REST to GraphQL Migration
Introducing GraphQL alongside your REST API rather than instead of it, with query scope limits and resolver performance handled before clients arrive.
GraphQL solves over-fetching and introduces new problems
The case for GraphQL is real. Mobile clients stop making six calls to render one screen, front-end teams stop waiting for custom endpoints, and the schema becomes documentation that cannot go stale. Those are genuine wins.
What comes with it is a different failure surface. A single query can request arbitrary depth, which is a denial-of-service vector unless bounded. Naive resolvers turn one query into hundreds of database round trips. And unlike REST, you cannot see from an access log what a request actually scope.
So we introduce GraphQL alongside REST, with depth limiting, scope analysis, dataloader batching and per-operation tracing from the first schema rather than after the first outage.
Teams that start here often pair it with API integration, web app development and mobile app development.
REST tells you what a request scope from the URL. GraphQL does not, which is why scope analysis and tracing have to exist before the first client ships.
Where GraphQL adoptions go wrong
Four problems that appear once real clients start querying.
A list query resolves each item separately, turning one request into hundreds of database calls under load.
No depth or complexity limit, so a deeply nested query becomes an availability incident from a single client.
Types map one to one onto tables, so the API exposes storage decisions and becomes hard to evolve.
Everything is one POST endpoint, so monitoring cannot tell which query is slow or expensive.
What a GraphQL migration covers
A schema clients can rely on, with the operational safety it needs.
Types modelled on domain concepts rather than tables, with nullability and pagination decided deliberately.
GraphQL runs alongside REST, often resolving through existing services, so nothing has to be rewritten to start.
Per-request batching and caching so list queries make one database round trip per relationship rather than one per item.
Depth limiting, complexity scoring and persisted queries, so no single request can threaten availability.
Tracing and metrics by operation name, so slow and expensive queries are identifiable in production.
Front-end and mobile clients moved query by query, with REST retired only once traffic shows nothing calls it.
From schema design to client migration
GraphQL added alongside REST, never instead of it.
Existing endpoints, client call patterns and the screens that currently need multiple round trips are catalogued.
Domain-modelled types, pagination and error semantics agreed with client teams before implementation.
GraphQL layer deployed alongside REST with batching, depth limits and tracing from the first resolver.
Screens moved one at a time, measuring round trips and payload size against the REST baseline.
Endpoints retired only when traffic confirms no client depends on them.
The stack behind our GraphQL layers
Schema-first, with performance guarded from day one.
Schema-first implementations with typed resolvers generated from the schema.
Batching and caching per request, because the N+1 problem is the default without them.
Bounded scope per request, so one client cannot take the API down.
Metrics and traces by operation name rather than by endpoint.
What this looks like in production
Six requests per screen down to one
A marketplace mobile app made six REST calls to render its main listing screen. On poor connections the screen took several seconds to populate and partial failures produced half-rendered states that were difficult to reason about.
We introduced GraphQL alongside the existing API, resolving through the same services. The listing screen became a single query. The first load test then immediately exposed an N+1: one query was producing four hundred database calls.
Dataloader batching brought that to nine. Depth limiting and complexity scoring went in before any client shipped, which meant the migration never produced an availability incident.
GraphQL introduced without a rewrite
The schema goes in alongside REST and resolves through your existing services, so adoption is incremental and REST retires only when nothing calls it.
We move one slice at a time behind a router, with both systems live, so every step is small and every step is reversible.
A working demo and a written note every Friday covering what shipped, what slipped and what it means for the date. No status theatre.
Nothing goes live in one jump. We run in parallel or behind a flag until the numbers say it is safe to switch over.
