python-observabilitylisted

# 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. ## 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. ```python import logging import structlog def configure_logging(log_level: str = "INFO") -> None: """Configure structured logging for the application.""" structlog.configure( processors=[ structlog.contextvars.merge_contextvars, structlog.processo