distributed-tracing

Distributed tracing helps debug microservice latency by visualizing request flows across services to pinpoint slow components. It traces requests from the frontend through gateways and downstream services to identify exactly where delays occur.

You can set up OpenTelemetry and Jaeger for Python Flask by configuring trace providers, span processors, and context propagation. This implementation captures and exports trace data to visualize microservice request paths.

Yes, this distributed tracing implementation supports both Python and Node.js environments. You can configure trace providers and context propagation across both stacks to achieve comprehensive microservice observability.

Visualizing microservice request flows is best achieved by implementing distributed tracing with Jaeger and OpenTelemetry. This approach maps end-to-end request paths, allowing you to analyze interactions and pinpoint errors across services.

Context propagation is required for microservices tracing because it passes trace context across service boundaries. Without proper span processors and context propagation, distributed traces break and cannot visualize a continuous end-to-end request flow.