python-observability

What problem does it solve?

This Skill helps Python developers achieve end-to-end observability by providing structured logging, metrics, and distributed tracing, enabling faster diagnosis of production issues without invasive changes.

Core Features & Use Cases

• Structured Logging: emit JSON logs with consistent fields for correlation and filtering.
• Metrics & Tracing: collect Prometheus-compatible metrics and OpenTelemetry traces to monitor performance and service interactions.
• Use Case: when debugging a running service, instrument code to capture request latency, errors, and throughput, and correlate across services.

Quick Start

Install and configure the recommended libraries (e.g., structlog, prometheus_client, and opentelemetry) and provide a minimal example to initialize logging, metrics, and tracing. Example (plain text): import logging import structlog from prometheus_client import start_http_server from opentelemetry import trace from opentelemetry.sdk.trace import TracerProvider from opentelemetry.exporter.otlp.proto.grpc.exporter import OTLPSpanExporter from opentelemetry.sdk.trace.export import BatchSpanProcessor

Initialize logging

structlog.configure(...) logging.basicConfig(level="INFO") logger = structlog.get_logger()

Start metrics server

start_http_server(8000)

Configure tracing

provider = TracerProvider() processor = BatchSpanProcessor(OTLPSpanExporter(endpoint="http://localhost:4317")) provider.add_span_processor(processor) trace.set_tracer_provider(provider) tracer = trace.get_tracer(name) logger.info("Observability initialized")