GraphQL Data API

1. The client application issues a single GraphQL query (or mutation) declaring exactly which fields, nested relationships, and depth it requires, sent as a POST to a single /graphql endpoint.
2. The GraphQL gateway parses and validates the query against a strongly-typed schema, rejecting malformed or unauthorized field selections before any data is touched.
3. The query planner decomposes the request into a resolver tree, mapping each field to its backing source—a relational table, a document store, or a downstream microservice.
4. Resolvers execute in parallel where possible, and a DataLoader batching layer coalesces N+1 lookups into batched, deduplicated calls to prevent fan-out storms against the underlying stores.
5. The aggregation engine stitches partial results into the exact shape of the original query, applying field-level authorization and null-propagation rules.
6. The GraphQL gateway returns one JSON payload matching the query structure precisely, so the client neither over-fetches unused columns nor makes follow-up round-trips.

• Precise, single-request fetching vs. the operational burden of resolver performance tuning and N+1 mitigation.
• Strongly-typed, self-documenting schema vs. the loss of straightforward HTTP-level caching.
• Backend decoupling behind one graph vs. new attack surface from arbitrarily deep or expensive queries.
• Front-end agility vs. added complexity in authorization applied per field rather than per endpoint.

A travel-booking platform exposes a GraphQL API so its mobile app can render a trip screen in one call: flight legs from a reservations Postgres database, hotel details from a partner microservice, loyalty points from a rewards service, and weather from an external API. Previously the app made five REST calls and discarded most of the returned fields; now a single query requests only the traveler name, itinerary times, and point balance, with DataLoader batching the per-passenger loyalty lookups. Screen load time on 3G dropped sharply, and adding a "seat map" field required only a schema extension—no new endpoint or app release.

• Query cost governance: Enforce depth limits, complexity scoring, and pagination caps at parse time—unbounded nesting or aliased duplicate fields let a single request fan out into a denial-of-service against your stores.
• Partial-failure semantics: A resolver error nulls its field and propagates up to the nearest nullable parent, so one failing downstream can blank a whole subtree; return errors alongside partial data and design non-null fields deliberately.
• DataLoader scoping: Instantiate loaders per-request, never as singletons, or stale cached entities leak across users; batching only works within a single tick, so avoid awaiting sequentially in resolvers.
• Schema evolution: Add fields freely, but never repurpose or remove a field without the @deprecated directive and usage telemetry confirming no clients still select it—there is no URL version to isolate breaks.
• Caching: POST-based queries bypass HTTP/CDN caching; adopt persisted (allow-listed) queries to enable GET caching and block arbitrary ad-hoc queries in production.
• Observability: Instrument per-resolver latency, error rate, and field-level selection counts; without them, a slow nested field or an unused-but-expensive resolver is invisible in aggregate endpoint metrics.