Logging vs Print
The difference between logging and using print isn’t simply personal preference, rather choosing the proper tool per use case. Check out the Python docs when to use logging if you don’t believe me.
If you are simply using Python to write scripts with clear paths and failure on error, it’s perfectly reasonable to not have to use logging. However as soon as you have a long running program, a service, or a complicated script, it’s time to take the plunge.
The Logging Object
In every file you want to log with, you’ll be adding at minimum two lines:
import logging
logger = logging.getLogger("my_program")
This will grab the logger object associated with the name “my_program”. In very basic terms, think of the interpreter having an internal dictionary of loggers. It knows which one to give you back when to send it a key (name). This way, you don’t have to pass the it around yourself. So you should never share or pass logger objects, simply make another call to getLogger.
A very common mistake I see is projects having a single log.py file with the logger object in it and importing it everywhere, don’t do it.
Let’s repeat that a little louder.
Never share or pass the logger object
DO NOT DO
log.py
logger = logging.getLogger("my_program")
main.py
from .log import logger
submodule.py
from .log import logger
END DO NOT DO
It’s bad practice, prevents easy future modifications, and adds needless complication. You will always get the same logger back with the call the getLogger with the same name.
Therefore, if you are trying to get a logger for an imported project, don’t try to go searching for project.logger, simply make a call to getLogger("Project"). “But how do I know what they named their logger?” Most projects use the same convention, which I suggest you also follow:
import logging logging.getLogger(__name__)
The variable __name__ is a predefined global that houses the running module’s name as imported. So if you run the script directly, it will be "__main__". Whereas if you import the module (my_program.py) it will be "my_program". If you import a submodule of my_prgram.py called submodule.py (creative, arn’t I?) it would be set to "my_program.submodule", just as you would see it as an import line.
“Is it ok to have dots in the name?” It’s better than ok, it’s pure awesome. Python logging has a built in dot hierarchical system, allowing you to specify levels and handlers to the non root logger. In other words, each module could be expanded to have it’s own level and handlers (covered later).
You can set the name manually as well, getLogger("my_program.submodule"), which can be very handy if you want to split logging not by module, but rather shared functionality, or have a different place to log too. Which is one of the main reasons against sharing the logger object. If later on you want to have everything in a type of submodule log to a file, and you were naughty and have shared logger object, you now have to update every file to use a new object. Whereas if you followed good practices and used getLogger in each file, you would only have to add two lines of code to your main file to add a file handler.
One of the ‘gotchas’ with using the same logger across multiple modules is that you have to have the logger’s handlers and settings set up before you use it.
Sounds like a ‘duh’, but consider a case like:
my_project.py
import logging
from subdir import submodule
logger = logging.getLogger("my_project")
# Required handler code redacted for example
logger.warning("Speedboat crashed")
submodule.sub_func(5)
submodule.py
import logging
logger = logging.getLogger("my_project.submodule")
logger.debug("Submodule loaded")
def sub_func(var):
logger.info("Got variable {}".format(var))
return var + 1
Output
No handlers could be found for logger "my_project.submodule" 2016-12-09 16:14:13,079 - my_project - WARNING - Somethings on fire 2016-12-09 16:21:13,387 - my_project.submodule - INFO - Got variable 5
You will get an error message of “No handlers could be found for logger “my_project.submodule””, because the parent logger was not yet set up when it was imported, but was by the time its function was run.
Levels
In the logging module, there are five different preset logging levels:
- 10 – DEBUG
- 20 – INFO
- 30 – WARNING
- 40 – ERROR
- 50 – CRITICAL
When you set a logging level on a logger or handler, you are simply setting a number, and anything set to that number or above will be logged.
logger.setLevel(logging.DEBUG)
Is equivalent to:
logger.setLevel(10)
Everyone uses the first one generally because it is easier to read, and you generally already have the logging module imported, however it is not required.
By default, the logger itself has an effective logging level of WARNING (30), so it will not display debug or info messages. (The built in handlers have a level of NOTSET (0), so they won’t stop stuff from writing, but it is good practice to set them.)
import logging
logger = logging.getLogger(__name__)
# Required handler code redacted for example
print(f"The logger is set at level: {logger.getEffectiveLevel()}")
logger.debug("Debug message")
logger.info("Info message")
logger.warning("Something's on fire")
output
The logger is set at level: 30 2016-12-09 15:23:53,220 - __main__ - WARNING - Something's on fire
It is also possible to create your own level. This is rarely used, I have personally only seen it done on the extreme ends (i.e. 5 for low level debugging and 45 for near critical), so here is an example that doesn’t deal with either of those:
ALMOST_SCARY = 25
logging.addLevelName(ALMOST_SCARY, "ALMOST_SCARY")
logger.setLevel(ALMOST_SCARY)
logger.info("Info message")
logger.log(ALMOST_SCARY, "Spider in the tub")
logger.warning("House set on fire")
output
2016-12-09 15:35:29,413 - __main__ - ALMOST_SCARY - Spider in the tub 2016-12-09 15:35:29,413 - __main__ - WARNING - House set on fire
I find it good practice to set the logger itself as low as possible, then set the level on the handler’s instead. So that way if you eventually want a debug file, you don’t have to change (i.e. debug when fails) multiple lines, just one.
