Python's logging module is a powerful tool, but it can quickly become a source of "noise" when third-party libraries flood your logs with messages that are not relevant to debugging your application. Keeping logs clean is crucial for efficiently identifying issues and understanding the actual behavior of your code.

The Problem with basicConfig()¶

Many Python developers start using logging with logging.basicConfig(). While convenient, this function configures the "root logger" of your application. The problem is that all loggers in your application, including third-party libraries, are children of the root logger.

This means that if you configure the root logger to the DEBUG level, you will start seeing debug messages from all the libraries you use, which can be overwhelming and hide the truly important information from your own code.

The Recommended Solution: Granular Configuration¶

To avoid this overload, the strategy involves taking more granular control of logging levels.

1. Name Your Loggers: Follow the best practice of creating a specific logger for each module of your application using logging.getLogger(__name__). This creates a hierarchy of loggers that you can control individually.

   ```python

   my_module.py¶

   import logging logger = logging.getLogger(name)

   def my_function(): logger.info("My function is running.") logger.debug("Debug message in my function.") ```

2. Configure Levels on Application-Specific Loggers: Instead of modifying the root logger, set the logging level on the highest-level logger of your own application. This allows your modules to log messages at detailed levels (e.g., DEBUG), while third-party libraries (which will remain children of the unconfigured root logger, or with a less verbose default configuration) will only show more critical messages (e.g., WARNING or ERROR).

   ```python

   main.py (or your main entry point)¶

   import logging from my_package.my_module import my_function

   Create a logger for your main application¶

   app_logger = logging.getLogger('my_package') # Or the name of your main package app_logger.setLevel(logging.DEBUG)

   Create a handler for output (e.g., to console)¶

   handler = logging.StreamHandler() formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') handler.setFormatter(formatter)

   Add the handler to your application logger¶

   app_logger.addHandler(handler)

   Now, the root logger (and its children, the libraries) will not be directly affected¶

   You can control the level of specific libraries if you need to¶

   logging.getLogger('requests').setLevel(logging.WARNING) # Example for a library

   my_function() ```

Advanced Tip: First-Party Dependency Management¶

For projects with multiple internal modules or "first-party" dependencies (that you develop yourself), you can wrap getLogger() to always prefix the logger name with your organization or project name (e.g., ORG_NAME.my_module). This allows you to control the logging level of an entire set of internal loggers more easily.

Conclusion¶

Reducing noise in your logs not only makes them more readable but also more reliable as a debugging tool. By adopting a granular approach to Python's logging configuration, you ensure that critical information from your application is not lost in a sea of messages from third-party libraries.

Original article: How to Make Your Logs Less Noisy in Python | Sinclair Target