Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Scale like a pro
Search
Sponsored
·
Your Podcast. Everywhere. Effortlessly.
Share. Educate. Inspire. Entertain. You do you. We'll handle the rest.
→
Python Porto
December 14, 2017
Programming
1
310
Scale like a pro
Distributed task processing with Python and Celery
Python Porto
December 14, 2017
Tweet
Share
More Decks by Python Porto
See All by Python Porto
Detecting phishing with Recurrent Neural Networks
pyporto
0
50
Quick and Robust API with Django Rest Framework
pyporto
1
360
Django as your data management framework
pyporto
1
1.2k
Can my computer make jokes
pyporto
0
120
Building a serverless cloud service
pyporto
0
69
Python Porto #10. Past, present and future
pyporto
0
100
Entertaining testing with pytest
pyporto
0
220
Joyful Python Web App development with Appier
pyporto
0
170
Other Decks in Programming
See All in Programming
nuget-server - あなたが必要だったNuGetサーバー
kekyo
PRO
0
380
Claude Code Skill入門
mayahoney
0
420
What Spring Developers Should Know About Jakarta EE
ivargrimstad
0
550
Fundamentals of Software Engineering In the Age of AI
therealdanvega
2
290
AI時代のシステム設計:ドメインモデルで変更しやすさを守る設計戦略
masuda220
PRO
6
1.1k
20260313 - Grafana & Friends Taipei #1 - Kubernetes v1.36 的開發雜記:那些困在 Alpha 加護病房太久的 Metrics
tico88612
0
230
「効かない!」依存性注入(DI)を活用したAPI Platformのエラーハンドリング奮闘記
mkmk884
0
160
安いハードウェアでVulkan
fadis
0
740
GoのDB アクセスにおける 「型安全」と「柔軟性」の両立 - Bob という選択肢
tak848
0
270
へんな働き方
yusukebe
5
2.8k
AHC061解説
shun_pi
0
410
Laravel Nightwatchの裏側 - Laravel公式Observabilityツールを支える設計と実装
avosalmon
1
140
Featured
See All Featured
The SEO identity crisis: Don't let AI make you average
varn
0
420
Making Projects Easy
brettharned
120
6.6k
DevOps and Value Stream Thinking: Enabling flow, efficiency and business value
helenjbeal
1
150
Chrome DevTools: State of the Union 2024 - Debugging React & Beyond
addyosmani
10
1.1k
How Software Deployment tools have changed in the past 20 years
geshan
0
33k
The browser strikes back
jonoalderson
0
820
Exploring the Power of Turbo Streams & Action Cable | RailsConf2023
kevinliebholz
37
6.3k
Redefining SEO in the New Era of Traffic Generation
szymonslowik
1
250
Taking LLMs out of the black box: A practical guide to human-in-the-loop distillation
inesmontani
PRO
3
2.1k
Paper Plane
katiecoart
PRO
0
48k
How to optimise 3,500 product descriptions for ecommerce in one day using ChatGPT
katarinadahlin
PRO
1
3.5k
RailsConf 2023
tenderlove
30
1.4k
Transcript
Scale like a pro Distributed computing with message queues and
Python Roman Imankulov | Python Porto | December 2017
Source: https://blog.kissmetrics.com/wp-content/uploads/2011/04/loading-time.pdf
browser ./webserver.py request POST /obj/<id> obj.update()
browser ./webserver.py request POST /obj/<id> obj.update() update search indexes
browser ./webserver.py request POST /obj/<id> obj.update() update search indexes send
email “object updated” …
browser ./webserver.py request POST /obj/<id> obj.update() update search indexes update
business analytics send email “object updated” …
browser ./webserver.py request POST /obj/<id> obj.update() update search indexes update
business analytics send email “object updated” … response
None
browser ./webserver.py request POST /obj/<id> response obj.update() ./worker.py ./worker.py ./worker.py
update search indexes send email “object updated” update business analytics
Message queues
None
None
queue
frontends queue
frontends queue workers
frontends queue job 1 workers
frontends queue job 1 workers job 2
frontends queue job 1 workers job 2 job 3
frontends queue put() job 1 workers job 2 job 3
frontends queue put() job 1 workers job 2 job 3
