n }\n }\n\n return true;\n }\n\n public function requestAvailableIn(RateLimited $provider): int\n {\n return $provider->getRateLimits()->isNotEmpty()\n ? $provider->getRateLimits()\n ->map(fn (RateLimitInterface $rateLimit): int => $this->rateLimiter->availableIn($rateLimit->getKey()))\n ->max()\n : 0\n ;\n }\n\n public function incrementRequestCount(RateLimited $provider): void\n {\n /** @var RateLimitInterface $rateLimit */\n foreach ($provider->getRateLimits() as $rateLimit) {\n $this->rateLimiter->hit($rateLimit->getKey(), $rateLimit->getWindow());\n }\n }\n}","depth":4,"on_screen":true,"value":"<?php\n\ndeclare(strict_types=1);\n\nnamespace Jiminny\\Component\\Utility\\Service;\n\nuse Illuminate\\Cache\\RateLimiter;\nuse Jiminny\\Contracts\\Http\\RateLimited;\nuse Jiminny\\Contracts\\Http\\RateLimitInterface;\n\nclass ProviderRateLimiter\n{\n protected RateLimiter $rateLimiter;\n\n public function __construct(RateLimiter $rateLimiter)\n {\n $this->rateLimiter = $rateLimiter;\n }\n\n public function canMakeRequest(RateLimited $provider): bool\n {\n /** @var RateLimitInterface $rateLimit */\n foreach ($provider->getRateLimits() as $rateLimit) {\n $key = $rateLimit->getKey();\n\n if ($this->rateLimiter->tooManyAttempts($key, $rateLimit->getQuota())) {\n return false;\n }\n }\n\n return true;\n }\n\n public function requestAvailableIn(RateLimited $provider): int\n {\n return $provider->getRateLimits()->isNotEmpty()\n ? $provider->getRateLimits()\n ->map(fn (RateLimitInterface $rateLimit): int => $this->rateLimiter->availableIn($rateLimit->getKey()))\n ->max()\n : 0\n ;\n }\n\n public function incrementRequestCount(RateLimited $provider): void\n {\n /** @var RateLimitInterface $rateLimit */\n foreach ($provider->getRateLimits() as $rateLimit) {\n $this->rateLimiter->hit($rateLimit->getKey(), $rateLimit->getWindow());\n }\n }\n}","role_description":"text entry area","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Project","depth":3,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Project","depth":3,"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"New File or Directory…","depth":4,"bounds":{"left":0.0,"top":0.0,"width":0.018055556,"height":0.026666667},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Expand Selected","depth":4,"bounds":{"left":0.0,"top":0.0,"width":0.018055556,"height":0.026666667},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Collapse All","depth":4,"bounds":{"left":0.0,"top":0.0,"width":0.018055556,"height":0.026666667},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Options","depth":4,"bounds":{"left":0.0,"top":0.0,"width":0.018055556,"height":0.026666667},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Hide","depth":4,"bounds":{"left":0.0,"top":0.0,"width":0.018055556,"height":0.026666667},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false}]...
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Project: faVsco.js, menu
master, menu
Start Listen Project: faVsco.js, menu
master, menu
Start Listening for PHP Debug Connections
AskJiminnyReportActivityServiceTest
Run 'AskJiminnyReportActivityServiceTest'
Debug 'AskJiminnyReportActivityServiceTest'
More Actions
JetBrains AI
Search Everywhere
IDE and Project Settings
Execute
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View Parameters
Open Query Execution Settings…
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Tx: Auto
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# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] RateLimitInterface $rateLimit */
foreach ($provider->getRateLimits() as $rateLimit) {
$key = $rateLimit->getKey();
if ($this->rateLimiter->tooManyAttempts($key, $rateLimit->getQuota())) {
return false;
}
}
return true;
}
public function requestAvailableIn(RateLimited $provider): int
{
return $provider->getRateLimits()->isNotEmpty()
? $provider->getRateLimits()
->map(fn (RateLimitInterface $rateLimit): int => $this->rateLimiter->availableIn($rateLimit->getKey()))
->max()
: 0
;
}
public function incrementRequestCount(RateLimited $provider): void
{
/** @var RateLimitInterface $rateLimit */
foreach ($provider->getRateLimits() as $rateLimit) {
$this->rateLimiter->hit($rateLimit->getKey(), $rateLimit->getWindow());
}
}
}
Project
Project
New File or Directory…
Expand Selected
Collapse All
Options
Hide...
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NULL
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NULL
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NULL
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NULL
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2325
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102
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19
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2026-05-07T11:11:24.843084+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152284843_m2.jpg...
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PhpStorm
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faVsco.js – HS_local [jiminny@localhost]
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True
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NULL
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NULL
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NULL
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NULL
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NULL
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Project: faVsco.js, menu
master, menu
Start Listen Project: faVsco.js, menu
master, menu
Start Listening for PHP Debug Connections
AskJiminnyReportActivityServiceTest
Run 'AskJiminnyReportActivityServiceTest'
Debug 'AskJiminnyReportActivityServiceTest'
More Actions
JetBrains AI
Search Everywhere
IDE and Project Settings
Execute
Explain Plan
Browse Query History
View Parameters
Open Query Execution Settings…
In-Editor Results
Tx: Auto
Cancel Running Statements
Playground
jiminny
Sync Changes
Hide This Notification
Code changed:
Hide
6
1
6
Previous Highlighted Error
Next Highlighted Error
# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] RateLimitInterface $rateLimit */
foreach ($provider->getRateLimits() as $rateLimit) {
$key = $rateLimit->getKey();
if ($this->rateLimiter->tooManyAttempts($key, $rateLimit->getQuota())) {
return false;
}
}
return true;
}
public function requestAvailableIn(RateLimited $provider): int
{
return $provider->getRateLimits()->isNotEmpty()
? $provider->getRateLimits()
->map(fn (RateLimitInterface $rateLimit): int => $this->rateLimiter->availableIn($rateLimit->getKey()))
->max()
: 0
;
}
public function incrementRequestCount(RateLimited $provider): void
{
/** @var RateLimitInterface $rateLimit */
foreach ($provider->getRateLimits() as $rateLimit) {
$this->rateLimiter->hit($rateLimit->getKey(), $rateLimit->getWindow());
}
}
}
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|
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Statements","depth":4,"bounds":{"left":0.46841756,"top":0.09896249,"width":0.008643617,"height":0.01915403},"on_screen":true,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Playground","depth":4,"bounds":{"left":0.4793883,"top":0.09896249,"width":0.029587766,"height":0.01915403},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"jiminny","depth":4,"bounds":{"left":0.65359044,"top":0.09896249,"width":0.02825798,"height":0.01915403},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Sync 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Highlighted Error","depth":4,"bounds":{"left":0.66888297,"top":0.12210695,"width":0.00731383,"height":0.018355945},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Next Highlighted Error","depth":4,"bounds":{"left":0.6761968,"top":0.12210695,"width":0.006981383,"height":0.018355945},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXTextArea","text":"# **************************** HS **************************************\n\nselect * from teams where id = 2; # 2\nselect * from features; # 2\nselect * from team_features where team_id = 2; # 2\nselect * from crm_configurations where id = 2; # 2\nselect * from users where team_id = 2; #\nselect * from playbooks where team_id = 2; # event 38\nselect * from playbook_categories where playbook_id = 38; #\n\nSELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;\nhttps://app.hubspot.com/contacts/4392066/deal/16964514951/?engagement=96069102624\n https://app.staging.jiminny.com/playback/d5df34dc-bd66-4ff5-a7b3-8d3be30322a0\n\nSELECT * FROM activities WHERE uuid_to_bin('04fdcd0d-818f-4c53-92dc-6f18bc753ffd') = uuid;\n# 609126 softphone tr. 11241\n\nSELECT * FROM activities WHERE uuid_to_bin('6521bfcd-5a30-46e5-9f74-5440fd48befd') = uuid;\n# 608874 conference tr. 11226 crmId: 103422236596\n\nselect * from ai_prompts where transcription_id IN (11241, 11226);\nselect * from activity_summary_logs where activity_id = 608874;\n\nselect * from sidekick_settings;\nselect * from default_activity_types;\n\nselect * from crm_field_data where activity_id = 1223;\n\nselect * from crm_layouts where crm_configuration_id = 2;\nSELECT * FROM crm_layout_entities WHERE crm_layout_id IN (554);\nselect * from crm_fields where crm_configuration_id = 11 and object_type = 'event';\nSELECT * FROM crm_field_values WHERE crm_field_id IN (1455,1450);\n\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id = 971;\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id IN (6494,6495,6496,6497,6498,6499);\n\nSELECT\n CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,\n u.email,\n sa.*,\n t.owner_id FROM social_accounts sa\nJOIN users u\n on u.id = sa.sociable_id\nJOIN teams t on t.id = u.team_id\nWHERE u.team_id = 2 and sa.provider = 'hubspot';\n\nselect * from social_accounts where id = 1499;\n\nselect * from opportunities where team_id = 2\nand crm_provider_id IN ('51317301383');\n\nselect * from contacts where id = 85;\n\nselect * from opportunities where team_id = 2 order by id desc;\nselect * from opportunities where team_id = 2 and crm_provider_id = '51317301383'; # 5112\nselect * from opportunities where team_id = 2 and crm_provider_id = '55976759904'; # 5112\nselect * from opportunity_contacts where opportunity_id = 5117;\nselect * from crm_field_data where object_id = 1365;\nSELECT * FROM crm_fields WHERE id IN (1405, 1407, 1972, 2128);\n\nselect * from features;\nselect * from team_features where team_id IN (1);\nselect * from team_features where feature_id IN (36);\n\nSHOW CREATE TABLE opportunity_contacts;\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '111751';\n\n# $slug = 'HUBSPOT_WEBHOOK_SYNC';\n# $team = Jiminny\\Models\\Team::find(2);\n# $feature = Feature::query()->where('slug', $slug)->first();\n# TeamFeature::query()->create(['feature_id' => $feature->getId(),'team_id' => $team->getId()]);\n\n# hubspot_webhook_metrics\n\nselect * from opportunities where team_id = 2 and crm_provider_id IN ('374720564','14527423589','49908861993','50435771779'); # 1365\nSELECT * FROM opportunity_contacts WHERE opportunity_id = '414';\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '131501';\nselect * from contacts where id in (414, 464);\n\nselect * from activities where crm_configuration_id = 2;\n\nselect settings from crm_configurations where id = 11;\n\nselect * from teams; # 1, 2\nselect * from users;\nselect * from crm_configurations where id = 39;\nselect * from team_features where team_id = 2;\nselect * from features;\n# SELECT * FROM opportunities WHERE crm_configuration_id = 2\n# order by id desc;\n# and crm_provider_id = '49908861993';\n\n\nselect * from activity_providers where id IN (443, 202, 203, 227);\n\nselect * from activity_imports where id = 795889;\n\nselect c.id, c.provider, c.settings, t.* from teams t join crm_configurations c on t.id = c.team_id\nwhere c.provider = 'hubspot';\n\nselect * from crm_configurations crm JOIN teams t on crm.team_id = t.id\nwhere provider = 'hubspot';\nSELECT * FROM teams WHERE id = 31;\nSELECT * FROM users WHERE id = 257;\nSELECT * FROM opportunities WHERE team_id = 2;\n\nselect * from opportunity_contacts where opportunity_id = 5124;\nselect * from contacts where id IN (3850,3853,3851,4073,4140,4155,4480,4530,4623,5986,513,687,1806,1523,3613)\n\nselect * from activities where crm_configuration_id = 13;\n\nSELECT * FROM activities WHERE uuid_to_bin('826619ce-ec8e-4e59-8467-a01f5f6ad71e') = uuid; # 418141\n\n\nselect id, team_id, crm_provider_id from crm_configurations where provider = 'hubspot' and crm_provider_id IS NOT NULL;\nSELECT * FROM accounts WHERE team_id = 2 and crm_provider_id = '1212213464' order by id desc;\nSELECT * FROM contacts WHERE team_id = 2 and account_id = 5189 order by id desc;\nSELECT * FROM contacts WHERE team_id = 2 order by id desc;\nselect * from opportunity_contacts where contact_id = 6223;\nSELECT * FROM opportunities WHERE team_id = 2 and account_id = 5189 order by id desc;\n\nselect * from crm_profiles where crm_configuration_id = 2;\n\nselect * from activities where account_id = 46;","depth":4,"on_screen":true,"value":"# **************************** HS **************************************\n\nselect * from teams where id = 2; # 2\nselect * from features; # 2\nselect * from team_features where team_id = 2; # 2\nselect * from crm_configurations where id = 2; # 2\nselect * from users where team_id = 2; #\nselect * from playbooks where team_id = 2; # event 38\nselect * from playbook_categories where playbook_id = 38; #\n\nSELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;\nhttps://app.hubspot.com/contacts/4392066/deal/16964514951/?engagement=96069102624\n https://app.staging.jiminny.com/playback/d5df34dc-bd66-4ff5-a7b3-8d3be30322a0\n\nSELECT * FROM activities WHERE uuid_to_bin('04fdcd0d-818f-4c53-92dc-6f18bc753ffd') = uuid;\n# 609126 softphone tr. 11241\n\nSELECT * FROM activities WHERE uuid_to_bin('6521bfcd-5a30-46e5-9f74-5440fd48befd') = uuid;\n# 608874 conference tr. 11226 crmId: 103422236596\n\nselect * from ai_prompts where transcription_id IN (11241, 11226);\nselect * from activity_summary_logs where activity_id = 608874;\n\nselect * from sidekick_settings;\nselect * from default_activity_types;\n\nselect * from crm_field_data where activity_id = 1223;\n\nselect * from crm_layouts where crm_configuration_id = 2;\nSELECT * FROM crm_layout_entities WHERE crm_layout_id IN (554);\nselect * from crm_fields where crm_configuration_id = 11 and object_type = 'event';\nSELECT * FROM crm_field_values WHERE crm_field_id IN (1455,1450);\n\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id = 971;\nSELECT * FROM crm_field_data WHERE crm_layout_entity_id IN (6494,6495,6496,6497,6498,6499);\n\nSELECT\n CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,\n u.email,\n sa.*,\n t.owner_id FROM social_accounts sa\nJOIN users u\n on u.id = sa.sociable_id\nJOIN teams t on t.id = u.team_id\nWHERE u.team_id = 2 and sa.provider = 'hubspot';\n\nselect * from social_accounts where id = 1499;\n\nselect * from opportunities where team_id = 2\nand crm_provider_id IN ('51317301383');\n\nselect * from contacts where id = 85;\n\nselect * from opportunities where team_id = 2 order by id desc;\nselect * from opportunities where team_id = 2 and crm_provider_id = '51317301383'; # 5112\nselect * from opportunities where team_id = 2 and crm_provider_id = '55976759904'; # 5112\nselect * from opportunity_contacts where opportunity_id = 5117;\nselect * from crm_field_data where object_id = 1365;\nSELECT * FROM crm_fields WHERE id IN (1405, 1407, 1972, 2128);\n\nselect * from features;\nselect * from team_features where team_id IN (1);\nselect * from team_features where feature_id IN (36);\n\nSHOW CREATE TABLE opportunity_contacts;\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '111751';\n\n# $slug = 'HUBSPOT_WEBHOOK_SYNC';\n# $team = Jiminny\\Models\\Team::find(2);\n# $feature = Feature::query()->where('slug', $slug)->first();\n# TeamFeature::query()->create(['feature_id' => $feature->getId(),'team_id' => $team->getId()]);\n\n# hubspot_webhook_metrics\n\nselect * from opportunities where team_id = 2 and crm_provider_id IN ('374720564','14527423589','49908861993','50435771779'); # 1365\nSELECT * FROM opportunity_contacts WHERE opportunity_id = '414';\nSELECT * FROM opportunity_contacts WHERE crm_provider_id = '131501';\nselect * from contacts where id in (414, 464);\n\nselect * from activities where crm_configuration_id = 2;\n\nselect settings from crm_configurations where id = 11;\n\nselect * from teams; # 1, 2\nselect * from users;\nselect * from crm_configurations where id = 39;\nselect * from team_features where team_id = 2;\nselect * from features;\n# SELECT * FROM opportunities WHERE crm_configuration_id = 2\n# order by id desc;\n# and crm_provider_id = '49908861993';\n\n\nselect * from activity_providers where id IN (443, 202, 203, 227);\n\nselect * from activity_imports where id = 795889;\n\nselect c.id, c.provider, c.settings, t.* from teams t join crm_configurations c on t.id = c.team_id\nwhere c.provider = 'hubspot';\n\nselect * from crm_configurations crm JOIN teams t on crm.team_id = t.id\nwhere provider = 'hubspot';\nSELECT * FROM teams WHERE id = 31;\nSELECT * FROM users WHERE id = 257;\nSELECT * FROM opportunities WHERE team_id = 2;\n\nselect * from opportunity_contacts where opportunity_id = 5124;\nselect * from contacts where id IN (3850,3853,3851,4073,4140,4155,4480,4530,4623,5986,513,687,1806,1523,3613)\n\nselect * from activities where crm_configuration_id = 13;\n\nSELECT * FROM activities WHERE uuid_to_bin('826619ce-ec8e-4e59-8467-a01f5f6ad71e') = uuid; # 418141\n\n\nselect id, team_id, crm_provider_id from crm_configurations where provider = 'hubspot' and crm_provider_id IS NOT NULL;\nSELECT * FROM accounts WHERE team_id = 2 and crm_provider_id = '1212213464' order by id desc;\nSELECT * FROM contacts WHERE team_id = 2 and account_id = 5189 order by id desc;\nSELECT * FROM contacts WHERE team_id = 2 order by id desc;\nselect * from opportunity_contacts where contact_id = 6223;\nSELECT * FROM opportunities WHERE team_id = 2 and account_id = 5189 order by id desc;\n\nselect * from crm_profiles where crm_configuration_id = 2;\n\nselect * from activities where account_id = 46;","role_description":"text entry area","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Sync Changes","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Hide This Notification","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":false,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Code changed:","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.042220745,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Hide","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXTextArea","text":"<?php\n\ndeclare(strict_types=1);\n\nnamespace Jiminny\\Component\\Utility\\Service;\n\nuse Illuminate\\Cache\\RateLimiter;\nuse Jiminny\\Contracts\\Http\\RateLimited;\nuse Jiminny\\Contracts\\Http\\RateLimitInterface;\n\nclass ProviderRateLimiter\n{\n protected RateLimiter $rateLimiter;\n\n public function __construct(RateLimiter $rateLimiter)\n {\n $this->rateLimiter = $rateLimiter;\n }\n\n public function canMakeRequest(RateLimited $provider): bool\n {\n /** @var RateLimitInterface $rateLimit */\n foreach ($provider->getRateLimits() as $rateLimit) {\n $key = $rateLimit->getKey();\n\n if ($this->rateLimiter->tooManyAttempts($key, $rateLimit->getQuota())) {\n return false;\n }\n }\n\n return true;\n }\n\n public function requestAvailableIn(RateLimited $provider): int\n {\n return $provider->getRateLimits()->isNotEmpty()\n ? $provider->getRateLimits()\n ->map(fn (RateLimitInterface $rateLimit): int => $this->rateLimiter->availableIn($rateLimit->getKey()))\n ->max()\n : 0\n ;\n }\n\n public function incrementRequestCount(RateLimited $provider): void\n {\n /** @var RateLimitInterface $rateLimit */\n foreach ($provider->getRateLimits() as $rateLimit) {\n $this->rateLimiter->hit($rateLimit->getKey(), $rateLimit->getWindow());\n }\n }\n}","depth":4,"bounds":{"left":0.11968085,"top":0.0,"width":0.26163563,"height":0.7725459},"on_screen":true,"value":"<?php\n\ndeclare(strict_types=1);\n\nnamespace Jiminny\\Component\\Utility\\Service;\n\nuse Illuminate\\Cache\\RateLimiter;\nuse Jiminny\\Contracts\\Http\\RateLimited;\nuse Jiminny\\Contracts\\Http\\RateLimitInterface;\n\nclass ProviderRateLimiter\n{\n protected RateLimiter $rateLimiter;\n\n public function __construct(RateLimiter $rateLimiter)\n {\n $this->rateLimiter = $rateLimiter;\n }\n\n public function canMakeRequest(RateLimited $provider): bool\n {\n /** @var RateLimitInterface $rateLimit */\n foreach ($provider->getRateLimits() as $rateLimit) {\n $key = $rateLimit->getKey();\n\n if ($this->rateLimiter->tooManyAttempts($key, $rateLimit->getQuota())) {\n return false;\n }\n }\n\n return true;\n }\n\n public function requestAvailableIn(RateLimited $provider): int\n {\n return $provider->getRateLimits()->isNotEmpty()\n ? $provider->getRateLimits()\n ->map(fn (RateLimitInterface $rateLimit): int => $this->rateLimiter->availableIn($rateLimit->getKey()))\n ->max()\n : 0\n ;\n }\n\n public function incrementRequestCount(RateLimited $provider): void\n {\n /** @var RateLimitInterface $rateLimit */\n foreach ($provider->getRateLimits() as $rateLimit) {\n $this->rateLimiter->hit($rateLimit->getKey(), $rateLimit->getWindow());\n }\n }\n}","role_description":"text entry area","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Project","depth":3,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Project","depth":3,"bounds":{"left":0.011968086,"top":0.047885075,"width":0.024268618,"height":0.024740623},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"New File or Directory…","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Expand Selected","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Collapse All","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Options","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Hide","depth":4,"bounds":{"left":0.27027926,"top":1.0,"width":0.008643617,"height":0.0},"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false}]...
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Project: faVsco.js, menu
master, menu
Start Listen Project: faVsco.js, menu
master, menu
Start Listening for PHP Debug Connections
AskJiminnyReportActivityServiceTest
Run 'AskJiminnyReportActivityServiceTest'
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# [PASSWORD_DOTS] HS [PASSWORD_DOTS]
select * from teams where id = 2; # 2
select * from features; # 2
select * from team_features where team_id = 2; # 2
select * from crm_configurations where id = 2; # 2
select * from users where team_id = 2; #
select * from playbooks where team_id = 2; # event 38
select * from playbook_categories where playbook_id = 38; #
SELECT * FROM activities WHERE crm_configuration_id = 2 and crm_provider_id is not null order by id desc;
[URL_WITH_CREDENTIALS] RateLimitInterface $rateLimit */
foreach ($provider->getRateLimits() as $rateLimit) {
$key = $rateLimit->getKey();
if ($this->rateLimiter->tooManyAttempts($key, $rateLimit->getQuota())) {
return false;
}
}
return true;
}
public function requestAvailableIn(RateLimited $provider): int
{
return $provider->getRateLimits()->isNotEmpty()
? $provider->getRateLimits()
->map(fn (RateLimitInterface $rateLimit): int => $this->rateLimiter->availableIn($rateLimit->getKey()))
->max()
: 0
;
}
public function incrementRequestCount(RateLimited $provider): void
{
/** @var RateLimitInterface $rateLimit */
foreach ($provider->getRateLimits() as $rateLimit) {
$this->rateLimiter->hit($rateLimit->getKey(), $rateLimit->getWindow());
}
}
}
Project
Project
New File or Directory…
Expand Selected
Collapse All
Options
Hide...
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152294906_m1.jpg...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]Support Daily • in 49 mA100% C 8DEV (docker)83Thu 7 May 14:11:34T₴1₴6DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 onttys006H82APP (-zsh)-zsh• 84|screenpipe*-zshPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#31...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]Support Daily • in 49 mA100% C 8DEV (docker)83Thu 7 May 14:11:34T₴1₴6DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 onttys006H82APP (-zsh)-zsh• 84|screenpipe*-zshPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#31...
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rostmancaltvlewWindowmelpProject© RingCentral/Clie rostmancaltvlewWindowmelpProject© RingCentral/Client.phpA EU [EU]v A jiminny@localhost& console uiminny clocainosADI [jiminny@localhost4 HS Jocal ljiminny@localhosA SF jiminny@localhost]A zoho_dev (jiminny@localhoV A PROD& console PrODIRematchActivityOnCrmObjectDetach.php# DeleteCrmEntity Trait.php© RateLimitException.phpMiddleware/RateLimited.pnpC) ProviderRateLimiter.pho Xclass ProviderRateLimiterpublic function canMakeRequest(RateLimited Sprovider): bool& console 1 PROD< DI PROD> AOAA QAI> 4 QAI PRODLSTAGING/** @var RateLimitInterface $rateLimit */foreach (Sprovider->getRateLimits() as SrateLimit) {$key = $rateLimit->getKeyO);if ($this-›rateLimiter->tooManyAttempts($key, $rateLimit->getQuota())) €return false;& console STAGING& console STAGINGduranus STAGINGI> Fxtansinnsservicesv D DatabaseV AEUconsole& |iminny@localhost4 HS_JocalA SF~ A PRODconsole 1s 381 msV A STAGINGA console 1s 250 ms…DockenPlatform Sprint 3 Q2© SyncToUsSevenShores|Hubsp.$i Service-Desk - Queur• Jy 20807 check varioA Sentry••Pull requests • jiminnU Useroilot I Ask Jimin@Jy-20773 fix user piloaJiminnuNew Tab40supoont Dally • In45 mQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.SEARCH › search deals= DocsParamsAuthorization • Headers 11 Body • ScriptsSettings• torm-dataerawO binary GraphQL JSON~rilters":"value": 1773041124362No environmentvf SaveCookies° Schema Beautify100% L2Thu 7 May 14:11:36UparadeVAIlVariables in requestG tokenCNi2rongMxIZQINQ…..› All variablesh OutoutW 1rowvii timinnv.socialaccounts X+=IX: Auto vRQGA®Did! sociable_idprovider_user_idprovider_user_tokenI provider_refresh_tokenMexnires149914827225182CNeR-JHgMxIZQLNQML8kQEwrAgwACAKUAhIJBB4BAQEDBxi(d5ab04e2-2109-4c0b-b513-8cba1dd543711778154080I refresh_token_expiresO providerhubspotI stateconnectedID auth_scope<nUll>retry_after<null>created_at2026-01-16 07-30-47I updated_at2026-05-07 11:11:20provider_user_token_encryptedeyJpdiI6Ik1PcXNtZ3R6TzFWMUVEMHBLb2U1Umc9PSIsInZtprovider_refresh_token_encryptedd5ab04e2-2109-4c0b-b513-8cba1dd54371I encryption_key (Hex)0x01020300784A7E08630802C9456B8B85CC5951D489859Cv COLLECtIONscontacts› CRM ObjectseRM owners› CRM Pipelines> Deals~ Engagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPoSt search tasksGET read call> PoST search callsGET list callsPosT meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio› HubspotJournal & webhoooks v4POSt Get tokenGET det subscriptionsPOST create subscriptionGET Journal earliestGET Journal latestGET https:ubspot-webhooks-lournal-nal.s.GET next offsetpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAI.GET DEAL WITH HISTORY PROPERTIES V3©Authi> Properties> RESSARCHSEARCHPost search contact by phonePOSt search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCS>FLOWS@ Connect Git = Concolda Te"hs_call_direction",BOdVCookiesJSONvPrevievSo, Pass the correct auth credentials v"message". "The OAuth token used to make this call expired 4 hour(s) ago.".#contov+". S"expire time": ["2026-05-07T06:41:35.576Z"401 Unauthorized • 145 ms • 1.14 KB • | e.g. Save Response •••==aID8...
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rostmancaltvlewWindowmelpProject© RingCentral/Clie rostmancaltvlewWindowmelpProject© RingCentral/Client.phpA EU [EU]v A jiminny@localhost& console uiminny clocainosADI [jiminny@localhost4 HS Jocal ljiminny@localhosA SF jiminny@localhost]A zoho_dev (jiminny@localhoV A PROD& console PrODIRematchActivityOnCrmObjectDetach.php# DeleteCrmEntity Trait.php© RateLimitException.phpMiddleware/RateLimited.pnpC) ProviderRateLimiter.pho Xclass ProviderRateLimiterpublic function canMakeRequest(RateLimited Sprovider): bool& console 1 PROD< DI PROD> AOAA QAI> 4 QAI PRODLSTAGING/** @var RateLimitInterface $rateLimit */foreach (Sprovider->getRateLimits() as SrateLimit) {$key = $rateLimit->getKeyO);if ($this-›rateLimiter->tooManyAttempts($key, $rateLimit->getQuota())) €return false;& console STAGING& console STAGINGduranus STAGINGI> Fxtansinnsservicesv D DatabaseV AEUconsole& |iminny@localhost4 HS_JocalA SF~ A PRODconsole 1s 381 msV A STAGINGA console 1s 250 ms…DockenPlatform Sprint 3 Q2© SyncToUsSevenShores|Hubsp.$i Service-Desk - Queur• Jy 20807 check varioA Sentry••Pull requests • jiminnU Useroilot I Ask Jimin@Jy-20773 fix user piloaJiminnuNew Tab40supoont Dally • In45 mQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.SEARCH › search deals= DocsParamsAuthorization • Headers 11 Body • ScriptsSettings• torm-dataerawO binary GraphQL JSON~rilters":"value": 1773041124362No environmentvf SaveCookies° Schema Beautify100% L2Thu 7 May 14:11:36UparadeVAIlVariables in requestG tokenCNi2rongMxIZQINQ…..› All variablesh OutoutW 1rowvii timinnv.socialaccounts X+=IX: Auto vRQGA®Did! sociable_idprovider_user_idprovider_user_tokenI provider_refresh_tokenMexnires149914827225182CNeR-JHgMxIZQLNQML8kQEwrAgwACAKUAhIJBB4BAQEDBxi(d5ab04e2-2109-4c0b-b513-8cba1dd543711778154080I refresh_token_expiresO providerhubspotI stateconnectedID auth_scope<nUll>retry_after<null>created_at2026-01-16 07-30-47I updated_at2026-05-07 11:11:20provider_user_token_encryptedeyJpdiI6Ik1PcXNtZ3R6TzFWMUVEMHBLb2U1Umc9PSIsInZtprovider_refresh_token_encryptedd5ab04e2-2109-4c0b-b513-8cba1dd54371I encryption_key (Hex)0x01020300784A7E08630802C9456B8B85CC5951D489859Cv COLLECtIONscontacts› CRM ObjectseRM owners› CRM Pipelines> Deals~ Engagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPoSt search tasksGET read call> PoST search callsGET list callsPosT meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio› HubspotJournal & webhoooks v4POSt Get tokenGET det subscriptionsPOST create subscriptionGET Journal earliestGET Journal latestGET https:ubspot-webhooks-lournal-nal.s.GET next offsetpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAI.GET DEAL WITH HISTORY PROPERTIES V3©Authi> Properties> RESSARCHSEARCHPost search contact by phonePOSt search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCS>FLOWS@ Connect Git = Concolda Te"hs_call_direction",BOdVCookiesJSONvPrevievSo, Pass the correct auth credentials v"message". "The OAuth token used to make this call expired 4 hour(s) ago.".#contov+". S"expire time": ["2026-05-07T06:41:35.576Z"401 Unauthorized • 145 ms • 1.14 KB • | e.g. Save Response •••==aID8...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]Support Daily • in 49 m100% [Thu 7 May 14:11:39DEV (docker)T81DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)83-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]Support Daily • in 49 m100% [Thu 7 May 14:11:39DEV (docker)T81DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)83-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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rostmancaltvlewWindowmelpProject© RingCentral/Clie rostmancaltvlewWindowmelpProject© RingCentral/Client.phpRematchActivityOnCrmObjectDetach.phpA EU [EU]v A jiminny@localhost& console uiminny clocainosADI [jiminny@localhost4 HS Jocal ljiminny@localhosA SF jiminny@localhost]A zoho_dev (jiminny@localhoV A PROD& console PrODI& console 1 PROD< DI PROD> AOAA QAI> 4 QAI PRODLSTAGING& console STAGING& console STAGINGduranus STAGINGI> Fxtansinns# DeleteCrmEntity Trait.php© RateLimitException.phpMiddleware/RateLimited.pnpC) ProviderRateLimiter.pho Xclass ProviderRateLimiterpublic function canMakeRequest(RateLimited Sprovider): boolservicesv D DatabaseV AEUconsole& |iminny@localhost4 HS_JocalA SF~ A PRODconsole 1s 381 msV A STAGINGA console 1s 250 ms…Docken/** @var RateLimitInterface $rateLimit */foreach (Sprovider->getRateLimits() as SrateLimit) {$key = $rateLimit->getKeyO);if ($this-›rateLimiter->tooManyAttempts($key, $rateLimit->getQuota())) €return false;h OutoutW 1rowvii timinnv.socialaccounts XIX: Auto vRQGA®Did! sociable_idprovider_user_idprovider_user_tokenI provider_refresh_tokenMexnires149914827225182CNeR-JHgMxIZQLNQML8kQEwrAgwACAKUAhIJBB4BAQEDBxi(d5ab04e2-2109-4c0b-b513-8cba1dd543711778154080I refresh_token_expiresO providerhubspotI stateconnectedID auth_scope<nUll>retry_after<null>created_at2026-01-16 07-30-47I updated_at2026-05-07 11:11:20provider_user_token_encryptedeyJpdiI6Ik1PcXNtZ3R6TzFWMUVEMHBLb2U1Umc9PSIsInZtprovider_refresh_token_encryptedd5ab04e2-2109-4c0b-b513-8cba1dd54371I encryption_key (Hex)0x01020300784A7E08630802C9456B8B85CC5951D489859CPlatform Sprint 3 Q2© SyncToUsSevenShores|Hubsp.$i Service-Desk - Queur• Jy 20807 check varioA Sentry••Pull requests • jiminnU Useroilot I Ask Jimin@Jy-20773 fix user piloaJiminnuNew Tab40supoont Dally • In45 mQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.SEARCH › search deals= DocsParamsAuthorization • Headers 11 Body • ScriptsSettings• torm-dataeraw• binary GraphQL JSON ~rilters":"value": 1773041124362No environmentvSaveCookiesSchema Beautify100% L2VAIlVariables in requestG token> All variables.Thu 7 May 14:11:39Uparadev COLLECtIONscontacts› CRM ObjectseRM owners› CRM PipelinesDeals~ Engagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPoSt search tasksGET read call> PoST search callsGET list callsPosT meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio› HubspotJournal & webhoooks v4POSt Get tokenGET det subscriptionsPOST create subscriptionGET Journal earliestGET Journal latestGET https:ubspot-webhooks-lournal-nal.s.GET next offsetpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAI.GET DEAL WITH HISTORY PROPERTIES V3©Authi> Properties> RESSARCHSEARCHPost search contact by phonePOSt search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCS>FLOWS@ Connect Git = Concolda Te"hs_call_direction",BOdVCookiesJSONvPrevievSo, Pass the correct auth credentials v"message". "The OAuth token used to make this call expired 4 hour(s) ago.".#contov+". S"expire time": ["2026-05-07T06:41:35.576Z"401 Unauthorized • 145 ms • 1.14 KB • CA e.g Save kesponse ••==aID8...