Handlers
Logging handlers are the brains behind the logger that tell it where to send the log messages. The logger object simply has a list of handlers logger.handlers (not to be confused with the available handlers under logging.handlers). When the logger receives a message to log, the logger will iterate through them, and if they have a logging level higher or equal to the one set on the incoming message, it will send it to the registered handler to ‘handle’ (Literal wording from the source code. Programmers are not an inventive lot).
The two most commonly used handlers are StreamHandler and FileHandler (which is a subclass of StreamHandler). However many come in the box, from SysLog to HTTP handlers, and is possible to make or modify one for your own needs.
Here is a basic example of creating a StreamHandler and assigning it to our logger:
import logging
import sys
logger = logging.getLogger(__name__)
my_stream_handler = logging.StreamHandler(stream=sys.stdout)
my_stream_handler.setLevel(logging.INFO)
logger.setLevel(logging.DEBUG)
logger.addHandler(my_stream_handler)
logger.warning("Crazy ex is headed over, has a blow torch")
output
Crazy ex is headed over, has a blow torch
“Wait a minute, all the other examples had time, module and logging level, where’d they go?” That’s all set up with Formatters, which we set up next. For now, sticking to handlers.
StreamHandler is pretty basic, good to use for sys.stdout and sys.stderr. FileHandler is based on top of it and is very commonly used, and often incorrectly. Anytime you are deploying to a production server that you aren’t running logrotate or similar on, you will want to use one of the rotating file handlers, either RotatingFileHandler, based on file size, or TimedRotatingFileHandler.
import logging
from logging.handlers import RotatingFileHandler
logger = logging.getLogger(__name__)
# If either maxBytes or backupCount is set to 0, it will not rotate
my_file_handler = RotatingFileHandler("rotating_example.log",
maxBytes=1024, backupCount=5)
my_file_handler.setLevel(logging.DEBUG)
logger.setLevel(logging.DEBUG)
logger.addHandler(my_file_handler)
for a in list(range(1, 100))[::-1]: # Reveres to start at 99 and count down
logger.info("{0} bottles of beer on a wall, take one down,"
"pass it around, {1} bottles of beer on the wall".format(a, a-1))
This will create six different files (the current log file and past 5 backups), named:
rotating_example.log rotating_example.log.1 rotating_example.log.2 rotating_example.log.3 rotating_example.log.4 rotating_example.log.5
rotating_example.log
9 bottles of beer on a wall, take one down, pass it around, 8 bottles of beer on the wall 8 bottles of beer on a wall, take one down, pass it around, 7 bottles of beer on the wall 7 bottles of beer on a wall, take one down, pass it around, 6 bottles of beer on the wall 6 bottles of beer on a wall, take one down, pass it around, 5 bottles of beer on the wall 5 bottles of beer on a wall, take one down, pass it around, 4 bottles of beer on the wall 4 bottles of beer on a wall, take one down, pass it around, 3 bottles of beer on the wall 3 bottles of beer on a wall, take one down, pass it around, 2 bottles of beer on the wall 2 bottles of beer on a wall, take one down, pass it around, 1 bottles of beer on the wall 1 bottles of beer on a wall, take one down, pass it around, 0 bottles of beer on the wall
rotating_example.log.5
54 bottles of beer on a wall, take one down, pass it around, 53 bottles of beer on the wall 53 bottles of beer on a wall, take one down, pass it around, 52 bottles of beer on the wall 52 bottles of beer on a wall, take one down, pass it around, 51 bottles of beer on the wall 51 bottles of beer on a wall, take one down, pass it around, 50 bottles of beer on the wall 50 bottles of beer on a wall, take one down, pass it around, 49 bottles of beer on the wall 49 bottles of beer on a wall, take one down, pass it around, 48 bottles of beer on the wall 48 bottles of beer on a wall, take one down, pass it around, 47 bottles of beer on the wall 47 bottles of beer on a wall, take one down, pass it around, 46 bottles of beer on the wall 46 bottles of beer on a wall, take one down, pass it around, 45 bottles of beer on the wall
Anything older than that is lost, which is a good thing when you are dealing more in the size of gigabyte log files after months or years of use.
Formatters
The last critical element of logging is the Formatters. They are often overlooked, as people generally use a copy pasted ones from the python docs. But when customized they can be much more project personalized for better debugging. The default, as seen just above is simply:
"%(message)s"
Each handler has it’s own formatter that is best to be set before it’s added to the logger. Probably the most commonly used one (other than the default), is:
"%(asctime)s - %(name)s - %(levelname)s - %(message)s"
They are all kinda a mouthful to set up:
my_stream_handler.setFormatter(
logging.Formatter("%(asctime)s - %(name)s - "
"%(levelname)s - %(message)s"))
2016-12-09 18:50:56,604 - __main__ - INFO - Info message 2016-12-09 18:50:56,604 - __main__ - WARNING - House set on fire
Here’s one I like better, as it sets everything before the message to be the same length, looking a lot cleaner:
"%(asctime)s - %(name)-12s %(levelname)-8s %(message)s"
2016-12-09 18:00:02,417 - __main__ INFO Info message 2016-12-09 18:00:02,417 - __main__ WARNING House set on fire
Doing a lot of threading? Wonder if you’re logging in a thread, or if it’s a thread in a thread?
"%(asctime)s : %(relativeCreated)1d %(threadName)s : %(name)s %(levelname)s %(message)s"
2016-12-09 18:55:12 : 0 MainThread : __main__ INFO Info message 2016-12-09 18:55:12 : 1 Thread-2 : __main__ WARNING House set on fire
There are plenty of options to chose from, they are all listed in the docs.
Putting it all together
Here is a ‘simple’ logging setup from everything discussed so far.
my_project.py
import logging
import sys
logger = logging.getLogger(__name__)
my_stream = logging.StreamHandler(stream=sys.stdout)
my_stream.setLevel(logging.DEBUG)
my_stream.setFormatter(
logging.Formatter("%(asctime)s - %(name)-12s "
"%(levelname)-8s %(message)s"))
logger.addHandler(my_stream)
logger.setLevel(logging.DEBUG)
...
logger.info("We the people")
output
2016-12-09 18:00:02,417 - __main__ INFO We the people
“That’s a lot to remember and write for every program…” I totally agree. I use this type of setup so often, and don’t want to have to re-create a config file for each that I have an importable version of this on PyPI.
import sys import reusables logger = reusables.get_logger(__name__, level=10, stream=sys.stdout)
Produces the same output as above. It also has built in formats for different scenarios and logging helper functions. Check out Reusables.
Realistic Example
One of the true powers behind having multiple handlers, is that you can set them at different levels for different goals. In this more realistic example, the main program is using the stream handler outputting to the screen. The submodule then has a child of that logger, which also has a rotating file handler set for errors or above only, meaning it will log to both the file and screen.
my_project.py
import logging
import sys
from subdir import submodule
logger = logging.getLogger("my_project")
my_stream = logging.StreamHandler(stream=sys.stdout)
my_stream.setFormatter(
logging.Formatter("%(levelname)-8s %(message)s"))
logger.addHandler(my_stream)
logger.setLevel(logging.DEBUG)
def main():
submodule.sub_func("test")
if __name__ == '__main__':
logger.info("Starting my project")
main()
subdir\submodule.py
import logging
from logging.handlers import RotatingFileHandler
logger = logging.getLogger("my_project.submodule")
sub_file_handler = RotatingFileHandler("out.log",
maxBytes=5000000, backupCount=5)
sub_file_handler.setFormatter(
logging.Formatter("%(asctime)s - %(name)-12s"
"%(levelname)-8s %(message)s"))
sub_file_handler.setLevel(logging.WARNING)
logger.addHandler(sub_file_handler)
def sub_func(var):
logger.info("Got variable {}".format(var))
import time
time.sleep(1)
try:
return var / 5.0
except TypeError:
logger.exception("Could not divide")
stdout
INFO Starting my project INFO Got variable test ERROR Could not divide Traceback (most recent call last): File "subdir\submodule.py", line 16, in sub_func return var / 5.0 TypeError: unsupported operand type(s) for div: 'str' and 'float'
out.log
2016-12-11 14:50:42,977 - my_project.submodule ERROR Could not divide
Traceback (most recent call last):
File "subdir\submodule.py", line 16, in sub_func
return var / 5.0
TypeError: unsupported operand type(s) for div: 'str' and 'float'
Repeating the setup
“Now that I know how to do it right, how can I just cut to the chase so I don’t have to write all that every time?”
- Use a logging config (.ini) file
- Use Reusables get_logger
- Use a dictionary (which can be loaded from markdown like YAML or JSON)
It’s mostly personal, if not team or company, preference as to which way you set up logging each time. I personally don’t like having to keep track of yet another config file, so I do it in code. Other’s need to be able to easily modify it and have a standard format across projects that can be enforced in config files. I won’t bother getting into the second two here, but more details can be found in the standard library.
Anything more to learn?
We haven’t even talked about fun still like creating custom Handlers, multiprocessing concerns, or Filters (more detailed way to process if something should be logged instead of just levels). There is always more to explore with the rich Python built-ins!
