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API development

Testing & monitoring

Confidence before release and visibility after it

API testing

Testing pyramid

End-to-end tests

Test complete user workflows across multiple APIs and services. Fewer but critical scenarios. Use Postman, Playwright, or Cypress.

Integration tests

Test API endpoints with real dependencies (database, external APIs). Use TestServer or Docker containers for isolated test environments.

Unit tests

Test individual components (controllers, services, validators) in isolation. Use mocking for dependencies. Aim for 70-80% code coverage.

Additional testing types

  • Contract testing: Consumer-driven contracts (Pact) and conformance to the OpenAPI spec (Dredd, Schemathesis)
  • Load testing: Measure performance under stress (k6, JMeter, Azure Load Testing)
  • Security testing: OWASP ZAP, penetration testing, vulnerability scanning
  • Chaos testing: Test resilience by injecting faults

Monitoring & observability

When something slows down at 3 a.m., a correlated trace shows which dependency is to blame in seconds instead of hours.

One request, end to end

Transaction trace4bf92f35…

  1. POST /api/v1/orders
    214 ms
  2. Validate JWT
  3. SQL · INSERT Orders
  4. Redis · SET order:7731
  5. Dataverse · Create salesorder
  6. Service Bus · order-created
{ "level": "Information", "operationId": "4bf92f35…", "orderId": 7731, "elapsedMs": 214 }
/health/liveHealthy
/health/readyHealthy

Illustrative example

Distributed tracing follows a single request through every dependency. Inject a fault and the trace shows the failed call and the retry, while the readiness probe reports degraded instead of down.

Application Insights

Comprehensive monitoring for APIs with Application Insights:

  • Request tracking: Response times, success rates, failure analysis
  • Dependency monitoring: Track calls to databases, external APIs, queues
  • Distributed tracing: Correlation across microservices
  • Custom events: Track business metrics and KPIs
  • Live metrics: Real-time monitoring dashboard
  • Alerts: Proactive notifications for anomalies

Logging best practices

  • Structured logging: JSON format with consistent schema
  • Correlation IDs: Track requests across services
  • Log levels: Trace, Debug, Information, Warning, Error, Critical
  • Sensitive data: Never log passwords, tokens, PII
  • Centralized logging: Azure Monitor, ELK stack, Datadog

Health checks

Implement health check endpoints for orchestrators and load balancers:

  • /health/live: Liveness probe (is process running?)
  • /health/ready: Readiness probe (can accept traffic?)
  • Dependency checks: Validate database, cache, queue connectivity
  • Response format: Return status code and optional details JSON
PreviousDocumentation & versioning

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