python-observability

# Python Observability Instrument Python applications with structured logs, metrics, and traces. When something breaks in production, you need to answer "what, where, and why" without deploying new code. ## When to Use This Skill - Adding structured logging to applications - Implementing metrics collection with Prometheus - Setting up distributed tracing across services - Propagating correlation IDs through request chains - Debugging production issues - Building observability dashboards ## Core Concepts ### 1. Structured Logging Emit logs as JSON with consistent fields for production environments. Machine-readable logs enable powerful queries and alerts. For local development, consider human-readable formats. ### 2. The Four Golden Signals Track latency, traffic, errors, and saturation for every service boundary. ### 3. Correlation IDs Thread a unique ID through all logs and spans for a single request, enabling end-to-end tracing. ### 4. Bounded Cardinality Keep metric label values bounded. Unbounded labels (like user IDs) explode storage costs. ## Quick Start ```python import structlog structlog.configure( processors=[ structlog.processors.TimeStamper(fmt="iso"), structlog.processors.JSONRenderer(), ], ) logger = structlog.get_logger() logger.info("Request processed", user_id="123", duration_ms=45) ``` ## Fundamental Patterns ### Pattern 1: Structured Logging with Structlog Configure structlog for JSON output with consistent fields....