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
Python Porto
December 14, 2017
Programming
1
260
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
42
Quick and Robust API with Django Rest Framework
pyporto
1
340
Django as your data management framework
pyporto
1
1.1k
Can my computer make jokes
pyporto
0
110
Building a serverless cloud service
pyporto
0
57
Python Porto #10. Past, present and future
pyporto
0
84
Entertaining testing with pytest
pyporto
0
190
Joyful Python Web App development with Appier
pyporto
0
160
Other Decks in Programming
See All in Programming
Is Xcode slowly dying out in 2025?
uetyo
0
110
Passkeys for Java Developers
ynojima
3
870
deno-redisの紹介とJSRパッケージの運用について (toranoana.deno #21)
uki00a
0
120
レガシーシステムの機能調査・開発におけるAI利活用
takuya_ohtonari
0
610
Webからモバイルへ Vue.js × Capacitor 活用事例
naokihaba
0
730
Gleamという選択肢
comamoca
6
740
Perplexity Slack Botを作ってAI活用を進めた話 / AI Engineering Summit プレイベント
n3xem
0
670
都市をデータで見るってこういうこと PLATEAU属性情報入門
nokonoko1203
1
530
The Evolution of Enterprise Java with Jakarta EE 11 and Beyond
ivargrimstad
1
810
Effect の双対、Coeffect
yukikurage
5
1.4k
単体テストの始め方/作り方
toms74209200
0
510
FormFlow - Build Stunning Multistep Forms
yceruto
1
180
Featured
See All Featured
Designing Experiences People Love
moore
142
24k
Docker and Python
trallard
44
3.4k
The Power of CSS Pseudo Elements
geoffreycrofte
77
5.8k
Product Roadmaps are Hard
iamctodd
PRO
53
11k
Testing 201, or: Great Expectations
jmmastey
42
7.5k
The Art of Programming - Codeland 2020
erikaheidi
54
13k
VelocityConf: Rendering Performance Case Studies
addyosmani
330
24k
GraphQLの誤解/rethinking-graphql
sonatard
71
11k
Refactoring Trust on Your Teams (GOTO; Chicago 2020)
rmw
34
3k
Helping Users Find Their Own Way: Creating Modern Search Experiences
danielanewman
29
2.7k
How to Think Like a Performance Engineer
csswizardry
24
1.7k
Making the Leap to Tech Lead
cromwellryan
134
9.3k
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