graphql-schema-design

Design scalable GraphQL schemas by defining object, interface, union, enum, and input types using SDL patterns. Structure query and mutation fields with nullable/non-null constraints, apply pagination (offset or cursor-based), and use directives for metadata and cross-cutting concerns to maintain API clarity as requirements evolve.

GraphQL pagination supports two patterns: offset-based (skip N, take M records) for simple use cases, and cursor-based for stable, scalable data fetching across API versions. Cursor pagination handles insertions and deletions without page drift, making it preferred for production APIs with frequent data changes.

Custom directives attach metadata and reusable logic to schema fields, arguments, and types without cluttering resolver code. Use directives for authentication checks, field validation, deprecation tracking, caching hints, and schema documentation to keep type definitions clean and maintainable across versions.

Schema evolution strategies include deprecating old fields with `@deprecated` directives, adding new fields alongside old ones, and using input types to bundle related arguments. Avoid removing fields; instead mark them deprecated, giving clients time to migrate before sunsetting in a future version.

Interfaces define a contract—fields all implementing types must provide—enabling shared query patterns. Unions group unrelated types without enforcing shared fields, requiring inline fragments to access type-specific data. Use interfaces for polymorphic types with common behavior; unions for heterogeneous return values.

Custom scalars serialize, deserialize, and validate domain-specific types (Date, UUID, JSON, Email) at the schema boundary. They centralize validation logic, improve type safety in client code generation, and document expected formats, reducing resolver-level validation boilerplate.