There is not an entirely reliable way to know the state of your Python app unless you use a competent logging system. In addition to being in the dark, you’re also unable to diagnose problems. The Python library helps you strictly define the kinds of data you want in your logs, control anything that goes out and configure it on its way out the door. But the biggest advantage is that you can minutely adjust the destination of any data that leaves the Python library.

Overall, there are many advantages to using a Python Syslog system that includes a logging library. You can use the library’s massive functionality to add context, log various severity levels, employ time stamps, understand a log’s origin and, finally, perform useful configurations. Here’s a quick look at some of the important details about using the Python log library:


It’s possible to use the library to add detailed metadata to logs. That’s just one way to build up the context around the various messages and ultimately assist with the error diagnosis. Contextualized log data makes it much easier to troubleshoot when a problem does end up arising.


If you want to easily see which messages are the most important ones at any given moment, you can do so if the log events have been segmented based on severity. You won’t have to spend hours sifting through page after page of events. You can simply hone in on the exact level of severity you’re interested in.

There’s another advantage to segmenting this way. When you read a specific message, you’ll instantly know how critical the situation is and can devote the proper amount of attention to it. The detail is power. In other words, information about debugging can come in handy when trouble arises. That’s why it pays big dividends to create log messages that are as detailed as possible.

Time Stamps

Whenever you find yourself in the midst of a messy application fix, you need to know specific timeline information. For example, if a database connection conks out, it’s extremely helpful to know what was going on the moment before and the moment after the failure. Timestamps provide the answer.

Another advantage of time stamps: you can see whether, and precisely when, you ran out of disk space, or when a particular request arrived. A Python logging library makes it easy to add information that is clearly and accurately time-stamped. Without timestamps, you’re essentially flying blind and just hoping for the best outcome.


It’s always a good policy to find out the origin of log messages. Getting that kind of context, with the logging library’s help, is invaluable during diagnosis and trouble-shooting. You can even see what thread, module, line number or function the message came from. Knowing a message’s origin is almost as vital as having it time stamped. Both stamping and origin context goes a long way toward offering you a crystal-clear view of what’s going on in your system.