distributed-tracing

End-to-end tracing tracks microservice calls to diagnose latency by instrumenting applications with OpenTelemetry, visualizing request flows with Jaeger, and identifying bottlenecks across service boundaries. This reveals exact failure points in distributed architectures.

Trace context propagation passes tracing metadata across service boundaries so requests are tracked accurately end-to-end. It ensures spans link together correctly, allowing Jaeger or Tempo to reconstruct the complete request flow through your microservices.

Yes, Jaeger and Tempo backends work with OpenTelemetry instrumentation in Kubernetes deployments. They collect and visualize traces from distributed applications, helping observe request flows and identify performance bottlenecks in production or staging environments.

Service dependency analysis uses distributed tracing to map request flows across microservices. By instrumenting with OpenTelemetry and visualizing traces in Jaeger, you identify which services contribute to latency and where failure points exist in the dependency chain.

Sampling strategies manage trace volume in serverless components and distributed applications. They determine which requests get traced, balancing observability needs against backend storage costs while maintaining enough data to diagnose latency and failure points effectively.