Page 5 - GraphQl

Untested GraphQL resolvers are a ticking time bomb: schema changes silently break frontend contracts, and authentication bugs stay hidden until production. This article shows how to test Magento GraphQL on three levels, from unit tests for resolver logic to real HTTP requests against the endpoint.

Apollo Client automatically normalizes GraphQL responses into a flat object graph, but without clean type policies and thoughtful cache updates, stale views appear quickly. This article shows how InMemoryCache really works, how custom cache keys are defined with keyFields, and how mutations, pagination and debugging play together in practice.

A single GraphQL mutation looks atomic from the outside, but often hides multiple write steps internally, payment capture, inventory reservation, shipment order. Transactional mutations in GraphQL must secure exactly this multi-step nature, using unit of work, idempotency keys and the saga pattern for distributed consistency.

A GraphQL resolver that combines database queries, validation, permission checks and transformation logic in a single class is no longer a resolver, it is an unbounded mini-service. This article shows how to keep resolvers thin, introduce clear layers, and move business logic to where it actually belongs.

A GraphQL API without a complexity limit is an open door for overloading attacks. Depth limits, field-specific costs and complexity budgets are the three layers that stop a single query from occupying the server for seconds, or for minutes, if the schema has enough expensive fields.

Good GraphQL documentation is not a static file, it lives inside the schema itself, complemented by introspection tools, living example queries, deprecation strategies, and a schema registry workflow that makes changes communicable.

In production systems, GraphQL is not a query format, it is an architecture topic. Anyone who writes elegant queries but ignores resolver depth, N+1 problems, complexity limits and caching builds APIs that break under load. These 50 patterns show what actually works in real GraphQL projects.

A GraphQL schema without a review process evolves in an uncontrolled way: breaking changes, inconsistent naming conventions, and missing documentation creep in. This article shows which points should be checked before every merge, manually and automatically with tooling.

Hasura turns an existing PostgreSQL database into a complete GraphQL API within seconds, including filtering, sorting, pagination and relationships, without writing a single resolver. The approach fits certain scenarios extremely well and is completely unsuitable for others. This article shows exactly where that line runs.

API Platform automatically generates GraphQL schemas from PHP entities, which sounds like the simplest possible solution. In practice, though, the limits show up quickly: complex mutations, domain-specific validation, N+1 performance problems, and a lack of control over the generated schema. This article shows when API Platform GraphQL is enough, and when it needs to be extended or replaced.