distributed-tracing-setup

Distributed tracing captures request flows across service boundaries. Select a backend (Jaeger, Zipkin, or Datadog), instrument your services to emit traces, configure trace propagation, and deploy collectors to aggregate and visualize end-to-end request paths for performance debugging.

All three backends collect and visualize distributed traces. Jaeger and Zipkin are open-source with self-hosted deployment; Datadog is a managed SaaS platform. Choose based on infrastructure preference, operational overhead, and integration needs with your existing observability stack.

Yes, distributed tracing requires instrumenting services to emit trace data. Each service must report spans with trace context headers so the tracing backend can reconstruct request flows. Without instrumentation, visibility across service boundaries is lost.

After instrumentation and collector deployment, generate a request through your services and verify it appears in your tracing UI with all expected spans connected. Check trace context propagation between services and confirm sampling settings allow the trace to be captured and stored.

Yes, distributed tracing works across development, staging, and production. Configure sampling rates to balance visibility and overhead—aggressive sampling in development, conservative in production—and use the same backend or separate instances per environment.

Trace sampling controls what percentage of requests are captured and stored to reduce storage and processing costs. Configure sampling based on traffic volume and retention needs; sampling decisions propagate via trace context so all services honor the same trace.