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rostmancaltvlewWindowmelpProject© RingCentral/Clie rostmancaltvlewWindowmelpProject© RingCentral/Client.phpRematchActivityOnCrmObjectDetach.phpA EU [EU]v A jiminny@localhost& console uiminny clocainosADI [jiminny@localhost4 HS Jocal ljiminny@localhosA SF jiminny@localhost]A zoho_dev (jiminny@localhoV A PROD& console PrODI& console 1 PROD< DI PROD> AOAA QAI> 4 QAI PRODLSTAGING& console STAGING& console STAGINGduranus STAGINGI> Fxtansinns# DeleteCrmEntity Trait.php© RateLimitException.phpMiddleware/RateLimited.pnpC) ProviderRateLimiter.pho Xclass ProviderRateLimiterpublic function canMakeRequest(RateLimited Sprovider): boolservicesv D DatabaseV AEUconsole& |iminny@localhost4 HS_JocalA SF~ A PRODconsole 1s 381 msV A STAGINGA console 1s 250 ms…Docken/** @var RateLimitInterface $rateLimit */foreach (Sprovider->getRateLimits() as SrateLimit) {$key = $rateLimit->getKeyO);if ($this-›rateLimiter->tooManyAttempts($key, $rateLimit->getQuota())) €return false;h OutoutW 1rowvii timinnv.socialaccounts XIX: Auto vRQGA®Did! sociable_idprovider_user_idprovider_user_tokenI provider_refresh_tokenMexnires149914827225182CNeR-JHgMxIZQLNQML8kQEwrAgwACAKUAhIJBB4BAQEDBxi(d5ab04e2-2109-4c0b-b513-8cba1dd543711778154080I refresh_token_expiresO providerhubspotI stateconnectedID auth_scope<nUll>retry_after<null>created_at2026-01-16 07-30-47I updated_at2026-05-07 11:11:20provider_user_token_encryptedeyJpdiI6Ik1PcXNtZ3R6TzFWMUVEMHBLb2U1Umc9PSIsInZtprovider_refresh_token_encryptedd5ab04e2-2109-4c0b-b513-8cba1dd54371I encryption_key (Hex)0x01020300784A7E08630802C9456B8B85CC5951D489859CPlatform Sprint 3 Q2© SyncToUsSevenShores|Hubsp.$i Service-Desk - Queur• Jy 20807 check varioA Sentry••Pull requests • jiminnU Useroilot I Ask Jimin@Jy-20773 fix user piloaJiminnuNew Tab40supoont Dally • In45 mQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.SEARCH › search deals= DocsParamsAuthorization • Headers 11 Body • ScriptsSettings• torm-dataeraw• binary GraphQL JSON ~rilters":"value": 1773041124362No environmentvSaveCookiesSchema Beautify100% L2VAIlVariables in requestG token> All variables.Thu 7 May 14:11:39Uparadev COLLECtIONscontacts› CRM ObjectseRM owners› CRM PipelinesDeals~ Engagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPoSt search tasksGET read call> PoST search callsGET list callsPosT meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio› HubspotJournal & webhoooks v4POSt Get tokenGET det subscriptionsPOST create subscriptionGET Journal earliestGET Journal latestGET https:ubspot-webhooks-lournal-nal.s.GET next offsetpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAI.GET DEAL WITH HISTORY PROPERTIES V3©Authi> Properties> RESSARCHSEARCHPost search contact by phonePOSt search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCS>FLOWS@ Connect Git = Concolda Te"hs_call_direction",BOdVCookiesJSONvPrevievSo, Pass the correct auth credentials v"message". "The OAuth token used to make this call expired 4 hour(s) ago.".#contov+". S"expire time": ["2026-05-07T06:41:35.576Z"401 Unauthorized • 145 ms • 1.14 KB • CA e.g Save kesponse ••==aID8...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]Support Daily • in 49 m100% [Thu 7 May 14:11:43DEV (docker)T81DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)83-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]Support Daily • in 49 m100% [Thu 7 May 14:11:43DEV (docker)T81DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)83-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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rostmancaltvlewWindowmelpProject© RingCentral/Clie rostmancaltvlewWindowmelpProject© RingCentral/Client.phpA EU [EU]v A jiminny@localhost& console uiminny clocainosADI [jiminny@localhost4 HS Jocal ljiminny@localhosA SF jiminny@localhost]A zoho_dev (jiminny@localhoV A PROD& console PrODIRematchActivityOnCrmObjectDetach.php# DeleteCrmEntity Trait.php© RateLimitException.phpMiddleware/RateLimited.pnpC) ProviderRateLimiter.pho Xclass ProviderRateLimitenpublic function canMakeRequest(RateLimited $provider): bool& console 1 PROD< DI PROD> AOAA QAI> 4 QAI PRODLSTAGING/** @var RateLimitInterface $rateLimit */foreach (Sprovider->getRateLimits() as SrateLimit) {$key = $rateLimit->getKeyO);if ($this-›rateLimiter->tooManyAttempts($key, $rateLimit->getQuota())) €return false;& console STAGING& console STAGINGduranus STAGINGI> Fxtansinnsservicesv D DatabaseV AEUconsole& |iminny@localhost4 HS_JocalA SF~ A PRODconsole 1s 381 msV A STAGINGA console 1s 250 ms…DockenPlatform Sprint 3 Q2© SyncToUsSevenShores|Hubsp.$ Service-Desk - Queue• Jy 20807 check varioA Sentry••Pull requests • jiminnyU Useroilot I Ask Jimin@Jy-20773 fix user piloaJiminnuNew TabhhlQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.GET next off. • POST search •HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-appsE DocsAuth TypeBearer TokenAuthorization • Headers 9 Body Scripts SettinasTokenThe authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationGET https://e•((token))supoont Dally • In45 m100% L2No environmentv~ SaveVariables in requestG tokenSendsw cookies> All variablesThu 7 May 14:11:48h OutoutW 1rowvii timinnv.socialaccounts XIX: Auto vFQGA®Did! sociable_idprovider_user_idprovider_user_tokenI provider_refresh_tokenMexnires149914827225182CNeR-JHgMxIZQLNQML8kQEwrAgwACAKUAhIJBB4BAQEDBxi(d5ab04e2-2109-4c0b-b513-8cba1dd543711778154080I refresh_token_expiresO providerhubspotI stateconnectedID auth_scope<nUll>retry_after<null>created_at2026-01-16 07-30-47I updated_at2026-05-07 11:11:20provider_user_token_encryptedeyJpdiI6Ik1PcXNtZ3R6TzFWMUVEMHBLb2U1Umc9PSIsInZtprovider_refresh_token_encryptedd5ab04e2-2109-4c0b-b513-8cba1dd54371I encryption_key (Hex)0x01020300784A7E08630802C9456B8B85CC5951D489859Cv COLLECtIONscontacts› CRM ObjectseRM owners› CRM Pipelines> Deals~ Engagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPoSt search tasksGET read call> PoST search callsGET list callsPosT meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio› HubspotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscriptionGET Journal earliestGET Journal latestGET https://hubspot-webhooks-iournal-na1.sGET next offsetpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAI.GET DEAL WITH HISTORY PROPERTIES V3©Authi> Properties> RESSARCHSEARCHPost search contact by phonePOST search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCS>FLOWSConnect Git E Console 2 TermirCNeR-JHgMxIZQINQ…..BodyCookiesJSONvPreviewSo, Pass the correct auth credentials v"message". "The OAuth token used to make this call expired 4 hour(s) ago.".#contov+". S"expire time": ["2026-05-07T06:41:35.576Z"401 Unauthorized • 149 ms • 1.14 KB • (a| ...=aIDOGlobals Vault Tools S 0 0 0...
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rostmancaltvlewWindowmelpProject© RingCentral/Clie rostmancaltvlewWindowmelpProject© RingCentral/Client.phpA EU [EU]v A jiminny@localhost& console uiminny clocainosADI [jiminny@localhost4 HS Jocal ljiminny@localhosA SF jiminny@localhost]A zoho_dev (jiminny@localhoV A PROD& console PrODIRematchActivityOnCrmObjectDetach.php# DeleteCrmEntity Trait.php© RateLimitException.phpMiddleware/RateLimited.pnpC) ProviderRateLimiter.pho Xclass ProviderRateLimitenpublic function canMakeRequest(RateLimited $provider): bool& console 1 PROD< DI PROD> AOAA QAI> 4 QAI PRODLSTAGING/** @var RateLimitInterface $rateLimit */foreach (Sprovider->getRateLimits() as SrateLimit) {$key = $rateLimit->getKeyO);if ($this-›rateLimiter->tooManyAttempts($key, $rateLimit->getQuota())) €return false;& console STAGING& console STAGINGduranus STAGINGI> Fxtansinnsservicesv D DatabaseV AEUconsole& |iminny@localhost4 HS_JocalA SF~ A PRODconsole 1s 381 msV A STAGINGA console 1s 250 ms…DockenPlatform Sprint 3 Q2© SyncToUsSevenShores|Hubsp.$ Service-Desk - Queue• Jy 20807 check varioA Sentry••Pull requests • jiminnyU Useroilot I Ask Jimin@Jy-20773 fix user piloaJiminnuNew TabhhlQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.GET next off. • POST search •HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-appsE DocsAuth TypeBearer TokenAuthorization • Headers 9 Body Scripts SettinasTokenThe authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationGET https://e•((token))supoont Dally • In45 m100% L2No environmentv~ SaveVariables in requestG tokenSendsw cookies> All variablesThu 7 May 14:11:48h OutoutW 1rowvii timinnv.socialaccounts XIX: Auto vFQGA®Did! sociable_idprovider_user_idprovider_user_tokenI provider_refresh_tokenMexnires149914827225182CNeR-JHgMxIZQLNQML8kQEwrAgwACAKUAhIJBB4BAQEDBxi(d5ab04e2-2109-4c0b-b513-8cba1dd543711778154080I refresh_token_expiresO providerhubspotI stateconnectedID auth_scope<nUll>retry_after<null>created_at2026-01-16 07-30-47I updated_at2026-05-07 11:11:20provider_user_token_encryptedeyJpdiI6Ik1PcXNtZ3R6TzFWMUVEMHBLb2U1Umc9PSIsInZtprovider_refresh_token_encryptedd5ab04e2-2109-4c0b-b513-8cba1dd54371I encryption_key (Hex)0x01020300784A7E08630802C9456B8B85CC5951D489859Cv COLLECtIONscontacts› CRM ObjectseRM owners› CRM Pipelines> Deals~ Engagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPoSt search tasksGET read call> PoST search callsGET list callsPosT meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio› HubspotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscriptionGET Journal earliestGET Journal latestGET https://hubspot-webhooks-iournal-na1.sGET next offsetpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAI.GET DEAL WITH HISTORY PROPERTIES V3©Authi> Properties> RESSARCHSEARCHPost search contact by phonePOST search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCS>FLOWSConnect Git E Console 2 TermirCNeR-JHgMxIZQINQ…..BodyCookiesJSONvPreviewSo, Pass the correct auth credentials v"message". "The OAuth token used to make this call expired 4 hour(s) ago.".#contov+". S"expire time": ["2026-05-07T06:41:35.576Z"401 Unauthorized • 149 ms • 1.14 KB • (a| ...=aIDOGlobals Vault Tools S 0 0 0...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]Support Daily • in 49 m100% [Thu 7 May 14:11:52DEV (docker)181DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)83-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]Support Daily • in 49 m100% [Thu 7 May 14:11:52DEV (docker)181DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)83-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
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Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
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...
|
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Pro","depth":15,"bounds":{"left":0.0043218085,"top":0.6943336,"width":0.037898935,"height":0.01915403},"on_screen":true,"role_description":"pop-up button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Get apps and extensions","depth":15,"bounds":{"left":0.08277926,"top":0.6943336,"width":0.007978723,"height":0.01915403},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"HubSpot rate limit implementation strategy, rename chat","depth":19,"bounds":{"left":0.043218084,"top":0.02793296,"width":0.09773936,"height":0.022346368},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"HubSpot rate limit implementation 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chat","depth":21,"bounds":{"left":0.48537233,"top":0.026336791,"width":0.010638298,"height":0.025538707},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Claude finished the response","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"You said: So just a solution for rate limit implementation.","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: So just a solution for rate limit implementation.","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating activity (engagement, meeting, call, note)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"searching for a company or contact","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating deal","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"patching IDs\nReview what the limit is at the moment in the HubSpot documentation and then think of the solution:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":25,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). Example of such a error Client error:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST https://api.hubapi.com/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"resulted in a","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"429 Too Many Requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"response: {\"status\":\"error\",\"message\":\"You have reached your secondly limit.\",\"errorType\":\"RATE_LIMIT\",\"correlationId\":\"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). 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Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. 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Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What \"two buckets per request\" actually costs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What \"two buckets per request\" actually costs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Each request makes","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"one round trip to Redis","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"lua","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[1] = burst bucket key (or search)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[2] = daily bucket key","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[1] = now (ms) ARGV[2] = window_ms","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[3] = bucket_max ARGV[4] = daily_max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[5] = request_id ARGV[6] = daily_ttl","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- Trim sliding 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Tell caller how long to sleep until oldest entry 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HubSpot rate limit implementation strategy, rename chat
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
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...
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claudeVIeWWindowHelpHubSpot rate limit implementat claudeVIeWWindowHelpHubSpot rate limit implementation strategyUl is per-portal), and see the search secondly limit anywhere except your own tracking. Bothare wny the keais-dacked dasnboard is non-optional once you nave more than a nandrul orlenlanlls.lets focus only on hubspot api I can call vai postman. If I want to know specificportal what limits does it have.Catalogued HubSoot APl endooints for auerving portal rate limits ›There are essentially two endpoints plus response header inspection. That's the entire APIsurface for rate-limit discovery per portal.1. Daily limit + current daily usageGET https://api.hubapi. com/account-info/v3/api-usage/daily/private-appsAuthorization: Bearer {portal_access_token}Returns:"results": [{"name": "private-apps-api-calls-daily","usageLimit": 1000000,"currentusage": 47213"resetsAt" : "2026-05-08T05:00: 00z",IfotchStatucll, lcllceees"This is the only API that tells you the daily ceiling and current spend for a portal. Theendpoint name savs private-apps. which is what it's officially documented for — itaggregates all legacy private apps in that portal. If your portal is connected via OAuth (publicapp), this endpoint may return empty results or a different shape; test it on one of yourcustomer portals to see.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 AdaptiveClaude is Al and can make mistakes. Please double-check responses.Q Searchn. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaborationGET Get EnaHTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appsE Docs ParamsAuth TypeBearer TokenGET Retrieve private app daily API usage • https://api.hubapi.com/account-info/2025-09/api-usage/daily/private-apps • HubSpot *GET Retrieve private apo daily APl usage • «baseUrl')/account-info/2026-03/api-usage/dailv/private-apos • HubSoot cGET Retrieve private app daily API usage • ({baseUrl))/account-info/2025-09/api-usage/daily/private-apps • HubSpot •The authorizationIgenerated when vGET Retrieve private app daily API usage • https://api.hubapi.com/account-info/2025-09/api-usage/daily/private-apps • HubSpot •more about Bearer Token authorization.Body Cookies 1 Headers 20 lest Results"JSONvPreview ?. Visualize v"results":"name". "private-aoos-aoi-calls-dail.v".lucadelimi+". 1000000"collectedAt": "2026-05-07T11:11:49.6772",*2026-05-08T04:00:002*hhlf Support Daily - in 48m100% C4)Thu 7 May 14:12:02No environmentv# SaveCookies* AIVariables in requestG token>All variablesCNeR-JHaMxlZoiNd.200 OK • 194 ms • 1.2 KB •a1 .•= =aID8Giobals Vault Tooks •- =...
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2758360491782491532
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NULL
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click
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ocr
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claudeVIeWWindowHelpHubSpot rate limit implementat claudeVIeWWindowHelpHubSpot rate limit implementation strategyUl is per-portal), and see the search secondly limit anywhere except your own tracking. Bothare wny the keais-dacked dasnboard is non-optional once you nave more than a nandrul orlenlanlls.lets focus only on hubspot api I can call vai postman. If I want to know specificportal what limits does it have.Catalogued HubSoot APl endooints for auerving portal rate limits ›There are essentially two endpoints plus response header inspection. That's the entire APIsurface for rate-limit discovery per portal.1. Daily limit + current daily usageGET https://api.hubapi. com/account-info/v3/api-usage/daily/private-appsAuthorization: Bearer {portal_access_token}Returns:"results": [{"name": "private-apps-api-calls-daily","usageLimit": 1000000,"currentusage": 47213"resetsAt" : "2026-05-08T05:00: 00z",IfotchStatucll, lcllceees"This is the only API that tells you the daily ceiling and current spend for a portal. Theendpoint name savs private-apps. which is what it's officially documented for — itaggregates all legacy private apps in that portal. If your portal is connected via OAuth (publicapp), this endpoint may return empty results or a different shape; test it on one of yourcustomer portals to see.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 AdaptiveClaude is Al and can make mistakes. Please double-check responses.Q Searchn. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaborationGET Get EnaHTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appsE Docs ParamsAuth TypeBearer TokenGET Retrieve private app daily API usage • https://api.hubapi.com/account-info/2025-09/api-usage/daily/private-apps • HubSpot *GET Retrieve private apo daily APl usage • «baseUrl')/account-info/2026-03/api-usage/dailv/private-apos • HubSoot cGET Retrieve private app daily API usage • ({baseUrl))/account-info/2025-09/api-usage/daily/private-apps • HubSpot •The authorizationIgenerated when vGET Retrieve private app daily API usage • https://api.hubapi.com/account-info/2025-09/api-usage/daily/private-apps • HubSpot •more about Bearer Token authorization.Body Cookies 1 Headers 20 lest Results"JSONvPreview ?. Visualize v"results":"name". "private-aoos-aoi-calls-dail.v".lucadelimi+". 1000000"collectedAt": "2026-05-07T11:11:49.6772",*2026-05-08T04:00:002*hhlf Support Daily - in 48m100% C4)Thu 7 May 14:12:02No environmentv# SaveCookies* AIVariables in requestG token>All variablesCNeR-JHaMxlZoiNd.200 OK • 194 ms • 1.2 KB •a1 .•= =aID8Giobals Vault Tooks •- =...
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2026-05-07T11:12:13.687916+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152333687_m2.jpg...
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rostmanEditVIewWindowmelpHubSpot rate limit implem rostmanEditVIewWindowmelpHubSpot rate limit implementation strategy vThere are essentially two endpoints plus response header inspection. That's the entire APIsurface for rate-limit discovery per portal.1. Daily limit + current daily usageGET https://api.hubapi.com/account-info/v3/api-usage/daily/private-appsAuthorization: Bearer {portal_access_token}Returns:"results": [{"name": "orivate-aoos-ao1-cauis-danv"."usageLimit": 1000000,"currentUsage": 47213"resetsAt": "2026-05-08T05:00 : 00z","fetchStatus": "SUCCESS"This is the only API that tells you the daily ceiling and current spend for a portal. Theendpoint name says private-apps, which is what it's officially documented for — itaggregates all legacy private apps in that portal. If your portal is connected via OAuth (publicapp), this endpoint may return empty results or a different shape; test it on one of yourcustomer portals to see.2. Portal context (timezone, tier-related info)GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {portal access token?Returns:"portalid": 123456."accountType": "STANDARD","timeZone": "Europe/Sofia",Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno.iWrite a message…Opus 4.7 Adaptive~Claude is Al and can make mistakes. Please double-check responses.hhlQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.GET read ciGET Get EnaGET https://e•HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-appsE DocsAuth TypeAuthorization • Headers 9 Body ScriptsSettingsToken((token))The authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationsupoont Dally • In 40 mNo environmentvg SaveCookies100% L2* AIVariables in request> All variablesThu 7 May 14:12:13v COLLECtIONscontacts› CRM ObjectseRM owners› CRM Pipelines> Deals~ Engagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPoSt search tasksGET read call> PoST search callsGET list callsPosT meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio› HubspotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscriptionGET Journal earliestGET Journal latestGeT https://hubspot-webhooks-iournal-na1.sGET next offsetpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAL.GET DEAL WITH HISTORY PROPERTIES V3©Authi> Properties> RESSARCHSEARCHPOST search contact bv ohonePOST search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCS> FLOWS$ Connect Git @ Console 2 TerminCNeR-JHaMxlZoiNO.BodyJSONvPreview? Visualize"results":"name": "private-apos-api-calls-dailly""usagelimit": 1000000,"collectedAt": "2026-05-07T11:11:49.6772""2026-05-08T04:00:002"200 OK • 194 ms • 1.2 KB •C| .•=Q1O0Globals Vault Tools S000...
