Article 15: Observability in Microservices (Logging, Metrics, Tracing)

1. Objective

Understand how to observe and debug microservices using logs, metrics, and distributed tracing.

2. Key Concepts

• Observability vs Monitoring
• Centralized logging
• Metrics
• Distributed tracing
• Correlation IDs

3. Why Observability Matters

In microservices:

• Failures are distributed
• Issues are hard to reproduce
• One request spans multiple services

4. The Three Pillars of Observability

Logs    -> What happened
Metrics -> How often / how much
Traces  -> Where it happened

5. Centralized Logging

• Use structured logs
• Include traceId and spanId
• Aggregate logs in one place

Example log pattern:

[traceId=abc123] Order created successfully

6. Distributed Tracing (Sleuth + Zipkin)

• Trace a request across services
• Identify latency bottlenecks

High-level flow:

Client -> API Gateway -> Order Service -> Payment Service

7. Metrics Basics

• Request count
• Error rate
• Response time

8. Common Mistakes

• Logging too much or too little
• No correlation IDs
• Ignoring metrics

9. Interview Notes

• Observability helps debug production issues
• Tracing is critical in microservices

10. Summary

Without observability, microservices are impossible to operate.

11. What’s Next

Make services resilient to failures.