python-backend-opentelemetry-instrumentation

OpenTelemetry tracing for Flask is configured by initializing a TracerProvider with Resource attributes and applying FlaskInstrumentor to automatically emit distributed traces and propagate trace context across your Python backend.

Configuring a TracerProvider to export traces to an OTLP endpoint involves setting Resource attributes such as service.name and service.version, then attaching an OTLP exporter to enable distributed tracing across your microservice architecture.

Creating manual spans for custom business logic in Python OpenTelemetry allows you to trace specific operations beyond automatic framework instrumentation, ensuring traceability and correlation for complex logic within FastAPI or Flask services.

Log correlation with OpenTelemetry tracing in Python backends works by injecting trace context into your logs, linking log entries to distributed spans so you can trace requests across microservices and debug operations effectively.

Yes, you can use OpenTelemetry auto-instrumentation for both FastAPI and Flask microservices, applying their respective instrumentors to automatically emit traces and propagate trace context across your distributed backend architecture.