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Good Schema Design and Why It Matters
Andrew Godwin
May 15, 2014
Programming
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1.1k
Good Schema Design and Why It Matters
A talk I gave at DjangoCon Europe 2014.
Andrew Godwin
May 15, 2014
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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: