distributed-tracing

Distributed tracing tracks request flows across microservices using Jaeger and Tempo to identify latency and failure points. By following a single request as it moves through multiple services, you can pinpoint exactly which operation causes delays.

Distributed tracing provides observability for microservice architectures by visualizing how requests travel across services. You need it when debugging complex system interactions or identifying performance bottlenecks in distributed systems where requests span multiple services.

You implement distributed tracing in Python Flask applications using OpenTelemetry for application instrumentation and Jaeger as the tracing backend. This setup requires context propagation via HTTP headers to maintain trace continuity across service boundaries.

Yes, OpenTelemetry is required for application instrumentation when using Jaeger and Tempo for distributed tracing. It provides the libraries to instrument your code and handles context propagation via HTTP headers across your microservices.

Distributed tracing with Jaeger and Tempo visualizes request flows to identify which service causes delays. You can trace a typical user request to determine if the bottleneck is in the product catalog, checkout API, or payment gateway service.

Context propagation in distributed tracing works by passing trace context via HTTP headers between services. This allows Jaeger and Tempo to stitch together spans from different microservices into a single end-to-end request trace.