GraphQL Development Service
Senior engineers building GraphQL APIs with proper N+1 prevention, schema design, federation, and end-to-end type safety.
Why Most GraphQL Apps Have N+1 Queries in Production
GraphQL solves real problems: the over-fetching and under-fetching of REST, the awkward versioning, the multi-round-trip-per-screen design that mobile teams hate. But shipping GraphQL well requires more discipline than shipping REST well. The default resolver pattern produces N+1 queries — for every parent, run a child query — and naive teams ship that to production and blame the database.
DataLoader is the answer but the standard answer is incomplete. It batches one query but does not cache across requests, so a federated graph still re-queries. Persisted queries help with cost but require a workflow most teams skip. Schema design matters more in GraphQL than REST because every field becomes a potential expensive call. A bad schema design is permanent — your clients depend on the shapes you ship.
Federation is the big-architecture decision. Apollo Federation, GraphQL Mesh, or schema stitching — each has trade-offs. A federated graph lets multiple teams contribute subgraphs but adds operational complexity that you may not need. We have seen teams adopt federation for two services and regret it within a quarter.
Authorization in GraphQL is harder than REST. Field-level permissions, resolver-level checks, query depth limits, query complexity limits — these are non-negotiable for production graphs. A graph without query-complexity limits is a denial-of-service waiting to happen the first time someone asks for `users { posts { comments { user { posts ... } } } }`.
AsyncForge has senior GraphQL engineers shipping production graphs. Submit schema design, resolver implementations, federation setup, N+1 audits, or a full GraphQL backend. Light 4 days, Standard 48 hours, Pro 1 day.
What You Get
Schema-first design
Schemas designed with intent: nullable for genuinely optional fields, non-null for guarantees. Naming conventions consistent. Pagination patterns (Relay-style cursors) applied uniformly.
DataLoader batching
Every resolver that loads a related entity uses DataLoader. Cache scoping per-request to avoid leaks. Documented patterns the team can extend.
Federation (Apollo/Mesh)
Federated supergraphs with proper entity ownership, subgraph routing, and CI-validated schema composition. Or schema stitching when federation overhead is unjustified.
Query complexity limits
Per-field cost analysis, max depth, max breadth, query timeouts. Persisted queries for production clients.
End-to-end typing
Codegen from schema → TypeScript types on the client (urql/Apollo/GraphQL Code Generator) and Python/Go/Rust types on the server.
Subscriptions
WebSocket subscriptions for real-time data. PubSub backed by Redis or NATS, scoped channels, proper cleanup on disconnect.
Technologies We Use
How It Works With AsyncForge
Subscribe
Project ready.
Submit GraphQL work
Schemas, resolvers, federation, audits.
We deliver
Tested, performance-checked, documented.
Iterate
Unlimited revisions.
Frequently Asked Questions
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