distributed-tracing

You can trace requests across microservices by instrumenting your application with OpenTelemetry and configuring Jaeger or Tempo collectors to visualize request flows and pinpoint slow operations.

Distributed tracing is a method to monitor the journey of requests across multiple microservices. You need it to debug complex request paths, analyze error propagation, and enhance system observability.

To instrument a Python Flask application for distributed tracing, you apply OpenTelemetry libraries to generate traces and configure the exporters to send the telemetry data to your Jaeger or Tempo backend.

Yes, you can use Jaeger and Tempo to monitor request flows. They act as distributed tracing backends that collect, store, and visualize the telemetry data generated by OpenTelemetry instrumentation.

You need OpenTelemetry to analyze error propagation because it provides the standard instrumentation layer that captures context across service boundaries, allowing Jaeger or Tempo to trace how errors spread.

Yes, distributed tracing works for identifying slow database queries in e-commerce applications by tracking the end-to-end request path and highlighting the specific service or database operation causing delays.