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
·
SiteGround - Reliable hosting with speed, security, and support you can count on.
→
Python Porto
December 14, 2017
Programming
1
300
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
46
Quick and Robust API with Django Rest Framework
pyporto
1
350
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
66
Python Porto #10. Past, present and future
pyporto
0
95
Entertaining testing with pytest
pyporto
0
210
Joyful Python Web App development with Appier
pyporto
0
170
Other Decks in Programming
See All in Programming
コントリビューターによるDenoのすゝめ / Deno Recommendations by a Contributor
petamoriken
0
200
フルサイクルエンジニアリングをAI Agentで全自動化したい 〜構想と現在地〜
kamina_zzz
0
400
CSC307 Lecture 03
javiergs
PRO
1
490
CSC307 Lecture 07
javiergs
PRO
0
540
そのAIレビュー、レビューしてますか? / Are you reviewing those AI reviews?
rkaga
6
4.5k
AIと一緒にレガシーに向き合ってみた
nyafunta9858
0
120
IFSによる形状設計/デモシーンの魅力 @ 慶應大学SFC
gam0022
1
290
メルカリのリーダビリティチームが取り組む、AI時代のスケーラブルな品質文化
cloverrose
2
510
今から始めるClaude Code超入門
448jp
7
8.1k
Package Management Learnings from Homebrew
mikemcquaid
0
180
コマンドとリード間の連携に対する脅威分析フレームワーク
pandayumi
1
440
OCaml 5でモダンな並列プログラミングを Enjoyしよう!
haochenx
0
110
Featured
See All Featured
DBのスキルで生き残る技術 - AI時代におけるテーブル設計の勘所
soudai
PRO
61
49k
I Don’t Have Time: Getting Over the Fear to Launch Your Podcast
jcasabona
34
2.6k
Efficient Content Optimization with Google Search Console & Apps Script
katarinadahlin
PRO
0
310
How GitHub (no longer) Works
holman
316
140k
jQuery: Nuts, Bolts and Bling
dougneiner
65
8.4k
Chrome DevTools: State of the Union 2024 - Debugging React & Beyond
addyosmani
9
1.1k
Agile Leadership in an Agile Organization
kimpetersen
PRO
0
78
技術選定の審美眼(2025年版) / Understanding the Spiral of Technologies 2025 edition
twada
PRO
117
100k
Unsuck your backbone
ammeep
671
58k
RailsConf & Balkan Ruby 2019: The Past, Present, and Future of Rails at GitHub
eileencodes
141
34k
16th Malabo Montpellier Forum Presentation
akademiya2063
PRO
0
46
[SF Ruby Conf 2025] Rails X
palkan
0
740
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