distributed-tracing

You implement distributed tracing by instrumenting microservices with OpenTelemetry to track request paths and measure latency. This provides end-to-end visibility to visualize inter-service calls and identify performance bottlenecks in distributed architectures.

Context propagation in distributed tracing passes trace context across service boundaries to maintain request continuity. You need it when debugging inter-service calls or identifying failure propagation, ensuring spans connect across your microservices architecture.

Yes, OpenTelemetry instrumentation supports Python, Node.js, and Go microservices. The implementation provides code examples for all three languages, including context propagation techniques and deployment guidelines for Jaeger and Tempo backends.

Both Jaeger and Tempo visualize request paths by collecting OpenTelemetry trace data. Jaeger focuses on distributed tracing visualization, while Tempo integrates trace correlation with metrics, letting you choose based on your observability stack preferences.

Configuring Jaeger and Tempo involves deploying the backends and connecting OpenTelemetry instrumentation to export trace data. The process includes deployment guidelines and configuration examples to verify end-to-end visibility across your microservices.

Failure propagation goes undetected without distributed tracing because individual service logs lack cross-service request context. Implementing tracing visualizes the full request path, exposing how failures cascade through inter-service calls in distributed architectures.