kata-python-logging-setup

You can set up structured JSON logging by configuring Loguru sinks with orjson serialization, applying field redaction, and emitting consistent JSON payloads with trace and correlation identifiers for production traceability.

You can instrument sync and async Python functions by applying a @logged decorator that automatically logs enter and exit events, captures execution duration, and records errors with minimal exception exposure.

The best way to correlate Python logs with OpenTelemetry traces is to wire correlation middleware into HTTP request handling and inject trace and span identifiers directly into your structured JSON log payloads.

You prevent sensitive data from appearing in Python application logs by implementing a logging bootstrap with redaction logic that strips sensitive fields and disables diagnostic backtrace output before payloads are serialized to JSON.

You enforce a standard Python logging pattern by defining a Ruff and AST pre-commit policy that blocks unauthorized inline logger calls outside an approved allowlist, ensuring all logging flows through the decorator workflow.

Yes, this structured logging approach works with both readable and JSON log outputs by configuring Loguru sinks to simultaneously emit human-readable formats and structured JSON records for production pipelines.