Part 7: Testing, Performance, and Production Deployment

The Production Deployment That Went Wrong

My first production deployment: pushed code at 9 AM on a Monday. By 9:30 AM, customer support was flooded with complaints. The API was timing out.

The problem? I'd never tested with production data volumes. My local database had 100 users. Production had 500,000. A query that took 10ms locally took 30 seconds in production—no indexes on the database tables.

Emergency rollback. Spent the day adding indexes and load testing. Redeployed at 5 PM.

Lesson: Testing with realistic data and load conditions isn't optional—it's survival.

This article covers the testing and deployment practices that would have saved me that day.

Testing Strategy

Testing Pyramid

        /\
       /E2E\         10% - End-to-end tests
      /______\
     /  API   \      20% - API/Integration tests
    /__________\
   /    Unit     \   70% - Unit tests
  /________________\

Unit Testing

Test business logic in isolation.

Integration Testing

Test API endpoints with real database (using Testcontainers).

Load Testing

Test API performance under load using k6.

Run load tests:

Performance Optimization

Database Indexing

Query Optimization

Caching

Production Deployment

Dockerfile

Docker Compose

Health Checks

CI/CD Pipeline (GitHub Actions)

Monitoring

Key Takeaways

  1. Test pyramid: 70% unit, 20% integration, 10% E2E

  2. Load test with realistic data volumes

  3. Add database indexes for frequently queried fields

  4. Cache frequently accessed data (Redis)

  5. Use connection pooling for databases

  6. Health checks for liveness and readiness

  7. Docker for consistent deployments

  8. CI/CD for automated testing and deployment

  9. Monitor performance metrics (Prometheus)

  10. Zero-downtime deployments with rolling updates

Series complete! You now have everything needed to build production-ready REST APIs.


Testing and monitoring aren't afterthoughts—they're what separate hobby projects from production systems.

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