distributed-tracing

Distributed tracing tracks a single request across microservices to pinpoint performance bottlenecks. By instrumenting applications with OpenTelemetry, it visualizes end-to-end request flows to identify exactly which service or database query causes delays.

Context propagation for distributed tracing works by passing trace context via HTTP headers across your Python, Node.js, and Go services. This OpenTelemetry mechanism links individual service operations into a unified request flow for visualization.

Setting up Jaeger and Tempo for microservice observability involves configuring your applications to export OpenTelemetry traces to these backends. This setup collects and visualizes request flows, enabling deep performance bottleneck identification.

Yes, you can use OpenTelemetry to analyze error propagation in microservices. Distributed tracing captures how errors occur and spread across service boundaries, allowing you to understand exactly where failures originate within the request flow.

You need distributed tracing for e-commerce application debugging when users report slow loading times and you must identify the specific lagging API call. It provides deep visibility into request paths across multiple microservices to resolve such performance issues.