Optimizing GraphQL Performance Tips and Techniques
summary
Shows a concrete path from an N+1 problem that produced 83 database queries to single-query performance using a combination of techniques: query lookahead and the Strawberry Django optimizer add automatic select_related joins; request-scoped DataLoader batching collects IDs and issues one batched SQL call; client and CDN-friendly persisted queries via ID (and Apollo automatic persisted queries) replace large GET bodies; enforceable query depth and weighted complexity limits plus Sentry-style resolver timing give observability and effective protection against expensive or abusive requests.
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chapters (8)
- 0:00GraphQL introduction
- 3:55Shifting complexity to backend
- 4:37N+1 query problem
- 9:34Data loaders for batching
- 13:29Caching challenges in GraphQL
- 17:46Persistent queries for CDN
- 20:37Monitoring misuse
- 23:48Future optimizations & Q&A
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