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
Scaling Django with Distributed Systems
Search
Andrew Godwin
April 07, 2017
Programming
3
2.2k
Scaling Django with Distributed Systems
A talk I gave at PyCon Ukraine 2017.
Andrew Godwin
April 07, 2017
Tweet
Share
More Decks by Andrew Godwin
See All by Andrew Godwin
Reconciling Everything
andrewgodwin
1
310
Django Through The Years
andrewgodwin
0
200
Writing Maintainable Software At Scale
andrewgodwin
0
440
A Newcomer's Guide To Airflow's Architecture
andrewgodwin
0
350
Async, Python, and the Future
andrewgodwin
2
660
How To Break Django: With Async
andrewgodwin
1
720
Taking Django's ORM Async
andrewgodwin
0
720
The Long Road To Asynchrony
andrewgodwin
0
650
The Scientist & The Engineer
andrewgodwin
1
760
Other Decks in Programming
See All in Programming
Deep Dive into ~/.claude/projects
hiragram
12
2.3k
XP, Testing and ninja testing
m_seki
3
220
Result型で“失敗”を型にするPHPコードの書き方
kajitack
5
580
イベントストーミング図からコードへの変換手順 / Procedure for Converting Event Storming Diagrams to Code
nrslib
2
600
deno-redisの紹介とJSRパッケージの運用について (toranoana.deno #21)
uki00a
0
180
iOS 26にアップデートすると実機でのHot Reloadができない?
umigishiaoi
0
110
AI時代のソフトウェア開発を考える(2025/07版) / Agentic Software Engineering Findy 2025-07 Edition
twada
PRO
50
13k
生成AIコーディングとの向き合い方、AIと共創するという考え方 / How to deal with generative AI coding and the concept of co-creating with AI
seike460
PRO
1
350
LT 2025-06-30: プロダクトエンジニアの役割
yamamotok
0
680
WebViewの現在地 - SwiftUI時代のWebKit - / The Current State Of WebView
marcy731
0
110
VS Code Update for GitHub Copilot
74th
2
600
「Cursor/Devin全社導入の理想と現実」のその後
saitoryc
0
720
Featured
See All Featured
Bash Introduction
62gerente
614
210k
How to Create Impact in a Changing Tech Landscape [PerfNow 2023]
tammyeverts
53
2.8k
Fight the Zombie Pattern Library - RWD Summit 2016
marcelosomers
233
17k
The Art of Programming - Codeland 2020
erikaheidi
54
13k
[RailsConf 2023 Opening Keynote] The Magic of Rails
eileencodes
29
9.5k
ReactJS: Keep Simple. Everything can be a component!
pedronauck
667
120k
Cheating the UX When There Is Nothing More to Optimize - PixelPioneers
stephaniewalter
281
13k
Why Our Code Smells
bkeepers
PRO
337
57k
Fashionably flexible responsive web design (full day workshop)
malarkey
407
66k
Rails Girls Zürich Keynote
gr2m
94
14k
Build The Right Thing And Hit Your Dates
maggiecrowley
36
2.8k
Responsive Adventures: Dirty Tricks From The Dark Corners of Front-End
smashingmag
252
21k
Transcript
None
Andrew Godwin Hi, I'm Django core developer Senior Software Engineer
at Used to complain about migrations a lot
Distributed Systems
c = 299,792,458 m/s
Early CPUs c = 60m propagation distance Clock ~2cm 5
MHz
Modern CPUs c = 10cm propagation distance 3 GHz
Distributed systems are made of independent components
They are slower and harder to write than synchronous systems
But they can be scaled up much, much further
Trade-offs
There is never a perfect solution.
Fast Good Cheap
None
Load Balancer WSGI Worker WSGI Worker WSGI Worker
Load Balancer WSGI Worker WSGI Worker WSGI Worker Cache
Load Balancer WSGI Worker WSGI Worker WSGI Worker Cache Cache
Cache
Load Balancer WSGI Worker WSGI Worker WSGI Worker Database
CAP Theorem
Partition Tolerant Consistent Available
PostgreSQL: CP Consistent everywhere Handles network latency/drops Can't write if
main server is down
Cassandra: AP Can read/write to any node Handles network latency/drops
Data can be inconsistent
It's hard to design a product that might be inconsistent
But if you take the tradeoff, scaling is easy
Otherwise, you must find other solutions
Read Replicas (often called master/slave) Load Balancer WSGI Worker WSGI
Worker WSGI Worker Replica Replica Main
Replicas scale reads forever... But writes must go to one
place
If a request writes to a table it must be
pinned there, so later reads do not get old data
When your write load is too high, you must then
shard
Vertical Sharding Users Tickets Events Payments
Horizontal Sharding Users 0 - 2 Users 3 - 5
Users 6 - 8 Users 9 - A
Both Users 0 - 2 Users 3 - 5 Users
6 - 8 Users 9 - A Events 0 - 2 Events 3 - 5 Events 6 - 8 Events 9 - A Tickets 0 - 2 Tickets 3 - 5 Tickets 6 - 8 Tickets 9 - A
Both plus caching Users 0 - 2 Users 3 -
5 Users 6 - 8 Users 9 - A Events 0 - 2 Events 3 - 5 Events 6 - 8 Events 9 - A Tickets 0 - 2 Tickets 3 - 5 Tickets 6 - 8 Tickets 9 - A User Cache Event Cache Ticket Cache
Teams have to scale too; nobody should have to understand
eveything in a big system.
Services allow complexity to be reduced - for a tradeoff
of speed
Users 0 - 2 Users 3 - 5 Users 6
- 8 Users 9 - A Events 0 - 2 Events 3 - 5 Events 6 - 8 Events 9 - A Tickets 0 - 2 Tickets 3 - 5 Tickets 6 - 8 Tickets 9 - A User Cache Event Cache Ticket Cache User Service Event Service Ticket Service
User Service Event Service Ticket Service WSGI Server
Each service is its own, smaller project, managed and scaled
separately.
But how do you communicate between them?
Service 2 Service 3 Service 1 Direct Communication
Service 2 Service 3 Service 1 Service 4 Service 5
Service 2 Service 3 Service 1 Service 4 Service 5
Service 6 Service 7 Service 8
Service 2 Service 3 Service 1 Message Bus Service 2
Service 3 Service 1
A single point of failure is not always bad -
if the alternative is multiple, fragile ones
Channels and ASGI provide a standard message bus built with
certain tradeoffs
Backing Store e.g. Redis, RabbitMQ ASGI (Channel Layer) Channels Library
Django Django Channels Project
Backing Store e.g. Redis, RabbitMQ ASGI (Channel Layer) Pure Python
Failure Mode At most once Messages either do not arrive,
or arrive once. At least once Messages arrive once, or arrive multiple times
Guarantees vs. Latency Low latency Messages arrive very quickly but
go missing more Low loss rate Messages are almost never lost but arrive slower
Queuing Type First In First Out Consistent performance for all
users First In Last Out Hides backlogs but makes them worse
Queue Sizing Finite Queues Sending can fail Infinite queues Makes
problems even worse
You must understand what you are making (This is surprisingly
uncommon)
Design as much as possible around shared-nothing
Per-machine caches On-demand thumbnailing Signed cookie sessions
Has to be shared? Try to split it
Has to be shared? Try sharding it.
Django's job is to be slowly replaced by your code
Just make sure you match the API contract of what
you're replacing!
Don't try to scale too early; you'll pick the wrong
tradeoffs.
Thanks. Andrew Godwin @andrewgodwin channels.readthedocs.io