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rostmanEditVIewWindowmelpHubSpot rate limit implem rostmanEditVIewWindowmelpHubSpot rate limit implementation strategy vThere are essentially two endpoints plus response header inspection. That's the entire APIsurface for rate-limit discovery per portal.1. Daily limit + current daily usageGET https://api.hubapi.com/account-info/v3/api-usage/daily/private-appsAuthorization: Bearer {portal_access_token}Returns:"results": [{"name": "orivate-aoos-ao1-cauis-danv"."usageLimit": 1000000,"currentUsage": 47213"resetsAt": "2026-05-08T05:00 : 00z","fetchStatus": "SUCCESS"This is the only API that tells you the daily ceiling and current spend for a portal. Theendpoint name says private-apps, which is what it's officially documented for — itaggregates all legacy private apps in that portal. If your portal is connected via OAuth (publicapp), this endpoint may return empty results or a different shape; test it on one of yourcustomer portals to see.2. Portal context (timezone, tier-related info)GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {portal access token?Returns:"portalid": 123456."accountType": "STANDARD","timeZone": "Europe/Sofia",Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno.iWrite a message…Opus 4.7 Adaptive~Claude is Al and can make mistakes. Please double-check responses.hhlQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.GET read ciGET Get EnaGET https://e•HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-appsE DocsAuth TypeAuthorization • Headers 9 Body ScriptsSettingsToken((token))The authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationsupoont Dally • In 40 mNo environmentvg SaveCookies100% L2* AIVariables in request> All variablesThu 7 May 14:12:13v COLLECtIONscontacts› CRM ObjectseRM owners› CRM Pipelines> Deals~ Engagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPoSt search tasksGET read call> PoST search callsGET list callsPosT meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio› HubspotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscriptionGET Journal earliestGET Journal latestGeT https://hubspot-webhooks-iournal-na1.sGET next offsetpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAL.GET DEAL WITH HISTORY PROPERTIES V3©Authi> Properties> RESSARCHSEARCHPOST search contact bv ohonePOST search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCS> FLOWS$ Connect Git @ Console 2 TerminCNeR-JHaMxlZoiNO.BodyJSONvPreview? Visualize"results":"name": "private-apos-api-calls-dailly""usagelimit": 1000000,"collectedAt": "2026-05-07T11:11:49.6772""2026-05-08T04:00:002"200 OK • 194 ms • 1.2 KB •C| .•=Q1O0Globals Vault Tools S000...
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HubSpot rate limit implementation strategy, rename chat
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
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...
|
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The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating activity (engagement, meeting, call, note)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"searching for a company or contact","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating deal","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"patching IDs\nReview what the limit is at the moment in the HubSpot documentation and then think of the solution:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":25,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). Example of such a error Client error:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST https://api.hubapi.com/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"resulted in a","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"429 Too Many Requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"response: {\"status\":\"error\",\"message\":\"You have reached your secondly limit.\",\"errorType\":\"RATE_LIMIT\",\"correlationId\":\"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"}]...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
Edit
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
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...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]Support Daily • in 48 m100% [Thu 7 May 14:12:18DEV (docker)T81DOCKERLast login: Thu MayDEV (docker)H82APP (-zsh)83-zsh• 84screenpipe*•$5-zsh₴67 09:44:56 on ttys006Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]Support Daily • in 48 m100% [Thu 7 May 14:12:18DEV (docker)T81DOCKERLast login: Thu MayDEV (docker)H82APP (-zsh)83-zsh• 84screenpipe*•$5-zsh₴67 09:44:56 on ttys006Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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ClaudeVIeWWindowHelpHubSpot rate limit implementat ClaudeVIeWWindowHelpHubSpot rate limit implementation strategyThere are essentially two endpoints plus response header inspection. That's the entire APIsurface for rate-limit discovery per portal.1. Daily limit + current daily usageGET https://api.hubapi.com/account-info/v3/api-usage/daily/private-appsAuthorization: Bearer {portal_access_token}Returns:"results": [{"name": "orivate-aoos-ao1-cauis-danv"."usageLimit": 1000000,"currentUsage": 47213"resetsAt": "2026-05-08T05:00 : 00z","fetchStatus": "SUCCESS"This is the only API that tells you the daily ceiling and current spend for a portal. Theendpoint name says private-apps, which is what it's officially documented for — itaggregates all legacy private apps in that portal. If your portal is connected via OAuth (publicapp), this endpoint may return empty results or a different shape; test it on one of yourcustomer portals to see.2. Portal context (time7related info)GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {portal access token?Returns:"portallid": 123456."accountType": "STANDARD","timeZone": "Europe/Sofia",Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno.Write a message…Opus 4.7 AdaptiveClaude is Al and can make mistakes. Please double-check responses.40hhlsupoont Dally • In 40 mThu 7 May 14:12:18Q Searchn. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.HTTP https:pi.hubapi.com/account-info/v3/api-usage/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps= Docs Params Authorization • Headers 9 Body Scripts SettingsAuth TypeBearer TokenTokenine autnorization neader will oe automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationGET Get Eng((token))GET https://t•No environmentvg Save100% L2* AIVariables in requestCookies> All variablesCNeR-JHaMxlZoiNd.Body Cookies 1 Headers 20 lest ResultsJSONvPreview ?. Visualize v"results": ["name": "private-aops-aoi-calls-daiilv".."ucadel imi+". 1000000"collectedAt": "2026-05-07T11:11:49.6772".*2026-05-08T04:00:002*200 OK • 194 ms • 1.2 KB •C| .•=Q108Globals Vault Tools?000...
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ClaudeVIeWWindowHelpHubSpot rate limit implementat ClaudeVIeWWindowHelpHubSpot rate limit implementation strategyThere are essentially two endpoints plus response header inspection. That's the entire APIsurface for rate-limit discovery per portal.1. Daily limit + current daily usageGET https://api.hubapi.com/account-info/v3/api-usage/daily/private-appsAuthorization: Bearer {portal_access_token}Returns:"results": [{"name": "orivate-aoos-ao1-cauis-danv"."usageLimit": 1000000,"currentUsage": 47213"resetsAt": "2026-05-08T05:00 : 00z","fetchStatus": "SUCCESS"This is the only API that tells you the daily ceiling and current spend for a portal. Theendpoint name says private-apps, which is what it's officially documented for — itaggregates all legacy private apps in that portal. If your portal is connected via OAuth (publicapp), this endpoint may return empty results or a different shape; test it on one of yourcustomer portals to see.2. Portal context (time7related info)GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {portal access token?Returns:"portallid": 123456."accountType": "STANDARD","timeZone": "Europe/Sofia",Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno.Write a message…Opus 4.7 AdaptiveClaude is Al and can make mistakes. Please double-check responses.40hhlsupoont Dally • In 40 mThu 7 May 14:12:18Q Searchn. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.HTTP https:pi.hubapi.com/account-info/v3/api-usage/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps= Docs Params Authorization • Headers 9 Body Scripts SettingsAuth TypeBearer TokenTokenine autnorization neader will oe automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationGET Get Eng((token))GET https://t•No environmentvg Save100% L2* AIVariables in requestCookies> All variablesCNeR-JHaMxlZoiNd.Body Cookies 1 Headers 20 lest ResultsJSONvPreview ?. Visualize v"results": ["name": "private-aops-aoi-calls-daiilv".."ucadel imi+". 1000000"collectedAt": "2026-05-07T11:11:49.6772".*2026-05-08T04:00:002*200 OK • 194 ms • 1.2 KB •C| .•=Q108Globals Vault Tools?000...
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PostmanEditVIewWindowmelpHubSpot rate limit implem PostmanEditVIewWindowmelpHubSpot rate limit implementation strategy vThere are essentially two endpoints plus response header inspection. That's the entire APIsurface for rate-limit discovery per portal.1. Daily limit + current daily usageGET https://api.hubapi.com/account-info/v3/api-usage/daily/private-appsAuthorization: Bearer {portal_access_token}Returns:"results": [{"name": "orivate-aoos-ao1-cauis-danv"."usageLimit": 1000000,"currentUsage": 47213"resetsAt": "2026-05-08T05:00: 00z","fetchStatus": "SUCCESS"This is the only API that tells you the daily ceiling and current spend for a portal. Theendpoint name says private-apps, which is what it's officially documented for — itaggregates all legacy private apps in that portal. If your portal is connected via OAuth (publicapp), this endpoint may return empty results or a different shape; test it on one of yourcustomer portals to see.2. Portal context (time7related info)GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {portal access token?Returns:"portalid": 123456."accountType": "STANDARD","timeZone": "Europe/Sofia",Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno.iWrite a message…Opus 4.7 Adaptive~Claude is Al and can make mistakes. Please double-check responses.Inu / May 14:12-20UparadeQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.GET read ciHTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-appsE DocsAuth TypeAuthorization • Headers 9 Body ScriptsSettingsTokenThe authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorization((token))40GET https://t•supoont Dally • In 40 m100% L2No environmentv~ SaveVariables in requestG tokenCookies> All variablesv COLLECtIONscontacts› CRM ObjectseRM owners› CRM PipelinesDeals~ Engagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePoSt search tasksGET read call> PoST search callsGET list callsPosT meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio› HubspotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscriptionGET Journal earliestGET Journal latestGeT https://hubspot-webhooks-iournal-na1.sGET next offsetpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAL.GET DEAL WITH HISTORY PROPERTIES V3©Authi> Properties> RESSARCHSEARCHPost search contact by phonePOST search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCS> FLOWSConnect Git E Console 2 TerminCNeR-JHgMxIZQINQ…..BodyJSONvPreview? Visualize"results":"name": "private-aops-aoi-calls-dailv"."ucadel imi+". 1000000"collectedAt": "2026-05-07T11:11:49.6772"."2026-05-08T04:00:002"200 OK • 194 ms • 1.2 KB •C| .•=Q108Globals Vault Tools S000...
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PostmanEditVIewWindowmelpHubSpot rate limit implem PostmanEditVIewWindowmelpHubSpot rate limit implementation strategy vThere are essentially two endpoints plus response header inspection. That's the entire APIsurface for rate-limit discovery per portal.1. Daily limit + current daily usageGET https://api.hubapi.com/account-info/v3/api-usage/daily/private-appsAuthorization: Bearer {portal_access_token}Returns:"results": [{"name": "orivate-aoos-ao1-cauis-danv"."usageLimit": 1000000,"currentUsage": 47213"resetsAt": "2026-05-08T05:00: 00z","fetchStatus": "SUCCESS"This is the only API that tells you the daily ceiling and current spend for a portal. Theendpoint name says private-apps, which is what it's officially documented for — itaggregates all legacy private apps in that portal. If your portal is connected via OAuth (publicapp), this endpoint may return empty results or a different shape; test it on one of yourcustomer portals to see.2. Portal context (time7related info)GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {portal access token?Returns:"portalid": 123456."accountType": "STANDARD","timeZone": "Europe/Sofia",Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno.iWrite a message…Opus 4.7 Adaptive~Claude is Al and can make mistakes. Please double-check responses.Inu / May 14:12-20UparadeQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.GET read ciHTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-appsE DocsAuth TypeAuthorization • Headers 9 Body ScriptsSettingsTokenThe authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorization((token))40GET https://t•supoont Dally • In 40 m100% L2No environmentv~ SaveVariables in requestG tokenCookies> All variablesv COLLECtIONscontacts› CRM ObjectseRM owners› CRM PipelinesDeals~ Engagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePoSt search tasksGET read call> PoST search callsGET list callsPosT meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio› HubspotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscriptionGET Journal earliestGET Journal latestGeT https://hubspot-webhooks-iournal-na1.sGET next offsetpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAL.GET DEAL WITH HISTORY PROPERTIES V3©Authi> Properties> RESSARCHSEARCHPost search contact by phonePOST search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCS> FLOWSConnect Git E Console 2 TerminCNeR-JHgMxIZQINQ…..BodyJSONvPreview? Visualize"results":"name": "private-aops-aoi-calls-dailv"."ucadel imi+". 1000000"collectedAt": "2026-05-07T11:11:49.6772"."2026-05-08T04:00:002"200 OK • 194 ms • 1.2 KB •C| .•=Q108Globals Vault Tools S000...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]Support Daily • in 48 m100% [Thu 7 May 14:12:22DEV (docker)83DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]Support Daily • in 48 m100% [Thu 7 May 14:12:22DEV (docker)83DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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PostmancaltVIewWindowmelpHubSpot rate limit imple PostmancaltVIewWindowmelpHubSpot rate limit implementation strategy vGEl nttps://ap1.hubap1.com/account-into/v3/detailsAuthorization: Bearer (portal access token}Kerums."portalid": 123456,"accountType": "STANDARD","timeZone": "Europe/Sofia"."utcoftset": "+03:00","uiDomain": "app.hubspot.com","dataHostingLocation": "eu1'T'his doesn t show limits directly, but you need timezone tointerpret resetsAt fromendpoint #1 and to know when this portal's dailv quota actuallv rolls over. There's no tierfeld that tells you "this is Pro 190/10s vs Starter 100 /10s" — you have to infer that from theburst headers below.3. Burst limit — onlv via response headersThere is no dedicated endpoint that returns "vour burst limit is 190 10s." You have to make anvcheap call and read the headers. The simplest ping:GET https:api.hubapi.com/account-info/v3/detailsAuthorization: Bearer portal access tokenIn Postman, look at the Headers tab of the response for:HeaderMeaningX-HubSpot-RateLimit-MaxBurst ceiling (e.g., 190 )X-HubSpot-RateLimit-RemainingCalls left in current windowX-HubSnot-RateLimit-interval -Mil isecondsWindow size, always 10000X-HubSpot-RateLimit-SecondlyDeprecated, ignoreKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Thu 7 May 14:12:34Q SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.GET next off. •HTTP https:pi.hubapi.com/account-info/v3/detailsnttps://api.nubapi.com/account-info/vs/details= DocsParamsAuthorization • Headers 9 Body ScriptsSettinasAuth TypeTokenThe authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorization.GET Get EngGET httos://:{tokenl}supoont Dally • In 40 mNo environmentv~ Save100% L24* AIVariables in requestG token> All variablesCOLLECTIONScontacts› CRM ObjectseRM owners> CRM PipelinesDealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscrintionGET Journal earliestGET lournal latestGET https://hubspot-webhooks-iournal-na1.sGET neyt offcotpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAL.GET DEAL WITH HISTORY pRODEptIcC vA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOSt search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 TermirCNeR-JHaMxlZoiNd.CookiesBodyJSONvPreview? Visualize"portalld": 4392066"timeZone". "US/Eastern" ."additionalCurrencies": (200 OK • 591 ms • 1.23 KB •(| .•CQ108Opus 4.7 Adaptive vGlobals Vault Tools?000...
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PostmancaltVIewWindowmelpHubSpot rate limit imple PostmancaltVIewWindowmelpHubSpot rate limit implementation strategy vGEl nttps://ap1.hubap1.com/account-into/v3/detailsAuthorization: Bearer (portal access token}Kerums."portalid": 123456,"accountType": "STANDARD","timeZone": "Europe/Sofia"."utcoftset": "+03:00","uiDomain": "app.hubspot.com","dataHostingLocation": "eu1'T'his doesn t show limits directly, but you need timezone tointerpret resetsAt fromendpoint #1 and to know when this portal's dailv quota actuallv rolls over. There's no tierfeld that tells you "this is Pro 190/10s vs Starter 100 /10s" — you have to infer that from theburst headers below.3. Burst limit — onlv via response headersThere is no dedicated endpoint that returns "vour burst limit is 190 10s." You have to make anvcheap call and read the headers. The simplest ping:GET https:api.hubapi.com/account-info/v3/detailsAuthorization: Bearer portal access tokenIn Postman, look at the Headers tab of the response for:HeaderMeaningX-HubSpot-RateLimit-MaxBurst ceiling (e.g., 190 )X-HubSpot-RateLimit-RemainingCalls left in current windowX-HubSnot-RateLimit-interval -Mil isecondsWindow size, always 10000X-HubSpot-RateLimit-SecondlyDeprecated, ignoreKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Thu 7 May 14:12:34Q SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.GET next off. •HTTP https:pi.hubapi.com/account-info/v3/detailsnttps://api.nubapi.com/account-info/vs/details= DocsParamsAuthorization • Headers 9 Body ScriptsSettinasAuth TypeTokenThe authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorization.GET Get EngGET httos://:{tokenl}supoont Dally • In 40 mNo environmentv~ Save100% L24* AIVariables in requestG token> All variablesCOLLECTIONScontacts› CRM ObjectseRM owners> CRM PipelinesDealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscrintionGET Journal earliestGET lournal latestGET https://hubspot-webhooks-iournal-na1.sGET neyt offcotpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAL.GET DEAL WITH HISTORY pRODEptIcC vA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOSt search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 TermirCNeR-JHaMxlZoiNd.CookiesBodyJSONvPreview? Visualize"portalld": 4392066"timeZone". "US/Eastern" ."additionalCurrencies": (200 OK • 591 ms • 1.23 KB •(| .•CQ108Opus 4.7 Adaptive vGlobals Vault Tools?000...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyX-HubSpot-RateLimit-Secondly-RemainingDeprecated, ignoreSO Max=190, Interval=10000 means this portal is on a Pro/ Enterprise privately-distributedaoo aliyuius. I vousee max=110 .rs a oubnc VAutn nstal. max=250 means they nave uneAPI Limit Increase add-on.What vou cannot get via API• Search 5/sec limit - not exposed anywhere. Not in headers (search responses stripthem), not in any endpoint. It's a documented constant: vou assume it.• Daily usage broken out by app within a portal — the daily endpoint aggregates all privateapps. You can't tell from the API which app spent the budget.• Per-app burst limit programmatically — only inferred from Max in headers from a callthat app madePostman recide to fullv profile a vortallIhree calls in order.1. GET /account-info/v3/details →grab portalid. timeZone also notethe x-HunSnot-Rate imit-x resnonse neaders (this is vour burst nronle)2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spend.reset time3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, but thisisn't necessary just for inspection.That gives vou evervthing HubSpot will tell vou about a specific portal's limits. Save the tworequests as a Postman collection with (faccess token}} and ((portal idi} ascollectionvariables and vou can profile anv portal in two clicks.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..running and testing as it goesWrite a message…Opus 4.7 Adaptive v100% L2Thu 7 May 14:12:40Xx Hubspot vQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.HTTP https:pi.hubapi.com/account-info/v3/detailsnttps://api.nubapi.com/account-info/vs/details= DocsParamsAuthorization • Headers 9 Body Scripts SettinasAuth TypeTokenThe authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationGET Get EngGET httos://:{tokenl}supoont Dally • In 40 mNo environmentv~ Savev COLLECtIONscontacts› CRM ObjectseRM owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET det subscriptionsPOST create subscrintionGET Journal earliestGET lournal latestGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offcotpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAI.GET DEAL WITH HISTORY pRODEpTICC VA©Authi> Properties> RESSARCHwCCADAUPOSt search contact bv ohonePOST search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWS$ Connect Git @ Console TermirCookiesVariables in requestG token> All variablesCNeR-JHaMxlZoiNd.BOdVJSONvPreview? Visualize"portalld": 4392066"additionalCurrencies": (200 OK • 591 ms • 1.23 KB •(| .•CQ108Globals Vault Tools?000...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyX-HubSpot-RateLimit-Secondly-RemainingDeprecated, ignoreSO Max=190, Interval=10000 means this portal is on a Pro/ Enterprise privately-distributedaoo aliyuius. I vousee max=110 .rs a oubnc VAutn nstal. max=250 means they nave uneAPI Limit Increase add-on.What vou cannot get via API• Search 5/sec limit - not exposed anywhere. Not in headers (search responses stripthem), not in any endpoint. It's a documented constant: vou assume it.• Daily usage broken out by app within a portal — the daily endpoint aggregates all privateapps. You can't tell from the API which app spent the budget.• Per-app burst limit programmatically — only inferred from Max in headers from a callthat app madePostman recide to fullv profile a vortallIhree calls in order.1. GET /account-info/v3/details →grab portalid. timeZone also notethe x-HunSnot-Rate imit-x resnonse neaders (this is vour burst nronle)2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spend.reset time3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, but thisisn't necessary just for inspection.That gives vou evervthing HubSpot will tell vou about a specific portal's limits. Save the tworequests as a Postman collection with (faccess token}} and ((portal idi} ascollectionvariables and vou can profile anv portal in two clicks.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..running and testing as it goesWrite a message…Opus 4.7 Adaptive v100% L2Thu 7 May 14:12:40Xx Hubspot vQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.HTTP https:pi.hubapi.com/account-info/v3/detailsnttps://api.nubapi.com/account-info/vs/details= DocsParamsAuthorization • Headers 9 Body Scripts SettinasAuth TypeTokenThe authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationGET Get EngGET httos://:{tokenl}supoont Dally • In 40 mNo environmentv~ Savev COLLECtIONscontacts› CRM ObjectseRM owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET det subscriptionsPOST create subscrintionGET Journal earliestGET lournal latestGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offcotpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAI.GET DEAL WITH HISTORY pRODEpTICC VA©Authi> Properties> RESSARCHwCCADAUPOSt search contact bv ohonePOST search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWS$ Connect Git @ Console TermirCookiesVariables in requestG token> All variablesCNeR-JHaMxlZoiNd.BOdVJSONvPreview? Visualize"portalld": 4392066"additionalCurrencies": (200 OK • 591 ms • 1.23 KB •(| .•CQ108Globals Vault Tools?000...
