distributed-tracing

Distributed tracing provides end-to-end visibility into request flows to diagnose latency and failures across microservices. It uses OpenTelemetry instrumentation to propagate context across service boundaries, with Jaeger or Tempo storing and querying the trace data.

You instrument Python, Node.js, and Go services using OpenTelemetry libraries to generate trace data. This involves adding tracing SDKs to your code, configuring exporters, and ensuring context propagation headers pass across service boundaries.

Yes, distributed tracing works with Kubernetes-deployed architectures by providing specific deployment guidance and best practices. It supports observable microservice setups in Kubernetes to track request flows, identify failures, and map service dependencies.

The best way to deploy Jaeger and Tempo is to install and run them as your trace collection backends. You then instrument your services with OpenTelemetry to send data to these backends, allowing you to query and view traces in the Jaeger UI.

Context propagation in distributed tracing passes trace context headers between services so a single request flow can be reconstructed across boundaries. OpenTelemetry handles this automatically across supported runtimes, allowing Jaeger and Tempo to link spans into continuous traces.

You need distributed tracing instead of standard logging when diagnosing latency, failures, or dependency issues across multiple microservices. It provides end-to-end visibility into request flows that isolated application logs cannot piece together across service boundaries.