GraphQL: Flexibility with Responsibility

GraphQL gives clients the power to request exactly the data they need — no over-fetching, no under-fetching. This flexibility comes with responsibilities for the API designer: schema design, performance guardrails, security constraints, and complexity management. At Nexis Limited, we use GraphQL for client-facing APIs where query flexibility significantly improves the developer experience.

Schema Design Principles

Think in Graphs, Not Tables

The most common mistake is mapping database tables directly to GraphQL types. Instead, design your schema around how clients consume data. A User type might combine data from users, profiles, and preferences tables into a cohesive entity that makes sense from the client's perspective.

Use Meaningful Types

  • Create specific types (ShippingAddress vs BillingAddress) rather than generic ones (Address with a type field).
  • Use enums for fields with a fixed set of values.
  • Use interfaces and unions for polymorphic types.
  • Use custom scalars for domain-specific types (DateTime, URL, Email).

Connections and Pagination

Use the Relay-style connection pattern for paginated lists — edges, nodes, and pageInfo. This provides consistent pagination across all list fields and supports cursor-based pagination for reliable pagination through changing data sets.

Performance Optimization

DataLoader Pattern

The N+1 query problem is GraphQL's most common performance issue. When resolving a list of orders with their associated customers, a naive implementation makes one database query per order to fetch the customer. DataLoader batches these requests — collecting all customer IDs in a single tick and making one batch query. This is essential for any production GraphQL server.

Query Complexity Limits

Clients can construct arbitrarily complex queries. Without limits, a single query could fetch millions of records. Implement query complexity analysis that assigns a cost to each field and rejects queries exceeding a complexity threshold. This prevents denial-of-service through expensive queries.

Query Depth Limits

Prevent deeply nested queries (user → posts → comments → author → posts → ...) that create excessive joins. Set a maximum query depth (typically 7-10 levels) and reject deeper queries.

Security Considerations

  • Authentication: Validate JWT or session tokens in middleware before the GraphQL resolver layer.
  • Authorization: Implement field-level authorization. Not all fields of a type should be visible to all users.
  • Rate limiting: Rate limit by query complexity, not just request count.
  • Introspection: Disable schema introspection in production to prevent attackers from mapping your API surface.
  • Input validation: Validate all mutation inputs using custom scalars and input validation libraries.

Caching GraphQL

GraphQL's flexible queries make HTTP caching difficult because each query can be unique. Approaches include response caching with normalized cache keys, persisted queries (allowlisted query strings that enable CDN caching), and client-side normalized caches (Apollo Client, urql).

When to Choose GraphQL vs REST

  • Choose GraphQL when clients need flexible data fetching, you have multiple clients with different data needs, or your data model is highly relational.
  • Choose REST when simplicity is a priority, caching is critical, or you have a simple CRUD API with predictable access patterns.

Conclusion

GraphQL is a powerful tool for building flexible APIs, but production-ready GraphQL requires careful schema design, performance optimization, and security constraints. Treat your GraphQL schema as a product — design it for your consumers, optimize for common queries, and protect against abuse.

Building a GraphQL API? Our team designs and implements production GraphQL services.