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.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.

python

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.

Level	Purpose	Examples
`DEBUG`	Development diagnostics	Variable values, internal state
`INFO`	Request lifecycle, operations	Request start/end, job completion
`WARNING`	Recoverable anomalies	Retry attempts, fallback used
`ERROR`	Failures needing attention	Exceptions, service unavailable

python

# 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",
)

Never log expected behavior at

ERROR

. A user entering a wrong password is

INFO

, not

ERROR

Pattern 4: Correlation ID Propagation

Generate a unique ID at ingress and thread it through all operations.

python

from contextvars import ContextVar
import uuid
import structlog

correlation_id: ContextVar[str] = ContextVar("correlation_id", default="")

def set_correlation_id(cid: str | None = None) -> str:
    """Set correlation ID for current context."""
    cid = cid or str(uuid.uuid4())
    correlation_id.set(cid)
    structlog.contextvars.bind_contextvars(correlation_id=cid)
    return cid

# FastAPI middleware example
from fastapi import Request

async def correlation_middleware(request: Request, call_next):
    """Middleware to set and propagate correlation ID."""
    # Use incoming header or generate new
    cid = request.headers.get("X-Correlation-ID") or str(uuid.uuid4())
    set_correlation_id(cid)

    response = await call_next(request)
    response.headers["X-Correlation-ID"] = cid
    return response

Propagate to outbound requests:

python

import httpx

async def call_downstream_service(endpoint: str, data: dict) -> dict:
    """Call downstream service with correlation ID."""
    async with httpx.AsyncClient() as client:
        response = await client.post(
            endpoint,
            json=data,
            headers={"X-Correlation-ID": correlation_id.get()},
        )
        return response.json()

Advanced Patterns

Pattern 5: The Four Golden Signals with Prometheus

Track these metrics for every service boundary:

python

from prometheus_client import Counter, Histogram, Gauge

# Latency: How long requests take
REQUEST_LATENCY = Histogram(
    "http_request_duration_seconds",
    "Request latency in seconds",
    ["method", "endpoint", "status"],
    buckets=[0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1, 2.5, 5, 10],
)

# Traffic: Request rate
REQUEST_COUNT = Counter(
    "http_requests_total",
    "Total HTTP requests",
    ["method", "endpoint", "status"],
)

# Errors: Error rate
ERROR_COUNT = Counter(
    "http_errors_total",
    "Total HTTP errors",
    ["method", "endpoint", "error_type"],
)

# Saturation: Resource utilization
DB_POOL_USAGE = Gauge(
    "db_connection_pool_used",
    "Number of database connections in use",
)

Instrument your endpoints:

python

import time
from functools import wraps

def track_request(func):
    """Decorator to track request metrics."""
    @wraps(func)
    async def wrapper(request: Request, *args, **kwargs):
        method = request.method
        endpoint = request.url.path
        start = time.perf_counter()

        try:
            response = await func(request, *args, **kwargs)
            status = str(response.status_code)
            return response
        except Exception as e:
            status = "500"
            ERROR_COUNT.labels(
                method=method,
                endpoint=endpoint,
                error_type=type(e).__name__,
            ).inc()
            raise
        finally:
            duration = time.perf_counter() - start
            REQUEST_COUNT.labels(method=method, endpoint=endpoint, status=status).inc()
            REQUEST_LATENCY.labels(method=method, endpoint=endpoint, status=status).observe(duration)

    return wrapper

Pattern 6: Bounded Cardinality

Avoid labels with unbounded values to prevent metric explosion.

python

# BAD: User ID has potentially millions of values
REQUEST_COUNT.labels(method="GET", user_id=user.id)  # Don't do this!

# GOOD: Bounded values only
REQUEST_COUNT.labels(method="GET", endpoint="/users", status="200")

# If you need per-user metrics, use a different approach:
# - Log the user_id and query logs
# - Use a separate analytics system
# - Bucket users by type/tier
REQUEST_COUNT.labels(
    method="GET",
    endpoint="/users",
    user_tier="premium",  # Bounded set of values
)

Pattern 7: Timed Operations with Context Manager

Create a reusable timing context manager for operations.

python

from contextlib import contextmanager
import time
import structlog

logger = structlog.get_logger()

@contextmanager
def timed_operation(name: str, **extra_fields):
    """Context manager for timing and logging operations."""
    start = time.perf_counter()
    logger.debug("Operation started", operation=name, **extra_fields)

    try:
        yield
    except Exception as e:
        elapsed_ms = (time.perf_counter() - start) * 1000
        logger.error(
            "Operation failed",
            operation=name,
            duration_ms=round(elapsed_ms, 2),
            error=str(e),
            **extra_fields,
        )
        raise
    else:
        elapsed_ms = (time.perf_counter() - start) * 1000
        logger.info(
            "Operation completed",
            operation=name,
            duration_ms=round(elapsed_ms, 2),
            **extra_fields,
        )

# Usage
with timed_operation("fetch_user_orders", user_id=user.id):
    orders = await order_repository.get_by_user(user.id)

Pattern 8: OpenTelemetry Tracing

Set up distributed tracing with OpenTelemetry.

Note: OpenTelemetry is actively evolving. Check the official Python documentation for the latest API patterns and best practices.

python

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter

def configure_tracing(service_name: str, otlp_endpoint: str) -> None:
    """Configure OpenTelemetry tracing."""
    provider = TracerProvider()
    processor = BatchSpanProcessor(OTLPSpanExporter(endpoint=otlp_endpoint))
    provider.add_span_processor(processor)
    trace.set_tracer_provider(provider)

tracer = trace.get_tracer(__name__)

async def process_order(order_id: str) -> Order:
    """Process order with tracing."""
    with tracer.start_as_current_span("process_order") as span:
        span.set_attribute("order.id", order_id)

        with tracer.start_as_current_span("validate_order"):
            validate_order(order_id)

        with tracer.start_as_current_span("charge_payment"):
            charge_payment(order_id)

        with tracer.start_as_current_span("send_confirmation"):
            send_confirmation(order_id)

        return order

Best Practices Summary

Use structured logging - JSON logs with consistent fields
Propagate correlation IDs - Thread through all requests and logs
Track the four golden signals - Latency, traffic, errors, saturation
Bound label cardinality - Never use unbounded values as metric labels
Log at appropriate levels - Don't cry wolf with ERROR
Include context - User ID, request ID, operation name in logs
Use context managers - Consistent timing and error handling
Separate concerns - Observability code shouldn't pollute business logic
Test your observability - Verify logs and metrics in integration tests
Set up alerts - Metrics are useless without alerting

python-observability

NPX Install

SKILL.md Content

Python Observability

When to Use This Skill

Core Concepts

1. Structured Logging

2. The Four Golden Signals

3. Correlation IDs

4. Bounded Cardinality

Quick Start

Fundamental Patterns

Pattern 1: Structured Logging with Structlog

Pattern 2: Consistent Log Fields

Pattern 3: Semantic Log Levels

Pattern 4: Correlation ID Propagation

Advanced Patterns

Pattern 5: The Four Golden Signals with Prometheus

Pattern 6: Bounded Cardinality

Pattern 7: Timed Operations with Context Manager

Pattern 8: OpenTelemetry Tracing

Best Practices Summary