convention-logging

To standardize Python logging, enforce consistent rules for level selection, message formatting, and structured patterns. This ensures safe, clear log output that improves debuggability and traceability across development and production environments.

Choosing between structlog and standard logging depends on your project's context. Guidelines provide decision criteria for development and production, ensuring you select the appropriate library to achieve structured and consistent logs.

Preventing sensitive data leakage requires applying masking conventions to your Python logs. Guidelines supply examples and gotchas to avoid exposing sensitive information and to reduce noisy logs in production environments.

Yes, the logging convention guidance is applicable to both new and existing Python projects. It supports refactoring existing code to align with team standards during code reviews and production audits.

You can verify log conformance by configuring a Python module to follow the conventions and checking logs against the rules. This guidance is compatible with check-logging verification processes.