Python remains the go-to choice for many programmers worldwide, enabling them to program high-performance applications efficiently. One of the reasons behind its global fame is its built-in logging module which helps developers log events and store information.
In this article, we will discuss 5 Python logging best practices to help you use this functionality efficiently and debug, analyze, and optimize codes effortlessly.
Knowing the right ways to log events in Python can help you make programming applications hassle-free. Here are 5 Python logging best practices to debug applications faster and up your Python game.
You cannot log everything, but you also cannot afford to miss the crucial bits, so it is best to strive for balance. Many programmers work with the default logging level and don’t bother changing it. However, it is vital to readjust them per your needs.
Here moderation helps, as setting it to warning makes Python super cautious and limits the logs while sticking to debug floods the code with needless information.
So, what to do? Optimize on the fly. Set the Python logging level to debug while you develop but change it to info or higher before it enters production. This tactic helps maintain the balance and program applications in Python better.
Timestamps help you track the sequence of events in Python and tell details like when the event occurred and its completion time. Their function may seem simple, yet they help a lot when you sit to debug or optimize the code and can help improve the Python logging experience.
However, Python timestamps log messages across applications, and this often causes a headache unless you use a consistent format. So, keep it consistent.
The best is to use ISO-8601 format as it specifies the order and limits the chaos. The format stamps time in the below format:
Log events, processes, and everything you like, but don’t ever log sensitive details like credentials, credit card details, or private keys. Why? As it poses a grave security threat or could help others breach your security and steal the information you save behind firewalls.
That said, sometimes it is essential to log sensitive details, and in case there is no way to avoid it, encrypt. Encryption helps ensure only the right eyes access the information and your code remains safe and secure. Simple practices like this work like a charm and help you make Python logging efficient and reliable.
Why do we log information? So, our future self or someone we want can read it to understand the code or the process better. Thus, it is best to be descriptive and clear, as you don’t want to log a message that makes no sense when you return to it later.
Using descriptive log messages helps you keep ambiguity at a minimum and debug the code efficiently, as you don’t have to spend time decoding the log. A way to do so is to add all the relevant details about the event, like the location, the time, and other relevant metadata.
Structured logging may seem tedious at first, however, it is worth the hassle as it helps keep things simple. In Python, it implies using a key-value pair to log and store events or details. More than us, structured logging helps the machine read the data right, and this optimizes analysis and debugging.
Not to forget, structured logging also makes it easy to filter, search, and analyze logs using specific values or fields and makes analysis and debugging a breeze.
Implementing Python logging best practices can simplify your life as a Python developer by streamlining analysis, diagnosis, and debugging processes.
That said, you cannot rely entirely on these Python logging practices and may have to implement more to achieve better observability. The more you adopt to better the log, the better it is, as every effort in the right direction pays dividends in the production stage. So, log away, but do it right.
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