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Setting up Python logging for a library/app

joaomcteixeira profile image João M.C. Teixeira ・6 min read

Hello [DEV] World! This is my first post here :-)

I started using the Python logging library a couple of years ago; it is definitively a powerful lib, and I encourage you to embrace it in your code developments.

There are countless web pages with HowTo's and tutorials explaining how to set a logging system for your Python one-file script. However, it is difficult to find places that explain how to set up the Python logging library to be used application-wide, and how to properly integrate and share the logging in all your application modules, comfortably. You can still find some examples on the web and in the official documentation cookbook. The referred lack of tutorials is especially true if your project focuses on both library development and non-developer interface distribution. In these cases, it is necessary to set up the logging such that it provides a proper output when users are utilizing your application as a library and when users use it as stand-alone software. In the latter case, end-users will expect .log and/or .debug files with the execution record. On the other hand, users of your libraries won't need such files, and almost certainly a message printed to the sys.stdout or sys.stderr suffices.
So, the question remains on how to set up such difference interfaces properly. You will see that once configured, the set up looks quite simple and apparent. But, honestly, it took me time to decompose the logging functionalities to a minimum set of operations that meets these requirements. For this logging configuration, we will make use of two main concepts: 1) global variables and 2) the fact that we can configure the logging dynamically. I will present the solution I currently using. Please share your thoughts if you have a different or better approach.

Configuring the logger widely

Consider the following project structure:

SampleProject/ # <- main repository folder
    src/
        sampleproject/
            __init__.py
            base.py (here is where your main API interface goes)
            logger.py
            package1/
                __init__.py
                pkg1_module.py
            (... etc ...)
    docs/
    (... other files...)
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What we want is to set up our logger object before any of project's module initiates so that they can make use of log right from the beginning.

For this, directly in the sampleproject root __init__.py, we define the main logger with logging level set to DEBUG. In the configuration I am presenting here, which is the one I am using nowadays, I define a single logger that serves the whole application/library/package.

# in src/sampleproject/__init__.py
import logging

log = logging.getLogger(__name__)
log.setLevel(logging.DEBUG)
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log is now instantiated and is a global variable set on the main project's __init__.py file.

Secondly, and immediately after in the __init__.py, I define the logging handler responsible for writing, by default, to the sys.stderr, read more about the StreamHandler. In elementary words, this is the logger handler that replaces the print function. I am not explaining logging Handlers here, for that, as said in the beginning, there are countless high-quality explanations out there:

# in src/sampleproject/__init__.py
import logging

from sampleproject.logger import INFOFORMATTER


log = logging.getLogger(__name__)
log.setLevel(logging.DEBUG)

# defines the stream handler
_ch = logging.StreamHandler()  # creates the handler
_ch.setLevel(logging.INFO)  # sets the handler info
_ch.setFormatter(logging.Formatter(INFOFORMATTER))  # sets the handler formatting

# adds the handler to the global variable: log
log.addHandler(_ch)
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Do you notice the variable INFOFORMATTER? I import it from logger.py module in sampleproject, where I define additional help variables or functions related to the logging, for example, the formatters:

# in src/sampleproject/logger.py
DEBUGFORMATTER = '%(filename)s:%(name)s:%(funcName)s:%(lineno)d: %(message)s'
"""Debug file formatter."""

INFOFORMATTER = '%(message)s'
"""Log file and stream output formatter."""
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Generally, I define a highly verbose debug formatter that identifies the file and line where the message is logged and, on the other hand, I use a simple and user-friendly formatter for the INFO log level, which is just the message the program needs to print.

Because the logger is now defined at the root level of the sampleproject, we can import it in any module.

For example:

# in src/sampleproject/package1/pkg1_module1.py
from sampleproject import log

# and then just use it
log.info('message')
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You can use it the same whether it is inside classes or inside functions.

def myfunct(args):
    # do something
    log.info('worked, this is the result {}'.format(result))
    return result
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Put this way, it sounds effortless.

