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.

```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.processors.add_log_level,
            structlog.processors.TimeStamper(fmt="iso"),
            structlog.processors.StackInfoRenderer(),
            structlog.processors.format_exc_info,
            structlog.processor