CI/CD Pipeline Design for Web Applications

Continuous Integration (CI) automatically builds and tests every code change. Continuous Deployment (CD) automatically deploys tested changes to production. Together, they enable multiple daily releases with confidence.

GitHub Actions is the most popular CI/CD platform for web applications. Define workflows in YAML files that trigger on push, pull request, schedule, or manual dispatch. The free tier covers most small-to-medium projects.

CI pipeline stages: install dependencies, lint code (ESLint, Prettier), type check (TypeScript), run unit tests, run integration tests, build the application, and optionally run E2E tests.

Fast feedback is critical. CI should complete in under 10 minutes. Parallelize independent steps (linting and type checking run simultaneously), cache dependencies (node_modules), and run only relevant tests for changed files.

Deployment strategies: direct deployment (replace current version), blue-green (maintain two environments, switch traffic), canary (route small percentage to new version), and rolling (gradually replace instances).

Environment strategy: development (local), staging (pre-production verification), and production. Staging should mirror production configuration. Use environment variables for configuration differences.

Preview deployments create unique URLs for each pull request (Vercel, Netlify, Cloudflare Pages). Reviewers can test changes in a production-like environment before merging. This catches visual and functional issues early.

Database migrations in CI/CD: run migrations automatically during deployment. Ensure migrations are backward-compatible (old code must work with new schema during rolling deployments). Test migrations on staging first.

Secrets management: store API keys and credentials in CI/CD secret storage, not in code repositories. Rotate secrets regularly. Use environment-specific secrets to prevent production credentials in development.

Post-deployment monitoring: verify the deployment with health checks, monitor error rates for spikes, track performance metrics for regressions, and have rollback procedures ready. Automate rollback if error rates exceed thresholds.