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
Good Schema Design and Why It Matters
Search
Andrew Godwin
May 15, 2014
Programming
12
1.2k
Good Schema Design and Why It Matters
A talk I gave at DjangoCon Europe 2014.
Andrew Godwin
May 15, 2014
Tweet
Share
More Decks by Andrew Godwin
See All by Andrew Godwin
Reconciling Everything
andrewgodwin
1
260
Django Through The Years
andrewgodwin
0
160
Writing Maintainable Software At Scale
andrewgodwin
0
400
A Newcomer's Guide To Airflow's Architecture
andrewgodwin
0
310
Async, Python, and the Future
andrewgodwin
2
610
How To Break Django: With Async
andrewgodwin
1
670
Taking Django's ORM Async
andrewgodwin
0
680
The Long Road To Asynchrony
andrewgodwin
0
590
The Scientist & The Engineer
andrewgodwin
1
700
Other Decks in Programming
See All in Programming
React 19でお手軽にCSS-in-JSを自作する
yukukotani
5
560
DevinとCursorから学ぶAIエージェントメモリーの設計とMoatの考え方
itarutomy
0
110
Fixstars高速化コンテスト2024準優勝解法
eijirou
0
190
AppRouterを用いた大規模サービス開発におけるディレクトリ構成の変遷と問題点
eiganken
1
440
歴史と現在から考えるスケーラブルなソフトウェア開発のプラクティス
i10416
0
300
PHPとAPI Platformで作る本格的なWeb APIアプリケーション(入門編) / phpcon 2024 Intro to API Platform
ttskch
0
380
Androidアプリのモジュール分割における:x:commonを考える
okuzawats
1
270
return文におけるstd::moveについて
onihusube
1
1.4k
ChatGPT とつくる PHP で OS 実装
memory1994
PRO
3
190
ATDDで素早く安定した デリバリを実現しよう!
tonnsama
1
1.8k
Azure AI Foundryのご紹介
qt_luigi
1
140
週次リリースを実現するための グローバルアプリ開発
tera_ny
1
1.1k
Featured
See All Featured
GraphQLとの向き合い方2022年版
quramy
44
13k
CoffeeScript is Beautiful & I Never Want to Write Plain JavaScript Again
sstephenson
160
15k
Optimising Largest Contentful Paint
csswizardry
33
3k
GraphQLの誤解/rethinking-graphql
sonatard
68
10k
jQuery: Nuts, Bolts and Bling
dougneiner
62
7.6k
Evolution of real-time – Irina Nazarova, EuRuKo, 2024
irinanazarova
6
500
Design and Strategy: How to Deal with People Who Don’t "Get" Design
morganepeng
127
18k
Why You Should Never Use an ORM
jnunemaker
PRO
54
9.1k
Cheating the UX When There Is Nothing More to Optimize - PixelPioneers
stephaniewalter
280
13k
Faster Mobile Websites
deanohume
305
30k
Docker and Python
trallard
43
3.2k
The Success of Rails: Ensuring Growth for the Next 100 Years
eileencodes
44
7k
Transcript
Andrew Godwin @andrewgodwin GOOD SCHEMA DESIGN WHY IT MATTERS and
Andrew Godwin Core Developer Senior Engineer Author & Maintainer
Schemas Explicit & Implicit
Explicit PostgreSQL MySQL Oracle SQLite CouchDB MongoDB Redis ZODB Implicit
Explicit Schema ID int Name text Weight uint 1 2
3 Alice Bob Charles 76 84 65 Implicit Schema { "id": 342, "name": "David", "weight": 44, }
Explicit Schema Normalised or semi normalised structure JOINs to retrieve
related data Implicit Schema Embedded structure Related data retrieved naturally with object
Silent Failure { "id": 342, "name": "David", "weight": 74, }
{ "id": 342, "name": "Ellie", "weight": "85kg", } { "id": 342, "nom": "Frankie", "weight": 77, } { "id": 342, "name": "Frankie", "weight": -67, }
Schemas inform Storage
PostgreSQL
Adding NULLable columns: instant But must be at end of
table
CREATE INDEX CONCURRENTLY Slower, and only one at a time
Constraints after column addition This is more general advice
MySQL Locks whole table Rewrites entire storage No DDL transactions
Oracle / MSSQL / etc. Look into their strengths
Changing the Schema Databases aren't code...
You can't put your database in a VCS You can
put your schema in a VCS But your data won't always survive.
Django Migrations Codified schema change format
None
Migrations aren't enough You can't automate away a social problem!
What if we got rid of the schema? That pesky,
pesky schema.
The Nesting Problem { "id": 123, "name": "Andrew", "friends": [
{"id": 456, "name": "David"}, {"id": 789, "name": "Mazz"}, ], "likes": [ {"id": 22, "liker": {"id": 789, "name", "Mazz"}}, ], }
You don't have to use a document DB (like CouchDB,
MongoDB, etc.)
Schemaless Columns ID int Name text Weight uint Data json
1 Alice 76 { "nickname": "Al", "bgcolor": "#ff0033" }
But that must be slower... Right?
Comparison (never trust benchmarks) Loading 1.2 million records PostgreSQL MongoDB
76 sec 8 min Sequential scan PostgreSQL MongoDB 980 ms 980 ms Index scan (Postgres GINhash) PostgreSQL MongoDB 0.7 ms 1 ms
Load Shapes
Read-heavy Write-heavy Large size
Read-heavy Write-heavy Large size Wikipedia TV show page Minecraft Forums
Amazon Glacier Eventbrite Logging
Read-heavy Write-heavy Large size Offline storage Append formats In-memory cache
Many indexes Fewer indexes
Your load changes over time Scaling is not just a
flat multiplier
General Advice Write heavy → Fewer indexes Read heavy →
Denormalise Keep large data away from read/write heavy data Blob stores/filesystems are DBs too
Lessons They're near the end so you remember them.
Re-evaulate as you grow Different things matter at different sizes
Adding NULL columns is great Always prefer this if nothing
else
You'll need more than one DBMS But don't use too
many, you'll be swamped
Indexes aren't free You pay the price at write/restore time
Relational DBs are flexible They can do a lot more
than JOINing normalised tables
Thanks! Andrew Godwin @andrewgodwin eventbrite.com/jobs are hiring: