Panel Discussion by Apollo Team: GraphQL Durability

The panel discussion focuses on the durability of GraphQL APIs, particularly how to maintain their scalability and reliability under various conditions, and handling GraphQL at scale.

Testing GraphQL APIs is challenging due to the infinite possibilities of queries and the contextual information used by resolvers. The recommendation is to focus on integration testing and testing middleware layers like rate limiting and error handling.

At GitHub, GraphQL API scaling is managed by using a custom implementation of data loaders to avoid N+1 queries and focusing on server-side application level caching.

Netflix handles spikes by scaling GraphQL servers horizontally, implementing service side throttling, client retries, and using an L7 proxy application gateway to reject excessive requests.

Effective caching strategies for GraphQL APIs include using persisted queries to reduce request size and enable HTTP caching, employing data loaders to batch and cache requests, and implementing application-level caching at the resolver level.

Panelists suggest handling errors in GraphQL by translating GraphQL-specific errors to standard HTTP status codes, using the 'errors as data' approach to enrich client applications with detailed error information, and ensuring robust observability and error tracking systems.

Panelists recommend resources like Marc Andre's book 'Production Ready GraphQL', and general materials on distributed systems, site reliability engineering, and domain-driven design to understand and build reliable APIs.