python-observability

Add structured logging and tracing to a Python microservice by configuring JSON logs via structlog and OpenTelemetry traces. This provides end-to-end visibility into request latency, traffic, and errors across distributed services.

Monitor Python application metrics with Prometheus by integrating metric collection to track latency, throughput, and error rates. This approach includes guards against unbounded label cardinality to prevent metric storage issues.

Correlation IDs work with structured logs by propagating unique identifiers from the start of a request to its end. This OpenTelemetry tracing context binds JSON logs together to trace requests across microservices.

You can use OpenTelemetry and Prometheus together in Python to achieve full observability. OpenTelemetry handles distributed tracing while Prometheus captures metrics, giving you correlated logs, traces, and monitoring data.

Prometheus metrics cause high memory usage in Python when unbounded label cardinality occurs. Guard against this by limiting label values, preventing the metric storage from expanding uncontrollably and saturating system resources.

You need distributed tracing patterns for Python production services when you require end-to-end visibility across microservices. Implement OpenTelemetry tracing to identify bottlenecks and root-cause production issues quickly.