Python is one of the most successful programming languages out there. Its easy syntax and user-friendliness make it the go-to programming language for a broad spectrum of people, from absolute beginners to advanced AI and ML engineers.
In this article, we will discuss the best practices for logging that will help you with backtracking and finding errors as well as their solutions.
Python Logging: The Basic Fundamentals
Contrary to other programming languages, Python comes built-in with a logging module. We will discuss this module as well as the basic configuration settings.
In-Built Logging Module
Python’s standard library comprises a flexible inbuilt logging module that allows you to create a variety of configurations to fulfill the logging needs of the developers.
Python’s logging module comprises functions designed to enable developers to log different destinations. They can do this by defining various handlers and routing the log messages to appropriate handlers.
The following snippet of code is an example of the logging module:
import logging
logging.info('This is an info message.')
logging.warning('This is used for warning messages.')
logging.debug('This message can help debug issues.')
Levels of Logging
Most logging tools provide different logging levels, and Python is no exception. In order of increasing severity, the following are logging levels:
1. NOT SET
2. DEBUG
3. INFO
4. WARNING
5. ERROR
6. CRITICAL
Logging levels are labels that you add to your entries. Later on, developers can use these labels to search and filter through the log entries.
Fundamental Logging Configuration
The essential components of the logging module are loggers, handlers, and formatters.
A logger is used when the developer wants to record a message in the application code. Handlers are the components that can effectively write messages to their destination. Formatters are responsible for formatting the layout of log messages.
Best Practices for Python Logging
Here are a few best practices of Python Logging to help developers:
Don’t Use a Primitive Approach
Using print statements or directly writing to files is highly tempting because of the ease that comes with it. However, they come with a price. Developers must use a proper logging solution for the developer to reap the other benefits that come with them. This primitive approach might be enough for a small application but will give the developer headaches when the application starts growing. The logs might become noisy since granularity will become very hard to handle.
Use the Standard Logging Module of Python
It’s a great practice to stick to the built-in logging module that comes with Python. The logging module allows you to define handlers and formatters easily and use them to create powerful combinations. It is very flexible and easy to use.
Use Appropriate Levels
Fortunately, the Python logging module presents fewer levels than other logging libraries. Following are the general guidelines for using the Python module:
● DEBUG: For debugging purposes in development
● INFO: This level is used when something expected happens, such as opening a new session
● WARNING: This level is used when something unexpected or unusual happens. A warning is not an error but requires attention.
● ERROR: This level is for things that go wrong but are usually recoverable
● CRITICAL: Use this level in a doomsday scenario.
Include a Timestamp for Each Entry Log
Knowing something happened without knowing when it happened is only marginally better than not knowing about the event. Make sure to add a timestamp to your log entries to make the lives of the people who use logs for troubleshooting easier. Doing so also allows developers to analyze the log entries to obtain insights/analytics about user behavior.
Conclusion
Python is one of the most popular languages. It offers simplicity, versatility, and a large ecosystem of third-party tools. Python is versatile enough to address various use cases, from web applications to data science libraries, SysAdmin scripts, and many other programs.
Because logging is essential for most applications, reviewing Python logging and highlighting best practices is valuable for all tech pros, especially for those who are just getting familiar with it. Make sure to incorporate these best practices while you code and eliminate the primitive print statements.