distributed-tracing

Distributed tracing tracks end-to-end request flows across microservices to identify performance bottlenecks. Using OpenTelemetry instrumentation with Jaeger or Tempo, you visualize spans and dependencies to pinpoint latency sources in complex architectures.

Context propagation in distributed tracing passes trace context across service boundaries to link spans into a cohesive request flow. It ensures end-to-end visibility when debugging latency issues or tracing error propagation through complex service dependencies.

Instrumenting Python, Node.js, and Go with OpenTelemetry involves adding tracing libraries to capture spans and propagate context. This enables services to export trace data to Jaeger or Tempo for visualizing end-to-end request flows and service dependencies.

Yes, distributed tracing supports both Jaeger and Tempo for visualizing and analyzing traces. It provides deployment and observability best practices for configuring either backend to store and query trace data from OpenTelemetry-instrumented microservices.

Tracing error propagation across distributed microservices requires end-to-end instrumentation with OpenTelemetry to capture error states within spans. Visualizing these traces in Jaeger or Tempo reveals how failures cascade through service dependencies.