logging_strict

Overview

logging.config yaml Strict typing and editable

For logging.config yaml files, logging-strict does the following:

  • Editable logging configuration

    While running a Python app, some arbritary package, out of no where, decides to log an informational warning. Within a multiprocessing worker (aka heavy background processing), these logging warnings go from annoying –> disruptive.

    The best can do is adapt to survive. Make this situation quickly solvable by adjusting the app’s logging configuration.

    asyncio is an example package which bleeds informational logging warnings

  • curates

    Intention is to have all the valid logging.config yaml in one place

  • validator

    logging_strict comes with a logging.config yaml validator. So can check the editted yaml file. Supports pre-commit

  • validates against a strictyaml schema

    The schema is specifically tailored for the logging.handlers

    As long as the yaml is valid, will have the data types logging.handlers expect

  • exports package data

    Alternative to pkgutil.get_data()

    Export data files using a pattern rather than one file at a time

The latest version is logging-strict 1.3.0. It is supported on:

  • py39 - py313

Why?

logging.config is more often than not hardcoded within a package’s source code. Removing logging.config from the source code and into an exported yaml config file, exported files are edittable. With added benefit can quickly adapt to unforeseen unexpected bleeding of logging messages.

When a bleed occurs, open the exported logging.config yaml file. Add the offending package to the loggers section or if already there, increase the logging level.

For example, for asyncio, adjust logging level from logging.WARNING –> logging.ERROR

Bye bye disruptive informational logging warning messages.

logging_strict comes with a logging.config yaml validator. So can check the editted yaml file.

On app|worker restart, the logging configuration changes take effect.

Quick start

Getting started is easy:

From your packages project base folder. This example assumes, unittests folder, tests/

  1. Install logging-strict:

    $ . [venv path]/activate
$ python3 -m pip install logging_strict

For full details

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