python-observability

name python-observability description Python observability patterns including structured logging, metrics, and distributed tracing. Use when adding logging, implementing metrics collection, setting up tracing, or debugging production systems. 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. 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 # DE BUG: 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. 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: 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() Detailed worked examples and patterns Detailed sections (starting with ## Advanced Patterns ) live in references/details.md . Read that file when the navigation summary above is insufficient. 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