luokai0 GraphQL Schema (Deep Workflow)
GraphQL concentrates complexity on the server: resolver graphs, N+1 fetches, schema evolution, and field-level authorization.
When to Offer This Workflow
Trigger conditions:
- Designing a new GraphQL API or federated subgraph
- Latency or complexity incidents from client queries
- Need for safe schema deprecation and versioning
Initial offer:
Use six stages: (1) domain modeling, (2) operations surface, (3) performance patterns, (4) errors & partial results, (5) security & authz, (6) versioning & evolution). Confirm client patterns (Apollo, Relay) and gateway (if any).
Stage 1: Domain Modeling
Goal: Types reflect domain concepts; avoid dumping everything on Query; use input objects for mutations with validation.
Stage 2: Operations Surface
Goal: Queries for reads; mutations for writes; subscriptions only when justified (scaling and operational cost).
Pagination
- Prefer cursor-based connections for large lists (Relay-style edges/nodes)
Stage 3: Performance Patterns
Goal: DataLoader or batching for N+1; query complexity/depth/cost limits; optional persisted queries for public APIs.
Stage 4: Errors & Partial Results
Goal: Document semantics of errors alongside partial data; map domain failures to structured extensions.
Stage 5: Security & Authz
Goal: Enforce authorization at field/object level—not only at the top resolver.
Stage 6: Versioning & Evolution
Goal: Prefer additive changes; @deprecated with migration window; in federation, clear ownership of types and entities.
Final Review Checklist
- Schema reflects domain and operations
- Pagination and mutations idiomatic
- Batching and complexity limits in place
- Error behavior documented for clients
- Field-level authz enforced
- Deprecation policy defined
Tips for Effective Guidance
- N+1 is the default failure mode—plan batching early.
- Pair with rest-best-practices when REST and GraphQL coexist at the edge.
Handling Deviations
- Public APIs: consider persisted queries or allowlists to limit abusive queries.