What is structured logging in Python?

Structured logging involves emitting logs in machine-readable formats like JSON, making it easier to query, filter, and analyze logs in production environments using tools like ELK or Datadog.

Can I use this with OpenTelemetry?

Yes, the skill includes advanced patterns for setting up OpenTelemetry tracing to provide end-to-end visibility into distributed systems and microservice interactions.

How does this skill handle correlation IDs?

It provides patterns for generating and propagating unique IDs through request headers and context variables, allowing you to trace a single request's lifecycle across multiple internal logs and services.

Does this skill support Prometheus metrics?

Yes, it includes specific implementation patterns for tracking the 'Four Golden Signals' (latency, traffic, errors, and saturation) using the Prometheus Python client.

Python Observability Patterns

Name: Python Observability Patterns
Author: gwickman

bygwickman

0•

Analytics & Monitoring

Implements structured logging, metrics collection, and distributed tracing for Python applications to improve production visibility.

This skill provides standardized patterns and best practices for instrumenting Python applications with deep observability. It enables developers to implement machine-readable structured JSON logging using Structlog, track the 'Four Golden Signals' via Prometheus, and set up distributed tracing with OpenTelemetry. By providing ready-to-use templates for correlation ID propagation and semantic log levels, it ensures that production systems can be monitored and debugged effectively without requiring constant code redeployments to find the root cause of issues.

Key Features

01Correlation ID propagation for end-to-end request tracing

02OpenTelemetry setup for distributed tracing across microservices

03Structured JSON logging configuration using Structlog

040 GitHub stars

05Implementation of the Four Golden Signals for service monitoring

06Prometheus metrics integration for latency, traffic, and errors

Use Cases

01Standardizing logging across a Python microservices architecture

02Setting up Prometheus and Grafana dashboards for application health

03Tracing complex request chains to debug production performance bottlenecks

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add gwickman/auto-dev-test-target-1 python-observability

For use in Claude.ai and ChatGPT

Download Skill