Python observability patterns including structured logging, metrics, and distributed tracing. Use when adding logging, implementing metrics collection, setting up tracing, or debugging production systems.
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Python observability patterns including structured logging, metrics, and distributed tracing. Use when adding logging, implementing metrics collection, setting up tracing, or debugging production systems.
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Stars38,174
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LanguagePython
LicenseMIT
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</> SKILL.md
python-observability.SKILL.md
---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.
Detailed sections (starting with `## Advanced Patterns`) live in `references/details.md`. Read that file when the navigation summary above is insufficient.
## Best Practices Summary
1. **Use structured logging** - JSON logs with consistent fields
2. **Propagate correlation IDs** - Thread through all requests and logs
3. **Track the four golden signals** - Latency, traffic, errors, saturation
4. **Bound label cardinality** - Never use unbounded values as metric labels
5. **Log at appropriate levels** - Don't cry wolf with ERROR
6. **Include context** - User ID, request ID, operation name in logs
7. **Use context managers** - Consistent timing and error handling
8. **Separate concerns** - Observability code shouldn't pollute business logic
9. **Test your observability** - Verify logs and metrics in integration tests
10. **Set up alerts** - Metrics are useless without alerting