frontends queue job 1 workers job 2 job 3
frontends queue job 1 workers put() job 2 job 3
frontends queue job 1 workers put() job 2 job 3
frontends queue job 1 workers job 2 job 3
frontends queue job 1 workers put() job 2 job 3
frontends queue job 1 workers put() job 2 job 3
frontends queue job 1 workers job 2 job 3
frontends queue job 1 get() workers job 2 job 3
frontends queue get() workers job 2 job 3 job 1
frontends queue workers job 2 job 3 job 1
frontends queue workers get() job 2 job 3 job 1
frontends queue workers get() job 3 job 1 job 2
frontends queue workers job 3 job 1 job 2
frontends queue workers job 3 job 2
frontends queue workers job 3 job 2 get()
frontends queue workers job 2 job 3 get()
frontends queue workers job 2 job 3
frontends queue workers
Queue in python
Multiprocessing
None
None
None
None
None
None
None
Celery The queue out of the box
None
None
None
None
None
Celery Workflows chains, groups and chords
Task signatures
Task signatures
Task signatures
Chains a(…) b(…) c(…)
Chains a(…) b(…) c(…)
Chains a(…) b(…) c(…)
a(…) b(…) c(…) Groups
a(…) b(…) c(…) Groups
a(…) b(…) c(…) Groups
Chords a(…) b(…) c(…) d(…)
Chords a(…) b(…) c(…) d(…)
Chords a(…) b(…) c(…) d(…)
Celery Extras out of the box
None
• Different backends
• Different backends • Different serializers
• Different backends • Different serializers • Callbacks / errbacks
• Different backends • Different serializers • Callbacks / errbacks
• Progress reports from tasks
• Different backends • Different serializers • Callbacks / errbacks
• Progress reports from tasks • Delayed tasks
• Different backends • Different serializers • Callbacks / errbacks
• Progress reports from tasks • Delayed tasks • Ignored results
• Different backends • Different serializers • Callbacks / errbacks
• Progress reports from tasks • Delayed tasks • Ignored results • Expiring results
• Different backends • Different serializers • Callbacks / errbacks
• Progress reports from tasks • Delayed tasks • Ignored results • Expiring results • Retry policies
• Different backends • Different serializers • Callbacks / errbacks
• Progress reports from tasks • Delayed tasks • Ignored results • Expiring results • Retry policies • Time limits on task execution
• Different backends • Different serializers • Callbacks / errbacks
• Progress reports from tasks • Delayed tasks • Ignored results • Expiring results • Retry policies • Time limits on task execution • Rate limits (N tasks per minute)
• Different backends • Different serializers • Callbacks / errbacks
• Progress reports from tasks • Delayed tasks • Ignored results • Expiring results • Retry policies • Time limits on task execution • Rate limits (N tasks per minute) • Autoscaling
• Different backends • Different serializers • Callbacks / errbacks
• Progress reports from tasks • Delayed tasks • Ignored results • Expiring results • Retry policies • Time limits on task execution • Rate limits (N tasks per minute) • Autoscaling • Multiple queues
• Different backends • Different serializers • Callbacks / errbacks
• Progress reports from tasks • Delayed tasks • Ignored results • Expiring results • Retry policies • Time limits on task execution • Rate limits (N tasks per minute) • Autoscaling • Multiple queues • Introspection and statistics
• Different backends • Different serializers • Callbacks / errbacks
• Progress reports from tasks • Delayed tasks • Ignored results • Expiring results • Retry policies • Time limits on task execution • Rate limits (N tasks per minute) • Autoscaling • Multiple queues • Introspection and statistics • Periodic tasks and crontabs
None
facebook.com/pyporto