python-observability

To instrument FastAPI applications, apply structlog for structured JSON logging and Prometheus primitives to track HTTP request latency, traffic, and errors with bounded label cardinality. This setup ensures machine-readable logs and request-level metrics for production observability.

Correlation ID propagation uses contextvars and middleware to link logs, metrics, and distributed traces end-to-end across service boundaries. This mechanism passes unique identifiers through request flows and background jobs, ensuring traceability across microservices.

Exporting OpenTelemetry spans from Python backend services is best achieved by configuring OTLP-compatible trace export with middleware. This approach propagates spans across request flows and background jobs, enabling distributed tracing to diagnose production incidents without redeploying code.

Adding observability to Python microservices requires applying instrumentation patterns like timing context managers and request tracking decorators to your codebase. These reusable patterns add structured logs, metrics, and traces so incidents can be diagnosed without needing to recompile or redeploy.

Yes, the observability instrumentation applies to Flask applications alongside FastAPI and backend services. It adds structured logging, Prometheus request metrics, and OpenTelemetry spans to meet JSON structured logs and OTLP-compatible trace export requirements.

Bounding metric label cardinality is required when tracking Prometheus request metrics to prevent uncontrolled growth of time series data. The instrumentation uses bounded label cardinality to safely track latency, traffic, errors, and saturation without overwhelming storage.