distributed-tracing

Debug microservice latency issues by implementing distributed tracing with Jaeger or Tempo to visualize request flows and pinpoint slow operations. This approach tracks requests across service boundaries to identify exactly which service causes performance bottlenecks.

Distributed tracing is a method to monitor request flows across microservices, needed when debugging complex distributed systems. It provides visibility into how requests traverse multiple services, helping analyze error propagation and pinpoint latency issues.

Set up OpenTelemetry instrumentation in your Python Flask application by configuring the SDK to export trace data to a Jaeger or Tempo backend. This implementation requires proper backend setup to collect and visualize the generated trace data.

Jaeger and Tempo are both backends used for storing and querying distributed tracing data collected via OpenTelemetry. You select one based on your observability stack preferences to visualize request flows and analyze error propagation.

You need OpenTelemetry to standardize application instrumentation, which generates the trace data required to analyze error propagation. This instrumentation captures spans across microservices, enabling backends like Jaeger to visualize how failures cascade.

Distributed tracing for service dependencies requires proper application instrumentation with OpenTelemetry and backend setup for Jaeger or Tempo. Without complete instrumentation across all microservices, trace visibility breaks and request flow analysis becomes incomplete.