Optimizing GraphQL Applications
summary
GraphQL optimization focuses on measurable changes: query batching (Apollo batches outgoing queries in 10ms slices) to cut network requests; automatic persisted queries that send a sha‑256 hash instead of huge query strings and store them in memcache; resolver strategies to avoid the n+1 problem (DataLoader, or turning queries into SQL as Hasura does to use Postgres JSON aggregation); and UX techniques like optimistic UI plus Apollo Engine for tracing, error tracking and profiling to spot real bottlenecks.
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chapters (11)
- 0:00Introduction
- 1:03GraphQL Adoption
- 4:00Query Batching
- 8:15Automatic Persistence Queries
- 12:15Apollo Engine
- 13:15Resolvers
- 14:15N1 Query Problem
- 16:45Raw SQL
- 18:25Query Splitting
- 19:15Optimistic UI
- 20:25Final Thoughts
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