python-observability-patterns

Structured logging consolidates Python application telemetry using structlog for logs, Prometheus for metrics, and OpenTelemetry for distributed tracing. This Skill provides production-ready patterns integrating all three across FastAPI and async frameworks, enabling request correlation and context propagation without manual instrumentation overhead.

Yes. OpenTelemetry handles trace collection and context propagation while Prometheus scrapes metrics endpoints. This Skill combines both for Python 3.10+ services, showing how to wire structured logs, metrics, and traces into web services and background tasks with shared correlation IDs.

Request context propagation uses correlation IDs and structured logging middleware. This Skill provides FastAPI middleware and instrumentation patterns that maintain context across API endpoints and asynchronous background jobs, enabling end-to-end request tracing and correlation.

While separate, logging, metrics, and tracing work together in observability. This Skill consolidates patterns for structlog, Prometheus, and OpenTelemetry into a unified approach for Python services, reducing configuration complexity and enabling correlated insights across all three signals.

Prometheus metrics capture request latency and database performance through client instrumentation. This Skill demonstrates metric patterns for profiling requests and DB operations in Python services, providing middleware and instrumentation code ready for production deployment.

Observability patterns must handle both sync and async contexts. This Skill covers structured logging, metrics, and tracing across FastAPI handlers and asynchronous background jobs, ensuring correlation IDs and context propagate correctly in both execution models.