Other considerations in the __init__ file

However, in case we need to import any of our project functionalities to the main namespace and because we want to set up the logging before loading all other modules in our project, we have to break a PEP8 rule on import statements, that says:

"Imports are always put at the top of the file, just after any module comments and docstrings, and before module globals and constants."

The __init__.py layout would finally look like this:

"""
PROJECT MAIN DOCSTRING
"""
# import ... # Python standard library imports here
import logging

from sampleproject.logger import (
    DEBUGFILE,
    DEBUGFORMATTER,
    INFOFILE,
    INFOFORMATTER,
    )

# this is exactly what we explained before
log = logging.getLogger(__name__)
log.setLevel(logging.DEBUG)
_ch = logging.StreamHandler()
_ch.setLevel(logging.INFO)
_ch.setFormatter(logging.Formatter(INFOFORMATTER))
log.addHandler(_ch)

# finally you would import from your project what you need
# to bring to the root NameSpace, if you need to do so.
from sampleproject.package1.module1 import SuperClass, megafunction
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This breaks, in my understanding, the referred PEP8 rule, because we import only after the log has been configured. If you are using a lint checker, for example, flake8 and isort, you may need to add the ignore statement that at the end of the import line:

from sampleproject.package1.module1 import SuperClass, megafunction  # noqa: F401 isort:skip
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is this a circular import?

Although SuperClass and megafunction use the log defined in the main __init___.py file and this imports the former two objects, I have found no problems with circular imports. Do you find it otherwise?

Configuring logging files for users

As said, if you are developing a project that serves both library purpose and stand-alone application, you need to create log files for the program executions. I usually create at least two files: one info.log and one debug.log. As expected, the info.log registers all messages with the INFO level while the debug.log registers all the DEBUG minimum level messages. How can we separate the creation of the log files from the log instantiation itself, so that the former only operates when the program runs as stand-alone software, for example, in a command-line application? Simply, add the handlers that configure both logfiles at the execution entry point. How?

In the logger.py module, define a init_log_files function, which is called from the entry point when sampleproject is executed as a stand-alone program. If sampleproject is used just as a library, these files will not be created.

# in src/sampleproject/logger.py
def init_log_files(log, mode='w'):
    """
    Initiate log files.

    Two files are initiated:

    1. :py:attr:`myapp.logger.DEBUGFILE`
    2. :py:attr:`myapp.logger.INFOFILE`

    Adds the two files as log Handlers to :py:attr:`log`.

    Parameters
    ----------
    mode : str, (``'w'``, ``'a'``)
        The writing mode to the log files.
        Defaults to ``'w'``, overwrites previous files.    """
    # here, I show a very simple configuration
    # but you can extend it to how many handlers
    # and tweaks you need.
    db = logging.FileHandler(DEBUGFILE, mode=mode)
    db.setLevel(logging.DEBUG)
    db.setFormatter(logging.Formatter(DEBUGFORMATTER))

    info = logging.FileHandler(INFOFILE, mode=mode)
    info.setLevel(logging.INFO)
    info.setFormatter(logging.Formatter(INFOFORMATTER))

    log.addHandler(db)
    log.addHandler(info)
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You can also extend this configuration to which better fit your needs. Lastly, in your CLI file, simply call init_log_file.

# in a CLI file
# for example, src/sampleproject/cli_1.py
from sampleproject import log
from sampleproject.logger import init_log_files

# some where down the line
def main(*args, **kwargs):
    init_log_files(log)
    # continue operating
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Any additional configurations, like dedicated CLI log filenames, can be added as parameters in init_log_files.

Summary

In this post, I have shared with you how:

  • I currently configure the Python logging library to serve a project as a whole, with multiple packages and modules
  • to separate the logging setup and the creation of file handlers, this allows the log to serve for both library purpose and stand-alone applications
  • activate log file handlers on CLI request

I hope you find this article of interest, and I look forward to your comments.

Note: this post is also available in my website.

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