distributed-tracing

Distributed tracing with OpenTelemetry instruments your services to track requests end-to-end, creating traces and spans that show which service or component causes latency. Use Jaeger or Tempo to visualize these traces and identify slow operations across your microservices architecture.

Jaeger provides real-time trace collection and a UI for immediate query and analysis. Tempo is designed for long-term trace storage and cost efficiency at scale. Both work with OpenTelemetry; choose Jaeger for operational debugging and Tempo for archived trace retention.

OpenTelemetry SDKs and language-specific instrumentation packages (like opentelemetry-instrumentation-flask for Python) capture traces and spans from your applications. Configure exporters to send data to Jaeger or Tempo, then enable auto-instrumentation to trace requests without modifying application code.

OpenTelemetry handles context propagation automatically via HTTP headers when properly configured. Ensure your services use OpenTelemetry middleware and instrumentation; context flows through request/response cycles across Flask, Express, and other frameworks without manual intervention.

Yes. Distributed tracing captures error context, logs, and attributes at each span, showing exactly where failures occur and how errors propagate through your service chain. Jaeger and Tempo let you query and visualize these error traces to pinpoint root causes.

You need OpenTelemetry SDK for your language, exporter libraries (Jaeger or Tempo), instrumentation packages for your frameworks (Flask, Express, etc.), and deployed Jaeger or Tempo backend. Basic understanding of microservice architecture and HTTP context propagation is helpful.