0152-python-observability

What problem does it solve?

It solves the difficulty of understanding what happened in production by giving you structured logs, measurable metrics, and distributed tracing so you can quickly answer what failed, where it occurred, and why.

Core Features & Use Cases

• Structured logging with consistent fields: Emit JSON logs (via structlog) that are easy to query, filter, and correlate across services.
• Metrics for the four golden signals: Capture latency, traffic, errors, and saturation using Prometheus primitives like counters, histograms, and gauges.
• Distributed tracing with correlation IDs: Propagate a request correlation ID through logs and traces to follow an execution path end-to-end.
• Use cases: Add observability while building or debugging FastAPI services, diagnosing slow endpoints, tracking error rates by operation, and creating dashboards for service health.

Quick Start

Instrument your Python service by enabling structured JSON logging with structlog, adding Prometheus metrics for request latency/errors, and propagating an X-Correlation-ID header through your request middleware.