In python, why use logging instead of print?

Question

For simple debugging in a complex project is there a reason to use the python logger instead of print? What about other use-cases? Is there an accepted best use-case for each (especially when you're only looking for stdout)?

I've always heard that this is a "best practice" but I haven't been able to figure out why.

Solution 1

The logging package has a lot of useful features:

• Easy to see where and when (even what line no.) a logging call is being made from.
• You can log to files, sockets, pretty much anything, all at the same time.
• You can differentiate your logging based on severity.

Print doesn't have any of these.

Also, if your project is meant to be imported by other python tools, it's bad practice for your package to print things to stdout, since the user likely won't know where the print messages are coming from. With logging, users of your package can choose whether or not they want to propogate logging messages from your tool or not.

Solution 2

One of the biggest advantages of proper logging is that you can categorize messages and turn them on or off depending on what you need. For example, it might be useful to turn on debugging level messages for a certain part of the project, but tone it down for other parts, so as not to be taken over by information overload and to easily concentrate on the task for which you need logging.

Also, logs are configurable. You can easily filter them, send them to files, format them, add timestamps, and any other things you might need on a global basis. Print statements are not easily managed.

Solution 3

Print statements are sort of the worst of both worlds, combining the negative aspects of an online debugger with diagnostic instrumentation. You have to modify the program but you don't get more, useful code from it.

An online debugger allows you to inspect the state of a running program; But the nice thing about a real debugger is that you don't have to modify the source; neither before nor after the debugging session; You just load the program into the debugger, tell the debugger where you want to look, and you're all set.

Instrumenting the application might take some work up front, modifying the source code in some way, but the resulting diagnostic output can have enormous amounts of detail, and can be turned on or off to a very specific degree. The python logging module can show not just the message logged, but also the file and function that called it, a traceback if there was one, the actual time that the message was emitted, and so on. More than that; diagnostic instrumentation need never be removed; It's just as valid and useful when the program is finished and in production as it was the day it was added; but it can have it's output stuck in a log file where it's not likely to annoy anyone, or the log level can be turned down to keep all but the most urgent messages out.

anticipating the need or use for a debugger is really no harder than using ipython while you're testing, and becoming familiar with the commands it uses to control the built in pdb debugger.

When you find yourself thinking that a print statement might be easier than using pdb (as it often is), You'll find that using a logger pulls your program in a much easier to work on state than if you use and later remove print statements.

I have my editor configured to highlight print statements as syntax errors, and logging statements as comments, since that's about how I regard them.

Solution 4

If you use logging then the person responsible for deployment can configure the logger to send it to a custom location, with custom information. If you only print, then that's all they get.

Solution 5

In brief, the advantages of using logging libraries do outweigh print as below reasons:

• Control what’s emitted
• Define what types of information you want to include in your logs
• Configure how it looks when it’s emitted
• Most importantly, set the destination for your logs

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In detail, segmenting log events by severity level is a good way to sift through which log messages may be most relevant at a given time. A log event’s severity level also gives you an indication of how worried you should be when you see a particular message. For instance, dividing logging type to debug, info, warning, critical, and error. Timing can be everything when you’re trying to understand what went wrong with an application. You want to know the answers to questions like:

• “Was this happening before or after my database connection died?”
• “Exactly when did that request come in?”

Furthermore, it is easy to see where a log has occurred through line number and filename or method name even in which thread.

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Here's a functional logging library for Python named loguru.

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Comments

[Chris Eberle]

For big projects logging is always a "best practice" because you can easily turn it on or off, and get more or less information. print offers neither of these advantages.

[agf]

See i.e. blog.tplus1.com/index.php/2007/09/28/…

[SingleNegationElimination]

I don't think there's ever a best use case for print.

[slushy]

The python logging documentation says the best use case for print is to display help messages for the user in a command line application.

[information_interchange]

Reading all these answers, I want to ask the reverse question: is there ever a reason to not use logging?

[rkechols]

I'd say print is also useful for writing to text files (e.g. print("text", file=f)), when you prefer its way of formatting over how f.write("text") does formatting. I think it depends on what exactly you're writing, though.

[TimothyAWiseman]

Very well said. I will sometimes use print when debugging a throwaway script that I intend to run exactly once, but any code that will ever been seen by other human eyes or that is meant to last more than one day gets logger.

[Jonathanb]

Definitely +1 for sending output to files. Parsing a logfile in the post-mortem is much better than having to make it break again in an open console window to find the error. Basically, a logger is ideal for any time you need to debug the script after it has failed rather than while it is failing. It's also ideal for any time you have to debug a complex problem that requires you to analyze the program output. Basically, any time you're dealing with errors more complex that syntax errors, a logger will probably simplify that for you.

[Sören]

"The flush may occure only at function return." Which function? print()? No, output buffering can delay output even longer. "If for any reason the program is killed (kill -9, hard reset of the computer, …)," In that case, all bets are off anyways. Nothing a logging library can do about it.

[Floh]

I should have been a little more precise.  In non interactive shell (big part of the executions), print is block buffered and then the print are buffered until the end of the current block. The logging module ensures the flush at the end of each call. So yes, in case of kill -9 or hard reset, we could still find all lines in the output where some could be missing if print had been used.

[Sören]

What is your definition of 'block'?

[Floh]

Let say you have ` def demo(): print("a") time.sleep("120") print("b") ` in a non interactive shell, you will see "a" appear at the same time as "b", directly at the end of the 120 seconds count.

[Sören]

Or even later. The duration of a function call has nothing to do with how long stuff is buffered.

[Floh]

Anyway, the print will appear only when demo returns and we can reasonably expect in the given example it will be around 120 seconds.

[Jason]

Can us print("hello world", flush=True)

[Floh]

The flush argument is a valid solution starting from python 3.3. However, the built-in logging module is the best solution for the logging use case.