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

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.

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.processors.JSONRenderer(),
        ],
        wrapper_class=structlog.make_filtering_bound_logger(
            getattr(logging, log_level.upper())
        ),
        context_class=dict,
        logger_factory=structlog.PrintLoggerFactory(),
        cache_logger_on_first_use=True,
    )

# Initialize at application startup
configure_logging("INFO")
logger = structlog.get_logger()

Pattern 2: Consistent Log Fields

Every log entry should include standard fields for filtering and correlation.

import structlog
from contextvars import ContextVar

# Store correlation ID in context
correlation_id: ContextVar[str] = ContextVar("correlation_id", default="")

logger = structlog.get_logger()

def process_request(request: Request) -> Response:
    """Process request with structured logging."""
    logger.info(
        "Request received",
        correlation_id=correlation_id.get(),
        method=request.method,
        path=request.path,
        user_id=request.user_id,
    )

    try:
        result = handle_request(request)
        logger.info(
            "Request completed",
            correlation_id=correlation_id.get(),
            status_code=200,
            duration_ms=elapsed,
        )
        return result
    except Exception as e:
        logger.error(
            "Request failed",
            correlation_id=correlation_id.get(),
            error_type=type(e).__name__,
            error_message=str(e),
        )
        raise

Pattern 3: Semantic Log Levels

Use log levels consistently across the application.

# DEBUG: Detailed internal information
logger.debug("Cache lookup", key=cache_key, hit=cache_hit)

# INFO: Normal operational events
logger.info("Order created", order_id=order.id, total=order.total)

# WARNING: Abnormal but handled situations
logger.warning(
    "Rate limit approaching",
    current_rate=950,
    limit=1000,
    reset_seconds=30,
)

# ERROR: Failures requiring investigation
logger.error(
    "Payment processing failed",
    order_id=order.id,
    error=str(e),
    payment_provider="stripe",
)

…