How to List All Existing Loggers Using the Python Logging Module

List All Existing Loggers Using Python Logging

Logging is necessary for understanding what is happening inside a Python program and tracing errors and issues. The Python logging module provides a flexible and powerful logging system that allows you to log messages from different parts of your application.

A key aspect of using the logging module effectively is understanding what loggers have already been defined in your application. This allows you to reuse existing loggers when needed instead of creating redundant ones.

In this guide, you’ll learn:

  • What loggers are in Python, and how the logging module works
  • Different methods to list existing loggers
  • How to check if a logger already exists before creating a new one
  • Tips for managing loggers efficiently in large applications

So, let’s dive in!

What Are Loggers in Python?

The Python logging module revolves around the concept of a logger. A logger is an object you create to log messages about what’s happening in your code.

Some key facts about loggers:

  • Loggers have a name that identifies them, like “main” or “network”
  • You can create as many loggers as you want by calling logging.getLogger(name)
  • Loggers exist in a hierarchy, with parent/child relationships
  • The root of the hierarchy is the “root” logger obtained with logging.getLogger()
  • Logging calls use the logger instance to log messages. For example:“Starting process”)

So, loggers are named objects you instantiate to log messages to different places. This avoids writing print statements everywhere!

Now, let’s look at how to list what loggers have already been created.

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Method 1: Access the Logging Manager’s Logger Dictionary

The easiest way to get existing loggers is to access the manager dictionary on the root logger:

import logging

loggers = [logging.getLogger(name) for name in logging.root.manager.loggerDict]

Here’s how it works:

  • The root logger is obtained with logging.getLogger()
  • The root logger has a manager property which is the global Logging Manager
  • This manager has a loggerDict which maps logger names to logger instances
  • We iterate over the names in this dict to get logger objects

This will return a list containing all initialized loggers in your application.

Let’s look at a quick example:

import logging

logger1 = logging.getLogger("app.module1")
logger2 = logging.getLogger("app.module2")

loggers = [logging.getLogger(name) for name in logging.root.manager.loggerDict]

print(len(loggers)) # 3 (incl. root logger)

So that’s the simplest way to get all existing logging objects!

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Including the Root Logger

One catch with the above method is that it does not include the root logger itself in the list.

To also get the root logger, you can do:

import logging

loggers = [logging.getLogger()] # root logger
loggers += [logging.getLogger(name) for name in logging.root.manager.loggerDict] 

First, we directly fetch the root logger, then extend the list with the others.

Method 2: Use the logging_tree Module

The logging_tree module provides specialized functions to examine the logger hierarchy:

import logging_tree

logging_tree.printout() # prints a visual tree

tree = logging_tree.tree() # get the tree object

Some advantages of using this module:

  • Easily visualize parent/child relationships
  • Includes handy traversal functions on the tree
  • Integrates seamlessly with existing loggers

So logging_tree is great for complex applications with many interdependent loggers.

Check If a Logger Exists

Now, look at a practical use case: checking if a logger exists before creating a new one.

name = "app.submodule" 

if name in logging.root.manager.loggerDict:
    print("Logger exists already")  
    logging.getLogger(name) # create a new logger

Here by testing for the presence of the name in loggerDict, we avoid redundant logger creation. This prevents errors and inefficient resource usage when running large applications over a long period.

Some other tips:

  • Encapsulate this check in a custom create_logger method
  • Use a central constants file to store logger names
  • Prefix logger names with the application name

Following best practices like these makes your loggers easier to manage over time.


We went over several methods to get existing loggers in Python:

  • Iterating over the manager dictionary on the root logger
  • Using the logging_tree module for advanced introspection
  • Techniques to check if a logger already exists before creating

Mastering logger introspection allows you to write large, complex applications and have full visibility into the logging system. So next time you’re troubleshooting a strange logging issue or planning logging for a new project, be sure to keep these techniques in mind!