distributed-tracing

Distributed tracing for microservices requires instrumenting Python, Node.js, or Go applications with OpenTelemetry to propagate trace context. This maps end-to-end request paths and reveals latency across service boundaries when exporting to Jaeger or Tempo.

Distributed tracing is used to visualize request flow and analyze dependencies across microservices. It helps pinpoint latency bottlenecks and failure points by mapping how requests traverse frontend, gateways, services, and databases.

Jaeger supports operational debugging by visualizing request paths across service boundaries. You can analyze latency issues and track error propagation to identify exact failure points within your microservices architecture.

Yes, distributed tracing works with both Jaeger and Tempo backends. OpenTelemetry exporters can be configured to send trace data to either system, satisfying setup and analysis needs for visualizing request flow and troubleshooting.

Adding OpenTelemetry instrumentation involves integrating SDKs into Python, Node.js, and Go services to capture trace context. This enables end-to-end visibility of request flow across distributed service boundaries.

Sampling strategies for distributed tracing determine which traces get exported to backends like Jaeger or Tempo. Configuring OpenTelemetry sampling helps manage overhead while retaining enough data for effective latency investigation and troubleshooting.