Monitoring tells you that something is wrong. Observability tells you what's happening and why. In distributed systems, observability is the difference between finding the root cause in minutes or spending hours investigating.
The three pillars of observability
📊 Metrics
Numerical data aggregated over time. Latency, requests/second, error rate, CPU usage.
📝 Logs
Detailed records of events. What happened, when and with what context.
🔗 Traces
The complete path of a request across multiple services.
Monitoring vs. Observability
Traditional monitoring is based on known unknowns: you configure alerts for what you expect to fail. Observability prepares you for unknown unknowns: situations you didn't anticipate.
Key practices
- Structured logging: logs in JSON format with consistent fields.
- Distributed tracing: correlate requests across services.
- Golden signals: latency, traffic, errors and saturation.
- Meaningful alerts: alert on symptoms, not causes.
- Accessible dashboards: the team should be able to quickly understand system status.
Useful tools
The observability ecosystem includes tools like Prometheus and Grafana for metrics, ELK Stack or Loki for logs, and Jaeger or Zipkin for tracing. OpenTelemetry is becoming the standard for unified instrumentation.
Observability is not a product you buy: it's a capability you build. It starts with instrumenting your code and ends with a team that knows how to use the data to make decisions.
