Observability in Distributed Systems: Beyond Logging and Metrics

Monitoring tells you when something is wrong. Observability tells you why. In distributed systems with dozens of microservices, third-party dependencies, and millions of concurrent users, the difference between these two capabilities is the difference between a 1-hour incident and a 12-hour incident.

The Three Pillars

Logs: Timestamped, discrete records of events. The most familiar observability signal.

• Use structured logging (JSON) so logs are machine-parseable
• Include correlation IDs in every log line to trace requests across services
• Log at the right level: DEBUG for development, INFO for significant events, ERROR for actionable issues
• Centralise with ELK stack, Splunk, or cloud-native solutions (CloudWatch, Google Cloud Logging)

Metrics: Numerical measurements over time. Aggregatable, time-series data.

• Instrument with the RED method: Rate, Errors, Duration for every service
• Use the USE method: Utilisation, Saturation, Errors for infrastructure
• Prometheus + Grafana is the de facto open-source stack
• Define SLIs (Service Level Indicators) and alert on SLO breaches, not arbitrary thresholds

Traces: End-to-end records of requests as they propagate through services.

• Implement distributed tracing with OpenTelemetry (the emerging standard)
• Every service must propagate trace context headers
• Traces reveal performance bottlenecks and failure points invisible in logs alone
• Jaeger, Zipkin, and Honeycomb are leading trace backends

Alerting Best Practices

Alert on symptoms (user-facing impact), not causes. Noisy, low-fidelity alerts cause alert fatigue—on-call engineers start ignoring alerts when 80% are false positives.

Implement error budgets: if your SLO is 99.9% availability, you have 44 minutes of downtime budget per month. Burn rate alerts notify you when you are consuming that budget too quickly.