From reading the docs about logging, I know I can use the basicConfig for example to set the format of logging records, such as:
import logging
logging.basicConfig(format="%(levelname)s - %(message)s")
for example.
My question is, can I specify different formats for different levels? What I'm trying to achieve is to format logging records to Azure Pipelines logging commands so that for examle the Python code:
logging.error("some error")
Will be printed as:
##[error]some error
Now I know I can use the %(levelname) but I don't want to rely on the correspondence between Azure Pipelines logging commands to Python's "logging" module. For example, in Python's logging there's info level, but not in Azure Pipelines.
There's an Azure doc specifically for Python logging which you might find useful. If I understand it correctly (I've never used Azure), you should be able to send it Python logs without doing anything special.
However, the log messages shown as part of this Azure chart are plainly a bit different. If you want your messages to appear like that, one option is to set some custom levels with the level-names Azure prefers. The doc you referenced has a section on that, although it warns against doing it in complex libraries.
If you want to reuse standard levels, but rename "info" -> "command" or some such, you're probably going to need a separate formatter for each such rename.
I use robot framework 3.0 under Python 2.7.8. Robot framework's documentation (http://robotframework.org/robotframework/latest/RobotFrameworkUserGuide.html#programmatic-logging-apis) states that
In addition to the new public logging API, Robot Framework offers a built-in support to Python's standard logging module. This works so that all messages that are received by the root logger of the module are automatically propagated to Robot Framework's log file.
I made a short library file to test this:
from logging import debug, error, info, warn
def try_logging():
info("This is merely a humble info message.")
debug("Most users never saw me.")
warn("I warn you about something.")
error("Something bad happened.")
My test case is:
*** Test Cases ***
Logtest
Try logging
When I run it it is a PASSED case, but nothing logged into the HTML log. The test execution log has the suit and the case and the keyword as it should but when I expand them nothing is logged but the "Start / End / Elapsed" line.
How could I forward the Python logger messages to Robot? As you can see the so called automatic propagation is not working automatically. My goal is to write a library that can be run with or without Robot Fw.
Ty for your help in advance.
After hours of code digging I managed to find the answer. I think it is worth sharing as it may be help you if you have some similar issue.
In my case I had some unused libraries imported. One of them was a class that was instantiated when Robot Framework imported the library file. This object had some logger settings that messed up the defaults, that is why I got no result in the robot log.
Without it I got the expected results and automatic propagation worked fine.
I'm writing a large hardware simulation library in Python3. For logging, I use the Python3 Logging module.
For controlling debug messages with method-level granularity, I learned "on the street" (ok, here at StackOverflow) to create sub-loggers within each method I wanted to log from:
sub_logger = logging.getChild("new_sublogger_name")
sub_logger.setLevel(logging.DEBUG)
# Sample debug message
sub_logger.debug("This is a debug message...")
By changing the call to setLevel(), the user is able to enable/disable debugging messages on a per-method basis.
Now the Boss Man don't like this approach. He's advocating a single-point at which all logging messages in the library can be enabled/disabled with the same method-level granularity. (This was to be accomplished by writing our own Python logging library BTW).
Not wanting to re-invent the logging wheel, I proposed to instead continue to use the Python Logging library, but instead use Filters to allow single-point control of logging messages.
Having not used Python Logging Filters very often, is there a consensus on using Filters vs Sublogger.setLevel() for this application? What are the pros/cons of each method?
I'm quite used to setLevel() after using it for a while, but that may be coloring my objectiveness. I DO NOT, however, wish to waste everyone's time writing another Python logging library.
I think the existing logging module does what you want. The trick is to separate the place where you call setLevel() (a configuration operation) from the places where you call getChild() (ongoing logging operations).
import logging
logger = logging.getLogger('mod1')
def fctn1():
logger.getChild('fctn1').debug('I am chatty')
# do stuff (notice, no setLevel)
def fctn2():
logger.getChild('fctn2').debug('I am even more chatty')
# do stuff (notice, no setLevel)
Notice there was no setLevel() there, which makes sense. Why call setLevel() every time and since when does a method know what logging level the user wants.
You set your logging levels in a configuration step at the beginning of the program. You can do it with the dictionary based configuration, a python module that does a bunch of setLevel() calls or even something you cook up with ini files or whatever. But basically it boils down to:
def config_logger():
logging.getLogger('abc.def').setLevel(logging.INFO)
logging.getLogger('mod1').setLevel(logging.WARN)
logging.getLogger('mod1.fctn1').setLeveL(logging.DEBUG)
(etc...)
Now, if you want to get fancy with filters, you can use them to inspect the stack frame and pull the method name out for you. But that gets more complicated.
I want to create task-specific log files that reflect what's happening during a particular operation, but I want these logging messages to also go to the primary Django log.
My current solution, which seems to work fine at first glance, is something like:
logger = getLogger("%s:%s" % (__name__, task_id))
handler = FileHandler(task_log_file)
logger.addHandler(handler)
# Work
logger.removeHandler(handler)
As I said, this works, but the main issue that occurs to me is that this logger isn't really temporary -- from what I've read of logging.Manager each logger will just hang around indefinitely until shutdown. In this case, when I'm done I know I won't use the logger again (okay, technically I might, but that will be rare), and assuming the system is stable this could be running through hundreds of thousands of tasks.
Is there a "right" way to do this?
You could have a way to mark-and-sweep your logging resource files, or just use a singleton pattern.
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.
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.
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.
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.
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
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.
Here's a functional logging library for Python named loguru.
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.
Logging essentially creates a searchable plain text database of print outputs with other meta data (timestamp, loglevel, line number, process etc.).
This is pure gold, I can run egrep over the log file after the python script has run.
I can tune my egrep pattern search to pick exactly what I am interested in and ignore the rest. This reduction of cognitive load and freedom to pick my egrep pattern later on by trial and error is the key benefit for me.
tail -f mylogfile.log | egrep "key_word1|key_word2"
Now throw in other cool things that print can't do (sending to socket, setting debug levels, logrotate, adding meta data etc.), you have every reason to prefer logging over plain print statements.
I tend to use print statements because it's lazy and easy, adding logging needs some boiler plate code, hey we have yasnippets (emacs) and ultisnips (vim) and other templating tools, so why give up logging for plain print statements!?
I would add to all other mentionned advantages that the print function in standard configuration is buffered. The flush may occure only at the end of the current block (the one where the print is).
This is true for any program launched in a non interactive shell (codebuild, gitlab-ci for instance) or whose output is redirected.
If for any reason the program is killed (kill -9, hard reset of the computer, …), you may be missing some line of logs if you used print for the same.
However, the logging library will ensure to flush the logs printed to stderr and stdout immediately at any call.