More fine grained logging control in Python applications

Note: This post is pretty Python specific, but most of the ideas should be applicable in any other programming language as well.

In a previous post I wrote about the implications of overly using logs in performance critical blocks, even when the log level is set high. The bottom line of that post was: even little things can add up and logs always cost something due to strict evaluation.

The above mentioned previous post was concerned about making logs in performance critical situations cheaper. In this blog post I want to write about a very similar problem: reducing the amount of logs.

I want to state the problem with a fictitous scenario: Our project consists of 20,000 lines of code distributed over 100 different files. Because we are good programmers all the source code in every file is well documented and a lot of logging is available if we need it. Now a bug is filed. We can pin the problem down to one of the 100 files. So to see wht’s going wrong we set the log level to DEBUG and run the program. Sadly our application is highly concurrent and when we look at the log it is polluted with unwanted messages. Before we can find the faulty code we have to apply a lot of filtering and searching. Unfortunately this is the case for every bug and every time the filtering and searching is different to the last time. We waste a lot of time just getting the right information.

What’s the problem? The problem is that the log level is valid for the whole project. Most often logging is set up in the standard way: 1 log level with 5 possible criticalities (debug, info, warning, error, critical) and all loggers listen to this one log level. Thus when we set the level to DEBUG logs will be produced everywhere in every file, even though we only want the logs from one file. No problem you say? The filtering of the log is easy you say? True! But the point is: these logs have been made! If you are dealing with performance critical code it is possible that this poses a serious problem. It’s better to never log unnecessary stuff in the first place.

The problem exists because one central log level is too coarse for a big and mature project. What we want is more fine grained control on a per file level.

Per file logging

The solution I want to present is a combination of two things: a logging configuration and a setup function for the logger.

I provided a very simple Python project for the following discussion. Here is the full zip.

Let’s start with the logging configuration:

import logging                         # (a)

LOG_LEVEL = logging.ERROR              # (b)

logging.basicConfig(level=LOG_LEVEL)   # (c)

LOG_HANDLER = logging.NullHandler()    # (d)

PER_MODULE_LOG_LEVELS = {              # (e)
    'moduleA': logging.ERROR,
    'moduleB': logging.ERROR,

The idea is that this file should contain every information about our logging environment. Here are the explanations:

(a) we import Python's standard logging module.
(b) we set the global log level of our application
(c) we write it to the basic configuration of the logging module
(d) we define our log handler (a real project would not use the NullHandler)
(e) the key idea: a dictionary that maps file names (modules) to a log level

I think the idea is already visible. We want to give every file it’s own private log level that we can adjust to our liking. In order for this to work every file in our project needs an entry in this dictionary. If we add or rename a file we have to adjust the dictionary accordingly. This however does not pose a big constraint, because tasks like this are easily scripted, e.g. as part of a make call.

Before I show the setup function I want to show how it is used in a module:

import logging
from util import setup_logger

logger, log_level = setup_logger(__name__)    # (a)

def functionA(i):
    ret = i * 2
    if log_level <= logging.DEBUG:            # (b)
        logger.debug(f"In functionA: argument {i} return value {ret}")
    return ret

As you can see in (a) we give the setup_logger function the name of the current module via the special __name__ variable and the function returns a logger and the logger’s log level. The reason we want the log level explicitely is to be able to use lazy logging as described in my other post. In the above example this happens in line (b).

Now to the setup_logger function:

import logging
import logging.handlers
import log_config

def setup_logger(name):
    logger = logging.getLogger(name)                        # (a)
    logger.propagate = False                                # (b)
    log_level = log_config.LOG_LEVEL                        # (c)

    if name in log_config.PER_MODULE_LOG_LEVELS:            # (d)
        log_level = log_config.PER_MODULE_LOG_LEVELS[name]

    fh = logging.handlers.MemoryHandler(2048, target=log_config.LOG_HANDLER)
    return (logger, log_level)

Here’s what’s happening:

(a) We use the standard logging module to give us a new logger for `name`,
    which the value of `__name__`, i.e. the module's name from which
    `setup_logger` is called
(b) We set the propagate flag of the logger to *False*. This is important,
    because loggers are typically structured in a hierarchy and we don't
    want this logger to send its output to the handlers of the parent. This
    allows us to have per file logging output control
(c) We set the default log level (of the base logger) in case the file does
    not have an entry in the dictionary
(d) Now we lookup the file's private log level and set it for the logger

What follows is just sample code of setting up a handler and adding it to the logger. Adjust this code to your needs.

Let’s now come back to the original motivating scenario of the intro and apply it to the simple project. If we assume that we know the problem is in moduleA, then all we have to do is set the log level of this module to DEBUG, like so:

    'moduleA': logging.DEBUG,
    'moduleB': logging.ERROR,

As a result we will now see the logging output of moduleA, and only of moduleA! That this can make a considerable performance difference shows the slightly pathological main function of our example project:

$ python3.6

On my machine I get the following results:

Log Level moduleA Log Level moduleB Average time in s for 10 runs
ERROR ERROR 0.39561099929997
DEBUG ERROR 12.451260923500376
DEBUG DEBUG 24.318808462699963

And in the example we used just in memory logging! If we print to stdout or file handles this gets even worse!

Small extra task: How do the times change when we are not using the lazy logging trick, i.e. not put an if-statement before our logging calls?