Building Observable Systems: Metrics, Logs, and Traces

Observability is more than monitoring—it's understanding system behavior through metrics, logs, and traces. Here's how we built a comprehensive observability stack.

The Three Pillars

**Metrics** provide time-series data about system performance. We use Prometheus to collect metrics from all services, with Grafana dashboards for visualization. Key metrics include request latency (p50, p95, p99), error rates, and resource utilization.

**Logs** capture discrete events and errors. Structured logging with JSON format makes logs searchable and analyzable. We aggregate logs using Loki and index them for fast queries. Every log entry includes trace IDs for correlation.

**Traces** show the journey of requests through distributed systems. OpenTelemetry instruments our services to capture spans representing each operation. Jaeger visualizes traces, helping us identify bottlenecks and failures across microservices.

Implementation Strategy

Start with metrics for high-level health, add logging for debugging, then implement tracing for complex distributed calls. Use consistent naming conventions and tag everything with service names, environments, and versions.

OpenTelemetry Integration

OpenTelemetry provides a vendor-neutral way to collect telemetry data. We instrumented our Node.js and Python services with auto-instrumentation libraries, then added custom spans for business logic.

Alerting Rules

Define alerts on SLIs that matter to users: error rates above 1%, latency p95 above 500ms, or saturation metrics approaching limits. Avoid alerting on symptoms—alert on user impact.

The result: Mean time to detection dropped from 20 minutes to under 2 minutes, and root cause analysis that used to take hours now takes minutes.