Скачать или смотреть From Data Loaders To Batch Resolvers - Aileen Chen, Airbnb

From Data Loaders To Batch Resolvers - Aileen Chen, Airbnb

In theory, data loaders solve most "N+1" problems in GraphQL. In practice, they can be hard to implement, so they’re typically used only in performance-critical situations and often reactively, once inefficiencies surface. To achieve better performance, batching needs to be applied wherever possible.

This talk introduces batch resolvers, a more developer-friendly alternative to data loaders. While traditional GraphQL resolvers take a single input and produce a single output, batching resolvers take a list of inputs and return a list of outputs. A batch resolver can simply call a batch service API without worrying about data loaders.

When a developer provides a batch resolver, our GraphQL server automatically aggregates individual data fetches into a single call to that resolver. It can also apply heuristics to improve aggregation, for example by consolidating different selection sets for the same entity into a single input. This design not only makes application developers’ lives easier, but also allows the server to better optimize query execution by coordinating batch dispatching as part of a broader execution strategy.

-------
About GraphQL Foundation
The GraphQL Foundation is a neutral foundation founded by global technology and application development companies. The GraphQL Foundation encourages contributions, stewardship, and a shared investment from a broad group in vendor-neutral events, documentation, tools, and support for GraphQL. Read more at https://graphql.org/community/foundat...

-------
Get More GraphQL
📥 Subscribe for more talks & presentations: @GraphQLFoundationTalks
▶️ Watch the Working Group Meetings: @GraphQLFoundation
✅ Join our Discord: https://discord.graphql.org/
🛠 Contribute on GitHub: https://github.com/graphql
🛼 Follow us on X: https://x.com/graphql
👩‍💼 Follow us on LinkedIn:   / graphql-foundation