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152393713_m1.jpg...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS...
|
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The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating activity (engagement, meeting, call, note)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"searching for a company or contact","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating deal","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"patching IDs\nReview what the limit is at the moment in the HubSpot documentation and then think of the solution:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":25,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). Example of such a error Client error:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST https://api.hubapi.com/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"resulted in a","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"429 Too Many Requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"response: {\"status\":\"error\",\"message\":\"You have reached your secondly limit.\",\"errorType\":\"RATE_LIMIT\",\"correlationId\":\"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What \"two buckets per request\" actually costs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What \"two buckets per request\" actually costs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Each request makes","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"one round trip to Redis","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"lua","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[1] = burst bucket key (or search)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[2] = daily bucket key","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[1] = now (ms) ARGV[2] = window_ms","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[3] = bucket_max ARGV[4] = daily_max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[5] = request_id ARGV[6] = daily_ttl","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- Trim sliding window","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZREMRANGEBYSCORE'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"}]...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
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Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS...
|
NULL
|
NULL
|
NULL
|
NULL
|
|
2363
|
102
|
41
|
2026-05-07T11:13:13.713814+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152393713_m2.jpg...
|
Claude
|
Claude
|
True
|
NULL
|
monitor_2
|
NULL
|
NULL
|
NULL
|
NULL
|
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
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┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
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php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls....
|
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strategy","depth":21,"bounds":{"left":0.04454787,"top":0.031923383,"width":0.09507979,"height":0.014365523},"on_screen":true,"lines":[{"char_start":0,"char_count":1,"bounds":{"left":0.04454787,"top":0.031923383,"width":0.003656915,"height":0.014365523}},{"char_start":1,"char_count":41,"bounds":{"left":0.048204787,"top":0.031923383,"width":0.09142287,"height":0.014365523}}],"role_description":"text"},{"role":"AXPopUpButton","text":"More options for HubSpot rate limit implementation strategy","depth":19,"bounds":{"left":0.14128989,"top":0.02793296,"width":0.0066489363,"height":0.022346368},"on_screen":true,"role_description":"pop-up button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Share chat","depth":21,"bounds":{"left":0.48537233,"top":0.026336791,"width":0.010638298,"height":0.025538707},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Claude finished the response","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"You said: So just a solution for rate limit implementation.","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: So just a solution for rate limit implementation.","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating activity (engagement, meeting, call, note)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"searching for a company or contact","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating deal","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"patching IDs\nReview what the limit is at the moment in the HubSpot documentation and then think of the solution:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":25,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). Example of such a error Client error:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST https://api.hubapi.com/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"resulted in a","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"429 Too Many Requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"response: {\"status\":\"error\",\"message\":\"You have reached your secondly limit.\",\"errorType\":\"RATE_LIMIT\",\"correlationId\":\"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"}]...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls....
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ClaudeFileEditVIewWindowmelt= HubSpot rate limit i ClaudeFileEditVIewWindowmelt= HubSpot rate limit implementation strategy vX-HubSpot-RateLimit-Secondly-RemainingDeprecated, ignoreSO Max=190, Interval=10000 means this portal is on a Pro/ Enterprise privately-distributedaoo aliyuius. I vousee max=110 .s a pubnc VAutn instal. max=250 means they nave theAPI Limit Increase add-on.What vou cannot get via API• Search 5/sec limit - not exposed anywhere. Not in headers (search responses stripthem), not in any endpoint. It's a documented constant: vou assume it.• Daily usage broken out by app within a portal — the daily endpoint aggregates all privateapps. You can't tell from the API which app spent the budget.• Per-app burst limit programmatically — only inferred from Max in headers from a callthat app madePostman recide to fullv profile a vortallIhree calis in order.1. GET /account-info/v3/details →grab portalid. timeZone also notethe x-HunSnot-Rate imit-x resnonse neaders (this is vour burst nronle)2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spend.reset time3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, but thisisn't necessary just for inspection.That gives vou evervthing HubSpot will tell vou about a specific portal's limits. Save the tworequests as a Postman collection with (faccess token}} and (portal idi} ascollectionvariables and vou can profile anv portal in two clicks.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..running and testing as it goesWrite a message…Opus 4.7 Adaptive v"supoont Dally • In 47m100% L2Thu 7 May 14:13:15Q Searchn. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.GET next off. •HTTP https:pi.hubapi.com/account-info/v3/detailsnttps://api.nubapi.com/account-info/vs/details= Docs Params Authorization • Headers 9 Body Scripts SettingsAuth TypeRearer TokenTokenine autnorization neader will oe automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorization{tokenl}hellGET httos://:No environmentv~ SaveCookies4* AIVariables in requestG token> All variablesCNeR-JHaMxlZoiNO.Body Cookies 1 Headers 20 lest ResultsJSONvPreviewe. Visualize v"portalld": 4392066"timeZone". "US/Eastern" .200 OK • 591 ms • 1.23 KB •(| .•CQ108Globals Vault Tools?000...
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ClaudeFileEditVIewWindowmelt= HubSpot rate limit i ClaudeFileEditVIewWindowmelt= HubSpot rate limit implementation strategy vX-HubSpot-RateLimit-Secondly-RemainingDeprecated, ignoreSO Max=190, Interval=10000 means this portal is on a Pro/ Enterprise privately-distributedaoo aliyuius. I vousee max=110 .s a pubnc VAutn instal. max=250 means they nave theAPI Limit Increase add-on.What vou cannot get via API• Search 5/sec limit - not exposed anywhere. Not in headers (search responses stripthem), not in any endpoint. It's a documented constant: vou assume it.• Daily usage broken out by app within a portal — the daily endpoint aggregates all privateapps. You can't tell from the API which app spent the budget.• Per-app burst limit programmatically — only inferred from Max in headers from a callthat app madePostman recide to fullv profile a vortallIhree calis in order.1. GET /account-info/v3/details →grab portalid. timeZone also notethe x-HunSnot-Rate imit-x resnonse neaders (this is vour burst nronle)2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spend.reset time3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, but thisisn't necessary just for inspection.That gives vou evervthing HubSpot will tell vou about a specific portal's limits. Save the tworequests as a Postman collection with (faccess token}} and (portal idi} ascollectionvariables and vou can profile anv portal in two clicks.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..running and testing as it goesWrite a message…Opus 4.7 Adaptive v"supoont Dally • In 47m100% L2Thu 7 May 14:13:15Q Searchn. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.GET next off. •HTTP https:pi.hubapi.com/account-info/v3/detailsnttps://api.nubapi.com/account-info/vs/details= Docs Params Authorization • Headers 9 Body Scripts SettingsAuth TypeRearer TokenTokenine autnorization neader will oe automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorization{tokenl}hellGET httos://:No environmentv~ SaveCookies4* AIVariables in requestG token> All variablesCNeR-JHaMxlZoiNO.Body Cookies 1 Headers 20 lest ResultsJSONvPreviewe. Visualize v"portalld": 4392066"timeZone". "US/Eastern" .200 OK • 591 ms • 1.23 KB •(| .•CQ108Globals Vault Tools?000...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyX-HubSpot-RateLimit-Secondly-RemainingDeprecated, ignoreSO Max=190, Interval=10000 means this portal is on a Pro/ Enterprise privately-distributedaoo aliyuius. I vousee max=110 .rs a oubnc VAutn nstal. max=250 means they nave uneAPI Limit Increase add-on.What vou cannot get via API• Search 5/sec limit - not exposed anywhere. Not in headers (search responses stripthem), not in any endpoint. It's a documented constant; vou assume it.• Daily usage broken out by app within a portal — the daily endpoint aggregates all privateapps. You can't tell from the API which app spent the budget.• Per-app burst limit programmatically — only inferred from Max in headers from a callthat app madePostman recide to fullv profile a vortallIhree calls in order.1. GET /account-info/v3/details →grab portalld. timeZone also notethe x-HunSnot-Rate imit-x resnonse neaders (this is vour burst nronle)2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spend.reset time3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, but thisisn't necessary just for inspection.That gives vou evervthing HubSpot will tell vou about a specific portal's limits. Save the tworequests as a Postman collection with (faccess token}} and ((portal idi} ascollectionvariables and vou can profile anv portal in two clicks.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..running and testing as it goesWrite a message…Opus 4.7 Adaptive vhellQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.https://lapi.hubapi.com/account-info/v3/api-usaqe/daily/private-apps= DocsParamsAuthorization • Headers 9 Body ScriptsSettinasAuth TypeTokenThe authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationGET httos://:{tokenl}"supoont Dally • In 47m100% L2Thu 7 May 14:13:18No environmentvg Save4* AIVariables in request> All variablesv COLLECtIONscontacts› CRM ObjectseRM owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET det subscriptionsPOST create subscrintionGET Journal earliestGET Journal latestGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offcotpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAI.GET DEAL WITH HISTORY pRODEpTICC VA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOSt search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 TermirCNeR-JHaMxlZoiNd.CookiesBOdVJSONvPreview? Visualize"portalld": 4392066"additionalCurrencies": (200 OK • 591 ms • 1.23 KB •(| .•CQ108Globals Vault Tools?000...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyX-HubSpot-RateLimit-Secondly-RemainingDeprecated, ignoreSO Max=190, Interval=10000 means this portal is on a Pro/ Enterprise privately-distributedaoo aliyuius. I vousee max=110 .rs a oubnc VAutn nstal. max=250 means they nave uneAPI Limit Increase add-on.What vou cannot get via API• Search 5/sec limit - not exposed anywhere. Not in headers (search responses stripthem), not in any endpoint. It's a documented constant; vou assume it.• Daily usage broken out by app within a portal — the daily endpoint aggregates all privateapps. You can't tell from the API which app spent the budget.• Per-app burst limit programmatically — only inferred from Max in headers from a callthat app madePostman recide to fullv profile a vortallIhree calls in order.1. GET /account-info/v3/details →grab portalld. timeZone also notethe x-HunSnot-Rate imit-x resnonse neaders (this is vour burst nronle)2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spend.reset time3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, but thisisn't necessary just for inspection.That gives vou evervthing HubSpot will tell vou about a specific portal's limits. Save the tworequests as a Postman collection with (faccess token}} and ((portal idi} ascollectionvariables and vou can profile anv portal in two clicks.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..running and testing as it goesWrite a message…Opus 4.7 Adaptive vhellQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.https://lapi.hubapi.com/account-info/v3/api-usaqe/daily/private-apps= DocsParamsAuthorization • Headers 9 Body ScriptsSettinasAuth TypeTokenThe authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationGET httos://:{tokenl}"supoont Dally • In 47m100% L2Thu 7 May 14:13:18No environmentvg Save4* AIVariables in request> All variablesv COLLECtIONscontacts› CRM ObjectseRM owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET det subscriptionsPOST create subscrintionGET Journal earliestGET Journal latestGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offcotpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAI.GET DEAL WITH HISTORY pRODEpTICC VA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOSt search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 TermirCNeR-JHaMxlZoiNd.CookiesBOdVJSONvPreview? Visualize"portalld": 4392066"additionalCurrencies": (200 OK • 591 ms • 1.23 KB •(| .•CQ108Globals Vault Tools?000...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ahol§ Support Daily - in 47 m100% [DEV (docker)83Thu 7 May 14:13:18T81₴6DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zshPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ahol§ Support Daily - in 47 m100% [DEV (docker)83Thu 7 May 14:13:18T81₴6DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zshPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ahol§ Support Daily - in 47 m100% C 8DEV (docker)83Thu 7 May 14:13:27T81₴6DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zshPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ahol§ Support Daily - in 47 m100% C 8DEV (docker)83Thu 7 May 14:13:27T81₴6DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zshPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyX-HubSpot-RateLimit-Secondly-RemainingDeprecated, ignoreSO Max=190, Interval=10000 means this portal is on a Pro/ Enterprise privately-distributedaoo aliyuius. I vousee max=110 .rs a oubnc VAutn nstal. max=250 means they nave uneAPI Limit Increase add-on.What vou cannot get via API• Search 5/sec limit - not exposed anywhere. Not in headers (search responses stripthem), not in any endpoint. It's a documented constant; vou assume it.• Daily usage broken out by app within a portal — the daily endpoint aggregates all privateapps. You can't tell from the API which app spent the budget.• Per-app burst limit programmatically — only inferred from Max in headers from a callthat app madePostman recide to fullv profile a vortallIhree calls in order.1. GET /account-info/v3/details →grab portalld. timeZone also notethe x-HunSnot-Rate imit-x resnonse neaders (this is vour burst nronle)2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spend.reset time3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, but thisisn't necessary just for inspection.That gives vou evervthing HubSpot will tell vou about a specific portal's limits. Save the tworequests as a Postman collection with (faccess token}} and ((portal idi} ascollectionvariables and vou can profile anv portal in two clicks.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..running and testing as it goesWrite a message…Opus 4.7 Adaptive v0 hllQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.https://lapi.hubapi.com/account-info/v3/api-usaqe/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps= DocsParamsAuthorization • Headers 9 Body ScriptsSettinasAuth TypeTokenThe authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationGET httos://:{tokenl}"supoont Dally • In 47m100% L2Inu / May 14:13-20No environmentva SaveSeng* ~ Cookies4* AIVariables in requestG token› All Varlablesv COLLECtIONscontacts› CRM ObjectseRM owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET det subscriptionsPOST create subscrintionGET Journal earliestGET lournal latestGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offcotpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAI.GET DEAL WITH HISTORY pRODEpTICC VA©Authi> Properties> RESSARCHwCCADAUPOSt search contact bv ohonePOSt search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWS$ Connect Git @ Console TermirCNeR-JHaMxlZoiNd.BOdVJSONv200 OK • 591 ms • 1.23 KB •(| .•CQ108Previewp. Visualize v"portalld": 4392066odditionalCurroncioch. ПGlobals Vault Tools?000...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyX-HubSpot-RateLimit-Secondly-RemainingDeprecated, ignoreSO Max=190, Interval=10000 means this portal is on a Pro/ Enterprise privately-distributedaoo aliyuius. I vousee max=110 .rs a oubnc VAutn nstal. max=250 means they nave uneAPI Limit Increase add-on.What vou cannot get via API• Search 5/sec limit - not exposed anywhere. Not in headers (search responses stripthem), not in any endpoint. It's a documented constant; vou assume it.• Daily usage broken out by app within a portal — the daily endpoint aggregates all privateapps. You can't tell from the API which app spent the budget.• Per-app burst limit programmatically — only inferred from Max in headers from a callthat app madePostman recide to fullv profile a vortallIhree calls in order.1. GET /account-info/v3/details →grab portalld. timeZone also notethe x-HunSnot-Rate imit-x resnonse neaders (this is vour burst nronle)2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spend.reset time3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, but thisisn't necessary just for inspection.That gives vou evervthing HubSpot will tell vou about a specific portal's limits. Save the tworequests as a Postman collection with (faccess token}} and ((portal idi} ascollectionvariables and vou can profile anv portal in two clicks.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..running and testing as it goesWrite a message…Opus 4.7 Adaptive v0 hllQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.https://lapi.hubapi.com/account-info/v3/api-usaqe/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps= DocsParamsAuthorization • Headers 9 Body ScriptsSettinasAuth TypeTokenThe authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationGET httos://:{tokenl}"supoont Dally • In 47m100% L2Inu / May 14:13-20No environmentva SaveSeng* ~ Cookies4* AIVariables in requestG token› All Varlablesv COLLECtIONscontacts› CRM ObjectseRM owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET det subscriptionsPOST create subscrintionGET Journal earliestGET lournal latestGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offcotpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAI.GET DEAL WITH HISTORY pRODEpTICC VA©Authi> Properties> RESSARCHwCCADAUPOSt search contact bv ohonePOSt search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWS$ Connect Git @ Console TermirCNeR-JHaMxlZoiNd.BOdVJSONv200 OK • 591 ms • 1.23 KB •(| .•CQ108Previewp. Visualize v"portalld": 4392066odditionalCurroncioch. ПGlobals Vault Tools?000...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return...
|
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Pro","depth":15,"bounds":{"left":0.0043218085,"top":0.6943336,"width":0.037898935,"height":0.01915403},"on_screen":true,"role_description":"pop-up button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Get apps and extensions","depth":15,"bounds":{"left":0.08277926,"top":0.6943336,"width":0.007978723,"height":0.01915403},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"HubSpot rate limit implementation strategy, rename chat","depth":19,"bounds":{"left":0.043218084,"top":0.02793296,"width":0.09773936,"height":0.022346368},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"HubSpot rate limit implementation 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chat","depth":21,"bounds":{"left":0.48537233,"top":0.026336791,"width":0.010638298,"height":0.025538707},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Claude finished the response","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"You said: So just a solution for rate limit implementation.","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: So just a solution for rate limit implementation.","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating activity (engagement, meeting, call, note)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"searching for a company or contact","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating deal","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"patching IDs\nReview what the limit is at the moment in the HubSpot documentation and then think of the solution:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":25,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). Example of such a error Client error:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST https://api.hubapi.com/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"resulted in a","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"429 Too Many Requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"response: {\"status\":\"error\",\"message\":\"You have reached your secondly limit.\",\"errorType\":\"RATE_LIMIT\",\"correlationId\":\"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). 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Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. 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Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What \"two buckets per request\" actually costs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What \"two buckets per request\" actually costs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Each request makes","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"one round trip to Redis","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"lua","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[1] = burst bucket key (or search)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[2] = daily bucket key","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[1] = now (ms) ARGV[2] = window_ms","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[3] = bucket_max ARGV[4] = daily_max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[5] = request_id ARGV[6] = daily_ttl","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- Trim sliding 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Tell caller how long to sleep until oldest entry expires","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"local","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"oldest","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZRANGE'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'WITHSCORES'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"return","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"{","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'BURST'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"oldest","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ahol§ Support Daily - in 47 m100% [8DEV (docker)83Thu 7 May 14:13:52181₴6DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84ffmpeg•$5-zshPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ahol§ Support Daily - in 47 m100% [8DEV (docker)83Thu 7 May 14:13:52181₴6DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84ffmpeg•$5-zshPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyX-HubSpot-RateLimit-Secondly-RemainingDeprecated, ignoreSO Max=190, Interval=10000 means this portal is on a Pro/ Enterprise privately-distributedaoo aliyuius. I vousee max=110 .rs a oubnc VAutn nstal. max=250 means they nave uneAPI Limit Increase add-on.What vou cannot get via API• Search 5/sec limit - not exposed anywhere. Not in headers (search responses stripthem), not in any endpoint. It's a documented constant; vou assume it.• Daily usage broken out by app within a portal — the daily endpoint aggregates all privateapps. You can't tell from the API which app spent the budget.• Per-app burst limit programmatically — only inferred from Max in headers from a callthat app madePostman recide to fullv profile a vortallIhree calls in order.1. GET /account-info/v3/details →grab portalld. timeZone also notethe x-HunSnot-Rate imit-x resnonse neaders (this is vour burst nronle)2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spend.reset time3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, but thisisn't necessary just for inspection.That gives vou evervthing HubSpot will tell vou about a specific portal's limits. Save the tworequests as a Postman collection with (faccess token}} and ((portal idi} ascollectionvariables and vou can profile anv portal in two clicks.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..running and testing as it goesWrite a message…Opus 4.7 Adaptive v40hellXx Hubspot vQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appsnttps://api.nubapi.com/account-info/vs/apl-usageo wy/private-apps= DocsParamsAuthorization • Headers 9 Body Scripts SettinasAuth TypeTokenThe authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationGET Get EngGET httos://:{tokenl}"supoont Dally • In 47mNo environmentvg Save100% L24* AIVariables in request> All variablesThu 7 May 14:13:52v COLLECtIONscontacts› CRM ObjectseRM owners> CRM PipelinesDealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscrintionGET Journal earliestGET lournal latesGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offcotpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAI.GET DEAL WITH HISTORY pRODEpTICC VA©Authi> Properties> RESSARCHwCCADAUPOSt search contact bv ohonePOST search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 TermirCNeR-JHaMxlZoiNd.CookiesBodyJSONvPreviewe. Visualize"results":"name": "private-aops-aoi-calls-daiilv".."ucadel imi+". 1000000"collectedat": "2026-05-07T11:13:28.621Z""2026-05-08T04:00:002200 OK • 229 ms • 1.21 KB •(| .•CQ108Globals Vault Tools?000...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyX-HubSpot-RateLimit-Secondly-RemainingDeprecated, ignoreSO Max=190, Interval=10000 means this portal is on a Pro/ Enterprise privately-distributedaoo aliyuius. I vousee max=110 .rs a oubnc VAutn nstal. max=250 means they nave uneAPI Limit Increase add-on.What vou cannot get via API• Search 5/sec limit - not exposed anywhere. Not in headers (search responses stripthem), not in any endpoint. It's a documented constant; vou assume it.• Daily usage broken out by app within a portal — the daily endpoint aggregates all privateapps. You can't tell from the API which app spent the budget.• Per-app burst limit programmatically — only inferred from Max in headers from a callthat app madePostman recide to fullv profile a vortallIhree calls in order.1. GET /account-info/v3/details →grab portalld. timeZone also notethe x-HunSnot-Rate imit-x resnonse neaders (this is vour burst nronle)2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spend.reset time3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, but thisisn't necessary just for inspection.That gives vou evervthing HubSpot will tell vou about a specific portal's limits. Save the tworequests as a Postman collection with (faccess token}} and ((portal idi} ascollectionvariables and vou can profile anv portal in two clicks.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..running and testing as it goesWrite a message…Opus 4.7 Adaptive v40hellXx Hubspot vQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/daily/private-appsnttps://api.nubapi.com/account-info/vs/apl-usageo wy/private-apps= DocsParamsAuthorization • Headers 9 Body Scripts SettinasAuth TypeTokenThe authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationGET Get EngGET httos://:{tokenl}"supoont Dally • In 47mNo environmentvg Save100% L24* AIVariables in request> All variablesThu 7 May 14:13:52v COLLECtIONscontacts› CRM ObjectseRM owners> CRM PipelinesDealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscrintionGET Journal earliestGET lournal latesGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offcotpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAI.GET DEAL WITH HISTORY pRODEpTICC VA©Authi> Properties> RESSARCHwCCADAUPOSt search contact bv ohonePOST search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 TermirCNeR-JHaMxlZoiNd.CookiesBodyJSONvPreviewe. Visualize"results":"name": "private-aops-aoi-calls-daiilv".."ucadel imi+". 1000000"collectedat": "2026-05-07T11:13:28.621Z""2026-05-08T04:00:002200 OK • 229 ms • 1.21 KB •(| .•CQ108Globals Vault Tools?000...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ahol§ Support Daily - in 47 m100% [DEV (docker)83Thu 7 May 14:13:58181₴6DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zshPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ahol§ Support Daily - in 47 m100% [DEV (docker)83Thu 7 May 14:13:58181₴6DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zshPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyX-HubSpot-RateLimit-Secondly-RemainingDeprecated, ignoreSO Max=190, Interval=10000 means this portal is on a Pro/ Enterprise privately-distributedaoo aliyuius. I vousee max=110 .rs a oubnc VAutn nstal. max=250 means they nave uneAPI Limit Increase add-on.What vou cannot get via API• Search 5/sec limit - not exposed anywhere. Not in headers (search responses stripthem), not in any endpoint. It's a documented constant: vou assume it• Daily usage broken out by app within a portal - the daily endpoint aggregates all privateapps. You can't tell from the API which app spent the budget.• Per-app burst limit programmatically — only interred from Max in headers from a callthat app madePostman recide to fullv profile a vortallIhree calls in order.1. GET /account-info/v3/details →grab portalid. timeZone also notethe x-HunSnot-Rate imit-x resnonse neaders (this is vour burst nronle)2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spend.reset time3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, but thisisn't necessary just for inspection.That gives vou evervthing HubSpot will tell vou about a specific portal's limits. Save the tworequests as a Postman collection with (faccess token}} and ((portal idi} ascollectionvariables and vou can profile anv portal in two clicks.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..running and testing as it goesWrite a message…Opus 4.7 Adaptive vhellQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.GET read ciGET Get EnaGET httos://:HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/secondlv/private-appshttps://api.hubapi.com/account-info/v3/api-usage/sec&hdly/private-apps= DocsParamsAuthorization • Headers 9 Body Scripts SettinasAuth TypeToken{tokenl}The authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorization"supoont Dally • In 47mNo environmentv~ SaveCookies100% L2Inu / May 14.13.09UparadeCOLLECTIONScontacts› CRM ObjectseRM owners> CRM PipelinesDealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscrintionGET Journal earliestGET lournal latesGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offsetpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAI.GET DEAL WITH HISTORY pRODEpTICC VA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOSt search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWS$ Connect Git @ Console 2 TerminVariables in requestG tokenAll variablesCNeR-JHaMxlZoiNd.BodyCookies 17> HTMLvPreviewY^ Debua with Al l v<meta htto-equiv="Content-Tvoe" content="text/html.charset=utf-8" /><titlesFrror 404 Not Foundk/titlesALASUTTO CODAD ЛЛЙИILО-404 Not Found • 146 ms • 1.01 KB • @| ..Globals Vault Tools? 0 0 0...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyX-HubSpot-RateLimit-Secondly-RemainingDeprecated, ignoreSO Max=190, Interval=10000 means this portal is on a Pro/ Enterprise privately-distributedaoo aliyuius. I vousee max=110 .rs a oubnc VAutn nstal. max=250 means they nave uneAPI Limit Increase add-on.What vou cannot get via API• Search 5/sec limit - not exposed anywhere. Not in headers (search responses stripthem), not in any endpoint. It's a documented constant: vou assume it• Daily usage broken out by app within a portal - the daily endpoint aggregates all privateapps. You can't tell from the API which app spent the budget.• Per-app burst limit programmatically — only interred from Max in headers from a callthat app madePostman recide to fullv profile a vortallIhree calls in order.1. GET /account-info/v3/details →grab portalid. timeZone also notethe x-HunSnot-Rate imit-x resnonse neaders (this is vour burst nronle)2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spend.reset time3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, but thisisn't necessary just for inspection.That gives vou evervthing HubSpot will tell vou about a specific portal's limits. Save the tworequests as a Postman collection with (faccess token}} and ((portal idi} ascollectionvariables and vou can profile anv portal in two clicks.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..running and testing as it goesWrite a message…Opus 4.7 Adaptive vhellQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.GET read ciGET Get EnaGET httos://:HTTP https:pi.hubapi.com/account-info/v3/api-usaqe/secondlv/private-appshttps://api.hubapi.com/account-info/v3/api-usage/sec&hdly/private-apps= DocsParamsAuthorization • Headers 9 Body Scripts SettinasAuth TypeToken{tokenl}The authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorization"supoont Dally • In 47mNo environmentv~ SaveCookies100% L2Inu / May 14.13.09UparadeCOLLECTIONScontacts› CRM ObjectseRM owners> CRM PipelinesDealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscrintionGET Journal earliestGET lournal latesGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offsetpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAI.GET DEAL WITH HISTORY pRODEpTICC VA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOSt search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWS$ Connect Git @ Console 2 TerminVariables in requestG tokenAll variablesCNeR-JHaMxlZoiNd.BodyCookies 17> HTMLvPreviewY^ Debua with Al l v<meta htto-equiv="Content-Tvoe" content="text/html.charset=utf-8" /><titlesFrror 404 Not Foundk/titlesALASUTTO CODAD ЛЛЙИILО-404 Not Found • 146 ms • 1.01 KB • @| ..Globals Vault Tools? 0 0 0...
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2026-05-07T11:14:07.032263+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152447032_m2.jpg...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
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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
[...
|
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Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. 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Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What \"two buckets per request\" actually costs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What \"two buckets per request\" actually costs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Each request makes","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"one round trip to Redis","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"lua","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[1] = burst bucket key (or search)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[2] = daily bucket key","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[1] = now (ms) ARGV[2] = window_ms","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[3] = bucket_max ARGV[4] = daily_max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[5] = request_id ARGV[6] = daily_ttl","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- Trim sliding window","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZREMRANGEBYSCORE'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"local","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"burst_used","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZCARD'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"local","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"daily_used","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"tonumber","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'GET'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"2","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'0'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"burst_used","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":">=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"tonumber","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"3","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"then","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- Tell caller how long to sleep until oldest entry expires","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"local","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"oldest","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZRANGE'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'WITHSCORES'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"return","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"{","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'BURST'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"oldest","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"}]...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
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┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]Support Daily • in 46 m100% C 8DEV (docker)83Thu 7 May 14:14:09T81₴6DOCKERLast login: Thu MayDEV (docker)H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh7 09:44:56 on ttys006Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]Support Daily • in 46 m100% C 8DEV (docker)83Thu 7 May 14:14:09T81₴6DOCKERLast login: Thu MayDEV (docker)H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh7 09:44:56 on ttys006Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyX-HubSpot-RateLimit-Secondly-RemainingDeprecated, ignoreSO Max=190, Interval=10000 means this portal is on a Pro/ Enterprise privately-distributedaoo aliyuius. I vousee max=110 .rs a oubnc VAutn nstal. max=250 means they nave uneAPI Limit Increase add-on.What vou cannot get via API• Search 5/sec limit - not exposed anywhere. Not in headers (search responses stripthem), not in any endpoint. It's a documented constant; vou assume it.• Daily usage broken out by app within a portal - the daily endpoint aggregates all privateapps. You can't tell from the API which app spent the budget.• Per-app burst limit programmatically — only interred from Max in headers from a callthat app madePostman recide to fullv profile a vortallIhree calls in order.1. GET /account-info/v3/details →grab portalld. timeZone also notethe x-HunSnot-Rate imit-x resnonse neaders (this is vour burst nronle)2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spend.reset time3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, but thisisn't necessary just for inspection.That gives vou evervthing HubSpot will tell vou about a specific portal's limits. Save the tworequests as a Postman collection with (faccess token}} and ((portal idi} ascollectionvariables and vou can profile anv portal in two clicks.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno.running and testing as it goesI can see dailvOpus 4.7 Adaptive vInu / May 14.14.12UparadeQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.https://lapi.hubapi.com/account-info/v3/api-usaqe/daily/private-appsnttps://api.nubapi.com/account-info/vs/api-usage= DocsParamsAuthorization • Headers 9 Body ScriptsSettinasAuth TypeTokenThe authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationGET read ci{tokenl}GET httos://:supoont Dally • In 40 mNo environmentva Save2 ~ Cookies100% L24* AIVariables in requestG tokenAll variablesCOLLECTIONScontacts› CRM ObjectseRM owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscrintionGET Journal earliestGET lournal latesGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offcotpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAL.GET DEAL WITH HISTORY pRODEpTICC VA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOSt search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWS$ Connect Git @ Console TermirCNeR-JHaMxlZoiNd.BOdVCookies 17> HTML vPreviewY^ Debua with Al l v<meta htto-equiv="Content-Tvoe" content="text/html.charset=utf-8" /><titlesFrror 404 Not Foundk/titlesALASUTTO CODAD ЛЛЙИILО-404 Not Found • 146 ms • 1.01 KB • @| ..Globals Vault Tools? 0 0 0...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyX-HubSpot-RateLimit-Secondly-RemainingDeprecated, ignoreSO Max=190, Interval=10000 means this portal is on a Pro/ Enterprise privately-distributedaoo aliyuius. I vousee max=110 .rs a oubnc VAutn nstal. max=250 means they nave uneAPI Limit Increase add-on.What vou cannot get via API• Search 5/sec limit - not exposed anywhere. Not in headers (search responses stripthem), not in any endpoint. It's a documented constant; vou assume it.• Daily usage broken out by app within a portal - the daily endpoint aggregates all privateapps. You can't tell from the API which app spent the budget.• Per-app burst limit programmatically — only interred from Max in headers from a callthat app madePostman recide to fullv profile a vortallIhree calls in order.1. GET /account-info/v3/details →grab portalld. timeZone also notethe x-HunSnot-Rate imit-x resnonse neaders (this is vour burst nronle)2. GET /account-info/v3/api-usage/daily/private-apps → daily limit, current spend.reset time3. (Optional) Trigger a 429 deliberately on a sandbox to confirm policyName shape, but thisisn't necessary just for inspection.That gives vou evervthing HubSpot will tell vou about a specific portal's limits. Save the tworequests as a Postman collection with (faccess token}} and ((portal idi} ascollectionvariables and vou can profile anv portal in two clicks.Keep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno.running and testing as it goesI can see dailvOpus 4.7 Adaptive vInu / May 14.14.12UparadeQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.https://lapi.hubapi.com/account-info/v3/api-usaqe/daily/private-appsnttps://api.nubapi.com/account-info/vs/api-usage= DocsParamsAuthorization • Headers 9 Body ScriptsSettinasAuth TypeTokenThe authorization header will be automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationGET read ci{tokenl}GET httos://:supoont Dally • In 40 mNo environmentva Save2 ~ Cookies100% L24* AIVariables in requestG tokenAll variablesCOLLECTIONScontacts› CRM ObjectseRM owners> CRM Pipelines> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscrintionGET Journal earliestGET lournal latesGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offcotpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAL.GET DEAL WITH HISTORY pRODEpTICC VA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOSt search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWS$ Connect Git @ Console TermirCNeR-JHaMxlZoiNd.BOdVCookies 17> HTML vPreviewY^ Debua with Al l v<meta htto-equiv="Content-Tvoe" content="text/html.charset=utf-8" /><titlesFrror 404 Not Foundk/titlesALASUTTO CODAD ЛЛЙИILО-404 Not Found • 146 ms • 1.01 KB • @| ..Globals Vault Tools? 0 0 0...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused....
|
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The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"}]...
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Bulgarian citizenship application process for EU residents
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HubSpot rate limit implementation strategy
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HubSpot rate limit handling with executeRequest
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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
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....
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152459774_m2.jpg...
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HubSpot rate limit implementation strategy, rename chat
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More options for HubSpot rate limit implementation strategy
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).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
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┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
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php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
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lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS
[
1
]
,
ARGV
[
1
]
,
ARGV
[
5
]
)
redis
.
call
(
'PEXPIRE'
,
KEYS
[
1
]
,
ARGV
[
2
]...
|
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chat","depth":21,"bounds":{"left":0.48537233,"top":0.026336791,"width":0.010638298,"height":0.025538707},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Claude finished the response","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"You said: So just a solution for rate limit implementation.","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: So just a solution for rate limit implementation.","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating activity (engagement, meeting, call, note)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"searching for a company or contact","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating deal","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"patching IDs\nReview what the limit is at the moment in the HubSpot documentation and then think of the solution:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":25,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). Example of such a error Client error:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST https://api.hubapi.com/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"resulted in a","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"429 Too Many Requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"response: {\"status\":\"error\",\"message\":\"You have reached your secondly limit.\",\"errorType\":\"RATE_LIMIT\",\"correlationId\":\"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. 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Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. 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Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. 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HubSpot rate limit implementation strategy, rename chat
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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Give negative feedback
Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS
[
1
]
,
ARGV
[
1
]
,
ARGV
[
5
]
)
redis
.
call
(
'PEXPIRE'
,
KEYS
[
1
]
,
ARGV
[
2
]...
|
NULL
|
NULL
|
NULL
|
NULL
|
|
2395
|
103
|
5
|
2026-05-07T11:14:21.771162+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152461771_m1.jpg...
|
Claude
|
Claude
|
True
|
NULL
|
monitor_1
|
NULL
|
NULL
|
NULL
|
NULL
|
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
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Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[...
|
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The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating activity (engagement, meeting, call, note)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"searching for a company or contact","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating deal","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"patching IDs\nReview what the limit is at the moment in the HubSpot documentation and then think of the solution:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":25,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). Example of such a error Client error:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST https://api.hubapi.com/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"resulted in a","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"429 Too Many Requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"response: {\"status\":\"error\",\"message\":\"You have reached your secondly limit.\",\"errorType\":\"RATE_LIMIT\",\"correlationId\":\"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What \"two buckets per request\" actually costs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What \"two buckets per request\" actually costs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Each request makes","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"one round trip to Redis","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"lua","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[1] = burst bucket key (or search)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[2] = daily bucket key","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[1] = now (ms) ARGV[2] = window_ms","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[3] = bucket_max ARGV[4] = daily_max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[5] = request_id ARGV[6] = daily_ttl","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- Trim sliding window","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZREMRANGEBYSCORE'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"}]...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
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
[...
|
NULL
|
NULL
|
NULL
|
NULL
|
|
2397
|
104
|
7
|
2026-05-07T11:14:22.852022+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152462852_m2.jpg...
|
Claude
|
Claude
|
True
|
NULL
|
monitor_2
|
NULL
|
NULL
|
NULL
|
NULL
|
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
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┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
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php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
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|
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strategy","depth":21,"bounds":{"left":0.04454787,"top":0.031923383,"width":0.09507979,"height":0.014365523},"on_screen":true,"lines":[{"char_start":0,"char_count":1,"bounds":{"left":0.04454787,"top":0.031923383,"width":0.003656915,"height":0.014365523}},{"char_start":1,"char_count":41,"bounds":{"left":0.048204787,"top":0.031923383,"width":0.09142287,"height":0.014365523}}],"role_description":"text"},{"role":"AXPopUpButton","text":"More options for HubSpot rate limit implementation strategy","depth":19,"bounds":{"left":0.14128989,"top":0.02793296,"width":0.0066489363,"height":0.022346368},"on_screen":true,"role_description":"pop-up button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Share chat","depth":21,"bounds":{"left":0.48537233,"top":0.026336791,"width":0.010638298,"height":0.025538707},"on_screen":true,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Claude finished the response","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"You said: So just a solution for rate limit implementation.","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: So just a solution for rate limit implementation.","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating activity (engagement, meeting, call, note)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"searching for a company or contact","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating deal","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"patching IDs\nReview what the limit is at the moment in the HubSpot documentation and then think of the solution:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":25,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). Example of such a error Client error:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST https://api.hubapi.com/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"resulted in a","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"429 Too Many Requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"response: {\"status\":\"error\",\"message\":\"You have reached your secondly limit.\",\"errorType\":\"RATE_LIMIT\",\"correlationId\":\"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false}]...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
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Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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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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Edit
Copy...
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152490026_m2.jpg...
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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
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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
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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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Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Edit
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Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
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lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS
[
1
]
,
ARGV
[
1
]
,
ARGV
[
5
]
)
redis
.
call
(
'PEXPIRE'
,
KEYS
[
1
]
,
ARGV
[
2
]
+
1000
)
local
d
=
redis
.
call
(
'INCR'
,
KEYS
[
2
]
)
if
d
==
1
then
redis
.
call
(
'EXPIRE'
,
KEYS
[
2
]
,
ARGV
[
6
]
)
end
return
{
1
,
'OK'
,
tonumber
(
ARGV
[
3
]
)
-
burst_used
-
1
}
One...
|
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The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. 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What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":25,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). Example of such a error Client error:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST https://api.hubapi.com/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"resulted in a","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"429 Too Many Requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"response: {\"status\":\"error\",\"message\":\"You have reached your secondly limit.\",\"errorType\":\"RATE_LIMIT\",\"correlationId\":\"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. 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Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. 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This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. 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Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. 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Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? 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HubSpot rate limit implementation strategy, rename chat
HubSpot rate limit implementation strategy
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-
ARGV
[
2
]
)
local
burst_used
=
redis
.
call
(
'ZCARD'
,
KEYS
[
1
]
)
local
daily_used
=
tonumber
(
redis
.
call
(
'GET'
,
KEYS
[
2
]
)
or
'0'
)
if
burst_used
>=
tonumber
(
ARGV
[
3
]
)
then
-- Tell caller how long to sleep until oldest entry expires
local
oldest
=
redis
.
call
(
'ZRANGE'
,
KEYS
[
1
]
,
0
,
0
,
'WITHSCORES'
)
return
{
0
,
'BURST'
,
(
oldest
[
2
]
+
ARGV
[
2
]
)
-
ARGV
[
1
]
}
end
if
daily_used
>=
tonumber
(
ARGV
[
4
]
)
then
return
{
0
,
'DAILY'
,
-
1
}
end
redis
.
call
(
'ZADD'
,
KEYS
[
1
]
,
ARGV
[
1
]
,
ARGV
[
5
]
)
redis
.
call
(
'PEXPIRE'
,
KEYS
[
1
]
,
ARGV
[
2
]
+
1000
)
local
d
=
redis
.
call
(
'INCR'
,
KEYS
[
2
]
)
if
d
==
1
then
redis
.
call
(
'EXPIRE'
,
KEYS
[
2
]
,
ARGV
[
6
]
)
end
return
{
1
,
'OK'
,
tonumber
(
ARGV
[
3
]
)
-
burst_used
-
1
}
One...
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NULL
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11
|
2026-05-07T11:14:56.909658+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152496909_m2.jpg...
|
Claude
|
Claude
|
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key...
|
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Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What \"two buckets per request\" actually costs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What \"two buckets per request\" actually costs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Each request makes","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"one round trip to Redis","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"lua","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[1] = burst bucket key (or search)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[2] = daily bucket key","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"}]...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
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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...
|
NULL
|
NULL
|
NULL
|
NULL
|
|
2403
|
103
|
7
|
2026-05-07T11:14:56.976256+00:00
|
/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152496976_m1.jpg...
|
Claude
|
Claude
|
True
|
NULL
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monitor_1
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NULL
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
Retry
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
Retry
Edit
Copy
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.
Analyzed per-request overhead concerns and quantified Redis performance impact
Analyzed per-request overhead concerns and quantified Redis performance impact
Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.
What "two buckets per request" actually costs
What "two buckets per request" actually costs
Each request makes
one round trip to Redis
— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:
Copy to clipboard
lua
-- KEYS[1] = burst bucket key (or search)
-- KEYS[2] = daily bucket key
-- ARGV[1] = now (ms) ARGV[2] = window_ms
-- ARGV[3] = bucket_max ARGV[4] = daily_max
-- ARGV[5] = request_id ARGV[6] = daily_ttl
-- Trim sliding window
redis
.
call
(
'ZREMRANGEBYSCORE'
,
KEYS
[
1
]
,
0
,
ARGV
[
1
]
-...
|
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The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating activity (engagement, meeting, call, note)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"searching for a company or contact","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"updating deal","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"patching IDs\nReview what the limit is at the moment in the HubSpot documentation and then think of the solution:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. 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Example of such a error Client error:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST https://api.hubapi.com/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"resulted in a","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"429 Too Many Requests","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"response: {\"status\":\"error\",\"message\":\"You have reached your secondly limit.\",\"errorType\":\"RATE_LIMIT\",\"correlationId\":\"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Show more","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"10:42","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Current HubSpot limits (Apr 2026)","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Current HubSpot limits (Apr 2026)","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Current HubSpot limits (Apr 2026)","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The burst window is per app; the daily quota is per portal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". That's the limit the 429 in your example is hitting (","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"secondly limit","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/crm/v3/objects/contact/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). It's a separate bucket from the 190/10s, and critically, search responses do not include the","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-*","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"headers","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— you can't read remaining capacity off the response, you have to track it yourself.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search endpoints return","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", and","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". The daily headers are not returned for OAuth-authenticated calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— that's enforced for marketplace listing, but it's also a sane operational target.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXLink","text":"hubspot","depth":26,"on_screen":false,"role_description":"link","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"So you have","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"four buckets","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Where to store it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Where to store it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"EXPIRE","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"plus Lua scripting for true CAS semantics.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Key layout I'd use:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:search:{portalId} ZSET (sliding 1s)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId} HASH (last seen headers)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The two short-window buckets are sliding-window-log (Redis ZSET, score =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"microtime","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The daily bucket is a plain","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"INCR","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with TTL set to seconds-until-midnight in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubSpot's account timezone","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to read it","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to read it","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two-phase: optimistic pre-check, authoritative post-update.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Before a call, run the Lua acquire script. If it returns \"denied,\" sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"After the call, parse","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Max","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-Interval-Milliseconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and store them in","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot:rl:meta:{portalId}","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"For search specifically, since headers don't come back, the local counter","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"is","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"not","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"on 429 (you really did make that request).","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with multiple jobs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with multiple jobs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Concurrency cap","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"via","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Queue::throttle()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Per-tenant fairness","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Priority lanes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-priority","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hubspot-bulk","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Backoff on 429","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— respect the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"header. If absent, exponential with jitter (e.g.,","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"min(2^attempt * 250ms, 30s) ± 20%","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"How to work with paginated requests","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"How to work with paginated requests","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The trap is treating \"fetch all\" as one logical operation. Each page is its own API call and competes for tokens with everything else.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Two practical rules:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Don't hold the worker idle while paginating.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"processing","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"of page N as a separate job, and queue a \"fetch page N+1\" job with a delay equal to the wait time. Each page becomes its own atomic unit.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Always batch where HubSpot offers it.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/{type}/batch/{read|update|create}","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"accepts up to 100 IDs per call. For your \"patching IDs\" flow this is the difference between 600 calls and 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Walkthrough: 600 opportunities","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Walkthrough: 600 opportunities","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Sane flow:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Identify deltas","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— if you can avoid Search at all, do. Use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"current state —","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/read","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 IDs per call → 6 calls. Burst weight 6.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Compute diff","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in your code (no API).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Batch update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v3/objects/deals/batch/update","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"with 100 per call → 6 calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Associations","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"—","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"POST /crm/v4/associations/{from}/{to}/batch/create","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"if needed → another few batch calls.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Recognition: pre-check denies if","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"<weight>","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"slots aren't free; on 429 the response surfaces","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(DAILY/SECONDLY) — log it, push the job back with","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry-After","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"delay, increment a metric so you can alarm on sustained 429 rate.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Pitfalls & bottlenecks worth pre-empting","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Pitfalls & bottlenecks worth pre-empting","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Daily quota is shared across the whole portal","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search is uniquely fragile","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"hs_object_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ranges or by","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"windows.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Retry storms after HubSpot incidents","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Webhook → sync → webhook loops","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"jy_last_sync_id","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":") and ignore webhooks where it just changed.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Token refresh","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"expires_in - 60s","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", single-flight the refresh (only one worker refreshes; others wait on a lock).","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search latency for fresh writes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Clock skew","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— Redis is authoritative. Don't use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"time()","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from each worker for window scoring; use","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis.call('TIME')","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inside the Lua script.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Dead workers with held tokens","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The 5% error rule","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Phantom contention with Laravel","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"RateLimited","depth":28,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"middleware","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":": it's per-job-class by default, not per-portal. If your","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SyncDealJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"and","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"UpdateActivityJob","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"HubspotRateLimiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"service that everything routes through.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: What will happen if there are two different types of rate limits?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: What will happen if there are two different types of rate limits?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"What will happen if there are two different types of rate limits?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search requests that have 5 requests per second","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:40","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: They're independent buckets — with one shared bucket on top.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed independent rate limit buckets and their practical interactions","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed independent rate limit buckets and their practical interactions","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"They're independent buckets — with one shared bucket on top.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"The three buckets and what each call hits","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"The three buckets and what each call hits","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"┌──────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search request ──→ │ Search: 5/sec │ ──┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"├──→ ┌─────────────────┐","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"┌──────────────────┐ │ │ Daily: 250k–1M │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"└──────────────────┘ └─────────────────┘","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What this means practically","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What this means practically","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting search secondly limit (5/s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only further","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"/search","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"lastmodifieddate","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting burst limit (190/10s):","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Hitting daily limit:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"So can one type of request block the other?","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"So can one type of request block the other?","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Search → Burst:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Burst → Search:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"No.","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Saturating burst leaves Search's 5/s untouched.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Either → Daily:","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":", but only by contributing to the same daily counter. It's volume that kills you here, not the type.","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In theory you can sustain ~50 search + ~190 other =","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"~240 calls per 10 seconds","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"indefinitely (until daily runs out), because the two buckets bill independently.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"Implementation consequence","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Implementation consequence","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"php","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'search'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for search endpoints only","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'burst'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// for everything else","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$limiter","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"->","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'daily'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"$weight","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"=","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":")","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":";","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"// every call, both kinds","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('search')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":". A batch update calls","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('burst')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"+","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"acquire('daily')","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"One subtle pitfall: when you backoff on a 429,","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"inspect","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"policyName","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"in the response body","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to know which bucket actually fired. If you see","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"SECONDLY","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"The other subtlety: the burst response gives you","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"X-HubSpot-RateLimit-Remaining","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give positive feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Give negative feedback","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"You said: Ok, so what will happen each time I make a request?","depth":20,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"You said: Ok, so what will happen each time I make a request?","depth":21,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?","depth":24,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"13:44","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Retry","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Edit","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXButton","text":"Copy","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXHeading","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":21,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"Claude responded: Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out.","depth":22,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":22,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"Analyzed per-request overhead concerns and quantified Redis performance impact","depth":23,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Yes, your understanding is correct — every request must check and reserve from each bucket it applies to, atomically, before the call goes out. But the overhead is much smaller than it sounds, and importantly, it's nowhere near your bottleneck.","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXHeading","text":"What \"two buckets per request\" actually costs","depth":24,"on_screen":false,"role_description":"heading"},{"role":"AXStaticText","text":"What \"two buckets per request\" actually costs","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"Each request makes","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"one round trip to Redis","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"— not two — because you fold both bucket checks into a single Lua script that runs atomically server-side:","depth":25,"on_screen":false,"role_description":"text"},{"role":"AXButton","text":"Copy to clipboard","depth":27,"on_screen":false,"role_description":"button","is_enabled":true,"is_focused":false,"is_selected":false,"is_expanded":false},{"role":"AXStaticText","text":"lua","depth":26,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[1] = burst bucket key (or search)","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- KEYS[2] = daily bucket key","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[1] = now (ms) ARGV[2] = window_ms","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[3] = bucket_max ARGV[4] = daily_max","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- ARGV[5] = request_id ARGV[6] = daily_ttl","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-- Trim sliding window","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"redis","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":".","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"call","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"(","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"'ZREMRANGEBYSCORE'","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"KEYS","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"0","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":",","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"ARGV","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"[","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"1","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"]","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"","depth":27,"on_screen":false,"role_description":"text"},{"role":"AXStaticText","text":"-","depth":27,"on_screen":false,"role_description":"text"}]...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
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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
]
-...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyIcan see daily https:/api.hubapi.com/account=into/v3/details what aboutsearch and burstDistinguished burst limits via headers from undocumented search constraints ›Burst: visible. but in response headers, not the body. Any non-searen call worksn Postman. send.Reply -GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token}Then in the response panel click the Headers tab (not Body). You'll see something like:X-HubSpot-RateLimit-Max: 190X-HubSpot-RateLimit-Remaining: 189X-HubSpot-RateLimit-Interval-Milliseconds: 10000That tells you the portal is on 190 calls per 10 seconds. The Remaining value is live — fire thesame call again immediatelv and it drops to 188. etc. There is no endpoint that returns this inJSON; the headers are the only channel.Search: not visible. At all.•No endnoint exnoses the s/see search limin• Search responses (POST /crm/v3/objects/{type}/search ) deliberately strip all x-HubSpot-RateLimit-* headers, so you can't read it from a search call either.• The 5/sec figure is a documented constant — you assume it, you don't query it.• The only way to "observe" the search limit empirically is to deliberately exceed it andinspect the 429 response, where the body contains "policyName""SECONDLY" and"message": "You have reached your secondly limit." — which is what you alreadysaw in vour original error.So vour tul ver-borta bicture from the APuisKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptive vhhlQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.HITP https://api.hubapi.com/account-info/v3/api-usage/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps= DocsParamsAuthorization • Headers 9 Body ScriptsSettinasAuth TypeTokenine autnorization neader will oe automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationGET httos://:{tokenl}"suppont Dally • In 40m100% L2Thu 7 May 14:15:02No environmentv~ Savecookies4* AIVariables in requestG token> All variablesCOLLECTIONScontacts› CRM ObjectseRM owners> CRM PipelinesDealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscrintionGET Journal earliestGET lournal latestGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offsetpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAL.GET DEAL WITH HISTORY pRODEptIcC vA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOSt search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 TerminCNeR-JHaMxlZoiNd.BOdVJSONvPreviewp. Visualize v"results":"name": "private-aops-aoi-calls-dailv"."ucadel imi+". 1000000"collectedAt": "2026-05-07T11:14:12.879Z""2026-05-08T04:00:002200 OK • 198 ms • 1.21 KB • (| .•=Q 8Globals Vault Tools?000...
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PostmanEditVIewWindowmelpHubSpot rate limit imple PostmanEditVIewWindowmelpHubSpot rate limit implementation strategyIcan see daily https:/api.hubapi.com/account=into/v3/details what aboutsearch and burstDistinguished burst limits via headers from undocumented search constraints ›Burst: visible. but in response headers, not the body. Any non-searen call worksn Postman. send.Reply -GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token}Then in the response panel click the Headers tab (not Body). You'll see something like:X-HubSpot-RateLimit-Max: 190X-HubSpot-RateLimit-Remaining: 189X-HubSpot-RateLimit-Interval-Milliseconds: 10000That tells you the portal is on 190 calls per 10 seconds. The Remaining value is live — fire thesame call again immediatelv and it drops to 188. etc. There is no endpoint that returns this inJSON; the headers are the only channel.Search: not visible. At all.•No endnoint exnoses the s/see search limin• Search responses (POST /crm/v3/objects/{type}/search ) deliberately strip all x-HubSpot-RateLimit-* headers, so you can't read it from a search call either.• The 5/sec figure is a documented constant — you assume it, you don't query it.• The only way to "observe" the search limit empirically is to deliberately exceed it andinspect the 429 response, where the body contains "policyName""SECONDLY" and"message": "You have reached your secondly limit." — which is what you alreadysaw in vour original error.So vour tul ver-borta bicture from the APuisKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptive vhhlQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.HITP https://api.hubapi.com/account-info/v3/api-usage/daily/private-appshttps://api.hubapi.com/account-info/v3/api-usage/daily/private-apps= DocsParamsAuthorization • Headers 9 Body ScriptsSettinasAuth TypeTokenine autnorization neader will oe automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorizationGET httos://:{tokenl}"suppont Dally • In 40m100% L2Thu 7 May 14:15:02No environmentv~ Savecookies4* AIVariables in requestG token> All variablesCOLLECTIONScontacts› CRM ObjectseRM owners> CRM PipelinesDealsEngagements• D OLD ENGAGEMENTSGET list meetingsPST search moditied companiePOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET get subscriptionsPOST create subscrintionGET Journal earliestGET lournal latestGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offsetpost get loken prodDEL DELSTE CURSCRIPTION DEP PORTAL.GET DEAL WITH HISTORY pRODEptIcC vA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOSt search contact by emailPOST search meetinasPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 TerminCNeR-JHaMxlZoiNd.BOdVJSONvPreviewp. Visualize v"results":"name": "private-aops-aoi-calls-dailv"."ucadel imi+". 1000000"collectedAt": "2026-05-07T11:14:12.879Z""2026-05-08T04:00:002200 OK • 198 ms • 1.21 KB • (| .•=Q 8Globals Vault Tools?000...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]• Support Daily • in 45 m100% C 8Thu 7 May 14:15:02DEV (docker)83DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]• Support Daily • in 45 m100% C 8Thu 7 May 14:15:02DEV (docker)83DOCKERLast login: Thu MayO ₴1DEV (docker)7 09:44:56 on ttys006H82APP (-zsh)-zsh• 84screenpipe*•$5-zsh₴6Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]• Support Daily • in 45 m100% [Thu 7 May 14:15:17DEV (docker)181DOCKERLast login: Thu MayDEV (docker)H82APP (-zsh)83-zsh• 84screenpipe*•$5-zsh₴67 09:44:56 on ttys006Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelp(ah]• Support Daily • in 45 m100% [Thu 7 May 14:15:17DEV (docker)181DOCKERLast login: Thu MayDEV (docker)H82APP (-zsh)83-zsh• 84screenpipe*•$5-zsh₴67 09:44:56 on ttys006Poetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentsPoetry could not find a pyproject.toml file in /Users/lukas/jiminny/app or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/jiminny/app (JY-20773-fix-automated-reports-user-pilot-tracking) $ devroot@docker_lamp_1:/home/jiminny# php artisan crm:sync-opportunity --teamId=2 --from='2026-05-01 00:00:00*Syncing opportunity for HubspotYour HubSpotaccount has become disconnected. Please login to Jiminny to reconnect. skipping...root@docker_lamp_1:/home/jiminny# php artisan jiminny:token-info -A 1499 -RDEVaccess_tokenCFPAsBNZxoDp5kAcRyeB1QoE5SM7DSgNuYTFSAFoAYABo3tj9DHAAeAACNeR-JHgMxIZQLNQMl8kQEwrAgwACAkUAhIJBB4BAQEDBxiCiYwCIN7Y_Qwo0qwCMhTnG549n-YtNuc1jgj-2AsLPSmw3DoyQLNQM18kQEwrAiUACBkGawEFThwBARIBAQEEATEEAQEBAQEBAQEBAQUBEggBAQEBAY1access_token_expires_at= 2026-05-07 11:41:20refresh_token => d5ab04e2-2109-4c0b-b513-8cba1dd54371refresh_token_expires_at =root@docker_lamp_1:/home/jiminny#...
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152517501_m2.jpg...
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PostmanEditVIewWindowhelpHubSpot rate limit imple PostmanEditVIewWindowhelpHubSpot rate limit implementation strategyIcan see daily https:/api.hubapi.com/account=into/v3/details what aboutsearch and burstDistinguished burst limits via headers from undocumented search constraints ›Burst: visible. but in response headers, not the body. Any non-searen call worksn Postman. send.Reply -GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token}Then in the response panel click the Headers tab (not Body). You'll see something like:X-HubSpot-RateLimit-Max: 190X-HubSpot-RateLimit-Remaining: 189X-HubSpot-RateLimit-Interval-Milliseconds: 10000That tells you the portal is on 190 calls per 10 seconds. The Remaining value is live — fire thesame call again immediatelv and it drops to 188. etc. There is no endpoint that returns this inJSON; the headers are the only channel.Search: not visible. At all.•No endnoint exnoses the s/see search limin• Search responses (POST /crm/v3/objects/{type}/search ) deliberately strip all x-HubSpot-RateLimit-* headers, so you can't read it from a search call either.• The 5/sec figure is a documented constant — you assume it, you don't query it.• The only way to "observe" the search limit empirically is to deliberately exceed it andinspect the 429 response, where the body contains "policyName""SECONDLY" and"message": "You have reached your secondly limit." — which is what you alreadysaw in vour original error.So vour tul ver-borta bicture from the APuisKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptive vQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.GET next off. •HTTP https:pi.hubapi.com/account-info/v3/detailsnttps://api.nubapi.com/account-info/vs/details= DocsAuthorization • Headers 9 Body ScriptsSettinasAuth TypeTokenine autnorization neader will oe automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorization.GET httos://:"suppont Dally • In 40mNo environmentv~ Save100% L24* AIVariables in requestG tokenCookies› All VarlablesThu 7 May 14:15:17UparadeCNeR-JHaMxlZoiNd.v COLLECtIONscontacts› CRM ObjectseRM ownersCRM PioelinecDealsEngagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET det subscriptionsPOST create subscrintionGET Journal earliestGET Journal latestGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offsetpost get loken prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY pRODEptIcC vA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOSt search contact by emailPOST search meetingsPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 Termirggers 20200 ok • 209ms • 1.24 KB • CA **•Statusdatecontent-typecf-rayct-cache-statusstrict-transport-securityaccess-control-allow-credentialsserver-uimingx-hubspot-correlation.idy-hubenot.ratolimit.intorval-millicocondcx-hubspot-ratelimit-remainingychubsnot-ratelimit-cocondlvreport-toThu. 07 Mav 2026 11:15:08 GMTIapplication/json;charset=utf-89f7fce1hd89dhda6-sofDYNAMICmax-age=31536000; includeSubDomains: preloadorigin, Accept-Encodingfalseheididace-"01000226.0dha-7012-052h-2072211/0762" Afridace-"0f76c01h011d2402-1A0%nosnift019e0226-0dba-7a1a-853b-a97a211c97f210000("endpoints":[{"url":"https://a.nel.cloudflare.comVreport\v4?s=%2B8ZxycfkqBM4vc64itFbi.f"quececs fraction".0.01 "renort to"."ef-nel""may aae".604900}cloudflareGlobals Vault Tools?000...
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PostmanEditVIewWindowhelpHubSpot rate limit imple PostmanEditVIewWindowhelpHubSpot rate limit implementation strategyIcan see daily https:/api.hubapi.com/account=into/v3/details what aboutsearch and burstDistinguished burst limits via headers from undocumented search constraints ›Burst: visible. but in response headers, not the body. Any non-searen call worksn Postman. send.Reply -GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token}Then in the response panel click the Headers tab (not Body). You'll see something like:X-HubSpot-RateLimit-Max: 190X-HubSpot-RateLimit-Remaining: 189X-HubSpot-RateLimit-Interval-Milliseconds: 10000That tells you the portal is on 190 calls per 10 seconds. The Remaining value is live — fire thesame call again immediatelv and it drops to 188. etc. There is no endpoint that returns this inJSON; the headers are the only channel.Search: not visible. At all.•No endnoint exnoses the s/see search limin• Search responses (POST /crm/v3/objects/{type}/search ) deliberately strip all x-HubSpot-RateLimit-* headers, so you can't read it from a search call either.• The 5/sec figure is a documented constant — you assume it, you don't query it.• The only way to "observe" the search limit empirically is to deliberately exceed it andinspect the 429 response, where the body contains "policyName""SECONDLY" and"message": "You have reached your secondly limit." — which is what you alreadysaw in vour original error.So vour tul ver-borta bicture from the APuisKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptive vQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.GET next off. •HTTP https:pi.hubapi.com/account-info/v3/detailsnttps://api.nubapi.com/account-info/vs/details= DocsAuthorization • Headers 9 Body ScriptsSettinasAuth TypeTokenine autnorization neader will oe automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorization.GET httos://:"suppont Dally • In 40mNo environmentv~ Save100% L24* AIVariables in requestG tokenCookies› All VarlablesThu 7 May 14:15:17UparadeCNeR-JHaMxlZoiNd.v COLLECtIONscontacts› CRM ObjectseRM ownersCRM PioelinecDealsEngagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPOST search tasksGET read call> Post search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET det subscriptionsPOST create subscrintionGET Journal earliestGET Journal latestGeT https:ubspot-webhooks-lournal-nal.s.GET neyt offsetpost get loken prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY pRODEptIcC vA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOSt search contact by emailPOST search meetingsPOST search notes> Post Search calls v3.IPOST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 Termirggers 20200 ok • 209ms • 1.24 KB • CA **•Statusdatecontent-typecf-rayct-cache-statusstrict-transport-securityaccess-control-allow-credentialsserver-uimingx-hubspot-correlation.idy-hubenot.ratolimit.intorval-millicocondcx-hubspot-ratelimit-remainingychubsnot-ratelimit-cocondlvreport-toThu. 07 Mav 2026 11:15:08 GMTIapplication/json;charset=utf-89f7fce1hd89dhda6-sofDYNAMICmax-age=31536000; includeSubDomains: preloadorigin, Accept-Encodingfalseheididace-"01000226.0dha-7012-052h-2072211/0762" Afridace-"0f76c01h011d2402-1A0%nosnift019e0226-0dba-7a1a-853b-a97a211c97f210000("endpoints":[{"url":"https://a.nel.cloudflare.comVreport\v4?s=%2B8ZxycfkqBM4vc64itFbi.f"quececs fraction".0.01 "renort to"."ef-nel""may aae".604900}cloudflareGlobals Vault Tools?000...
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2026-05-07T11:15:51.524068+00:00
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/Users/lukas/.screenpipe/data/data/2026-05-07/1778 /Users/lukas/.screenpipe/data/data/2026-05-07/1778152551524_m2.jpg...
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PostmanEditVIewWindowhelpHubSpot rate limit imple PostmanEditVIewWindowhelpHubSpot rate limit implementation strategyIcan see daily https:/api.hubapi.com/account=into/v3/details what aboutsearch and burstDistinguished burst limits via headers from undocumented search constraints ›Burst: visible. but in response headers, not the body. Any non-searen call worksn Postman. send.Reply -GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token}Then in the response panel click the Headers tab (not Body). You'll see something like:X-HubSpot-RateLimit-Max: 190X-HubSpot-RateLimit-Remaining: 189X-HubSpot-RateLimit-Interval-Milliseconds: 10000That tells you the portal is on 190 calls per 10 seconds. The Remaining value is live — fire thesame call again immediatelv and it drops to 188. etc. There is no endpoint that returns this inJSON; the headers are the only channel.Search: not visible. At all.•No endnoint exnoses the s/see search limin• Search responses (POST /crm/v3/objects/{type}/search ) deliberately strip all x-HubSpot-RateLimit-* headers, so you can't read it from a search call either.• The 5/sec figure is a documented constant — you assume it, you don't query it.• The only way to "observe" the search limit empirically is to deliberately exceed it andinspect the 429 response, where the body contains "policyName""SECONDLY" and"message": "You have reached your secondly limit." — which is what you alreadysaw in vour original error.So vour tul ver-borta bicture from the APuisKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptive vClaude ic Aland can mako mistakes Plesce double-chock racnoncac)Thu 7 May 14:15:51UparadeQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.GET next off.GET read ciGET httos://:HTTP https:pi.hubapi.com/account-info/v3/detailsnttps://api.nubapi.com/accoun-info/vs/details= DocsAuthorization • Headers 9 Body ScriptsSettinasAuth TypeTokenine autnorization neader will oe automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorization.suppont Dally • In 40mNo environmentv~ SaveCookies100% L24* AIVariables in requestG token› All VarlablesCOLLECTIONScontacts› CRM ObjectseRM ownersCRM Pioelinec> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPOST search tasksGET read call> PosT search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET det subscriptionsPOST create subscrintionGET Journal earliestGET Journal latestGET https:ubspot-webhooks-lournal-nal.s.GET neyt offsetpost get loken prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY pRODEpTIcC vA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOST search contact by emailPOST search meetingsPOST search notes> Post Search calls v3.POST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 TermirCNeR-JHaMxlZoiNd.Cookiesistatusdatecontent-tvoecf-rayef-cache-statusstrict-transport-securityaccess-control-allow-credentialsserver-timingyacontent-tune.ontionsx-hubspot-correlation-idw.hubenot.rotolimit.intorvol.milliconandewwlnerrwolmteohhnay-hubsnot-ratelimit-secondlv-remainingcontent-encodind200 OK • 209 ms • 1.24 KB • (| .•Thu. 07 Mav 2026 11:15:08 GMTIapplication/ison:charset=utf-89f7fce1hd89dhda6.sopDYNAMICmax-age=31536000; includeSubDomains: preloadorigin, Accept-Encodingtalsehcid:desc="019e0226-0dba-7a1a-853b-a97a211c97f2" cfr'desc="9f7fce1be11d3402-IAD"nosniff019e0226-0dba-7a1a-853b-a97a211c976210000"endpoints":""url":httos:Va.nel.cloudflare.com/report/v4?s=%288ZxvcfkaBM4vc64itFbi.f"cuecoss fraction".0.01 "ronort to"."of_nolkimay aag".604900}cloudflareGlobals Vault Tools?000...
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PostmanEditVIewWindowhelpHubSpot rate limit imple PostmanEditVIewWindowhelpHubSpot rate limit implementation strategyIcan see daily https:/api.hubapi.com/account=into/v3/details what aboutsearch and burstDistinguished burst limits via headers from undocumented search constraints ›Burst: visible. but in response headers, not the body. Any non-searen call worksn Postman. send.Reply -GET https://api.hubapi.com/account-info/v3/detailsAuthorization: Bearer {token}Then in the response panel click the Headers tab (not Body). You'll see something like:X-HubSpot-RateLimit-Max: 190X-HubSpot-RateLimit-Remaining: 189X-HubSpot-RateLimit-Interval-Milliseconds: 10000That tells you the portal is on 190 calls per 10 seconds. The Remaining value is live — fire thesame call again immediatelv and it drops to 188. etc. There is no endpoint that returns this inJSON; the headers are the only channel.Search: not visible. At all.•No endnoint exnoses the s/see search limin• Search responses (POST /crm/v3/objects/{type}/search ) deliberately strip all x-HubSpot-RateLimit-* headers, so you can't read it from a search call either.• The 5/sec figure is a documented constant — you assume it, you don't query it.• The only way to "observe" the search limit empirically is to deliberately exceed it andinspect the 429 response, where the body contains "policyName""SECONDLY" and"message": "You have reached your secondly limit." — which is what you alreadysaw in vour original error.So vour tul ver-borta bicture from the APuisKeep going in Claude CodeSwitch to Claude Code and let Claude work directiv in vour reno..Write a message…Opus 4.7 Adaptive vClaude ic Aland can mako mistakes Plesce double-chock racnoncac)Thu 7 May 14:15:51UparadeQ SearchYour team is now on the Free plan with 1 admin. You retain editing access and other members are read-only. View team permissions to see who can edit, or upgrade to restore collaboration.GET next off.GET read ciGET httos://:HTTP https:pi.hubapi.com/account-info/v3/detailsnttps://api.nubapi.com/accoun-info/vs/details= DocsAuthorization • Headers 9 Body ScriptsSettinasAuth TypeTokenine autnorization neader will oe automaticallygenerated when you send the reauest. Learnmore about Bearer Token authorization.suppont Dally • In 40mNo environmentv~ SaveCookies100% L24* AIVariables in requestG token› All VarlablesCOLLECTIONScontacts› CRM ObjectseRM ownersCRM Pioelinec> DealsEngagements• D OLD ENGAGEMENTSGET list meetingsPOST search moditied companiesPOST search tasksGET read call> PosT search callsGET list callsPOST meetings scheduledGET aet meetinaPOST get link to task> POST Create Contact with Associatio> HubsnotJournal & webhoooks v4POSt Get tokenGET det subscriptionsPOST create subscrintionGET Journal earliestGET Journal latestGET https:ubspot-webhooks-lournal-nal.s.GET neyt offsetpost get loken prodDEL DELETE SUBSCRIPTION PER PORTALGET DEAL WITH HISTORY pRODEpTIcC vA©Authi> Properties> RESSARCHwCCADAUPOST search contact bv ohonePOST search contact by emailPOST search meetingsPOST search notes> Post Search calls v3.POST Search related meetinas v3POST search dealsCAMIDONMCNTC> SPFCSELOWSConnect Git E Console 2 TermirCNeR-JHaMxlZoiNd.Cookiesistatusdatecontent-tvoecf-rayef-cache-statusstrict-transport-securityaccess-control-allow-credentialsserver-timingyacontent-tune.ontionsx-hubspot-correlation-idw.hubenot.rotolimit.intorvol.milliconandewwlnerrwolmteohhnay-hubsnot-ratelimit-secondlv-remainingcontent-encodind200 OK • 209 ms • 1.24 KB • (| .•Thu. 07 Mav 2026 11:15:08 GMTIapplication/ison:charset=utf-89f7fce1hd89dhda6.sopDYNAMICmax-age=31536000; includeSubDomains: preloadorigin, Accept-Encodingtalsehcid:desc="019e0226-0dba-7a1a-853b-a97a211c97f2" cfr'desc="9f7fce1be11d3402-IAD"nosniff019e0226-0dba-7a1a-853b-a97a211c976210000"endpoints":""url":httos:Va.nel.cloudflare.com/report/v4?s=%288ZxvcfkaBM4vc64itFbi.f"cuecoss fraction".0.01 "ronort to"."of_nolkimay aag".604900}cloudflareGlobals Vault Tools?000...
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