GraphQL API를 위한 지속적 통합

Manual testing before each deploy is slow and error-prone. Continuous Integration runs your build and tests automatically on every push, catching regressions before they reach production.

A typical pipeline runs ordered stages:

• Build the application
• Test unit and integration suites
• Schema check for breaking changes
• Package an artifact or image

Define a workflow that triggers on push and pull requests, then runs your Gradle build.

Your unit and integration tests run automatically as part of gradle build. The pipeline fails if any test fails, blocking the merge.

To check for breaking changes, first generate the current SDL from your running app or build, producing a schema.graphql artifact for comparison.

A breaking change (removing a field, changing a type) can break existing clients. Tools like the Rover CLI compare the new schema against the deployed one and fail if a change breaks compatibility.

Schema linters enforce naming and style conventions (PascalCase types, camelCase fields), keeping a large schema consistent across many contributors.

Re-downloading dependencies on every run is wasteful. Cache the Gradle directory so CI runs finish in seconds, not minutes.

Require the CI checks to pass before a pull request can merge. This makes green tests and a clean schema check non-negotiable for every change.

Once CI is green, a continuous delivery step can build a Docker image and deploy it. Schema checks in CI give you confidence to ship frequently and safely.

Build a reliable pipeline:

• Run tests and schema checks on every PR
• Block merges on red checks
• Cache dependencies for fast feedback
• Keep secrets in CI's secret store, never in code

You automated quality with CI:

• Pipelines build, test, and check the schema on every push
• GitHub Actions runs Gradle build and tests
• Export the schema and detect breaking changes
• Lint, cache, and protect branches with required checks

A solid CI pipeline keeps your GraphQL API safe to ship continuously.