Develop Reference

Changing log levels per request in Flask

I have a Flask codebase with the standard library's logging module used throughout. I'd like to be able to increase and decrease the log level on a per-request basis. This way I could get trace or debug level information logged just on the specific requests I'm interested in debugging by setting a query string param or something similar to that.
I know that Flask has a before_request and after_request decorator that could be used to raise and lower the log level. However, it would be a bad deal if an unhandled exception is thrown and prevented the log level from being reset to its default level. Is there a reliable way to set and restore the log level per request?
I am also not very familiar with how the logging module interacts with threads and Flask. If a multi-threaded application server is running my Flask app is the logger shared across threads? I don't want to lower the log level in one request and have another, unrelated request execute with the first requests's logger.

You can use the Flask logger to create a child logger:
#app.before_request
def before_request():
if 'trace' in request.args:
trace_logger = app.logger.getChild('trace')
trace_logger.setLevel(logging.DEBUG)
g._trace_logger = trace_logger
It will re-use the parent's configuration. Specifically, it'll use the handlers and formatters you've configured but not the parent's log level or filters. The logger is also scoped to the request via the g object so it will not be available in other requests, making this design thread-safe.
Next, write a helper function:
def trace(*args, **kwargs):
logger = getattr(g, '_trace_logger', None)
if logger is not None:
logger.debug(*args, **kwargs)
You can use this function as if it were a logger call throughout your codebase.
If you are not running in trace mode the function is a no-op.

How to enable logging so messages kept internally until get/clear?

In an application I want to collect messages related to some dedicated part of the processing, and then show these messages later at user request. I would like to report severity (e.g. info, warning), time of message, etc., so for this I considered to use the Python standard logging module to collect the messages and related information. However, I don't want these messages to go to a console or file.
Is there a way to create a Python logger, using logging, where the messages are kept internally (in memory) only, until read out by the application. I would expect start of code like:
log = logging.getLogger('my_logger')
... some config of log for internal only; not to console
log.error('Just some error')
... some code to get/clear messages in log until now
I have tried to look in logging — Logging facility for Python, but most example are for immediate output to file or console, so an example for internal logging or reference is appreciated.

You should just use another handler. You could use a StreamHandler over an io.StringIO that would simply log to memory:
log = logging.getLogger('my_logger')
memlog = io.StringIO()
log.addHandler(logging.StreamHandler(memlog))
All logging sent to log can be found in memlog.getvalue()
Of course, this is just a simple Handler that concatenates everything in one single string, even if for versions >= 3.2 each record is terminated, by default with a \n. For more specific requirements, you could have a look at a QueueHandler or implement a dedicated Handler.
References: logging.handlers in the Python Standard Library reference manual.

syslog.syslog vs SysLogHandler

I'm looking at how to log to syslog from within my Python app, and I found there are two ways of doing it:
Using syslog.syslog() routines
Using the logger module SysLogHandler
Which is the best option to use, advantages/disadvantages of each one, etc, because I really don't know which one should I use.

syslog.syslog() can only be used to send messages to the local syslogd. SysLogHandler can be used as part of a comprehensive, configurable logging subsystem, and can log to remote machines.

The logging module is a more comprehensive solution that can potentially handle all of your log messages, and is very flexible. For instance, you can setup multiple handers for your logger and each can be set to log at a different level. You can have a SysLogHandler for sending errors to syslog, and a FileHandler for debugging logs, and an SMTPHandler to email the really critical messages to ops. You can also define a hierarchy of loggers within your modules, and each one has its own level so you can enable/disable messages from specific modules, such as:
import logging
logger = logging.getLogger('package.stable_module')
logger.setLevel(logging.WARNING)
And in another module:
import logging
logger = logging.getLogger('package.buggy_module')
logger.setLevel(logging.DEBUG)
The log messages in both of the these modules will be sent, depending on the level, to the 'package' logger and ultimately to the handlers you've defined. You can also add handlers directly to the module loggers, and so on. If you've followed along this far and are still interested, then I recommend jumping to the logging tutorial for more details.

So far, there is a disadvantage in logging.handlers.SysLogHander which is not mentioned yet. That is I can't set options like LOG_ODELAY or LOG_NOWAIT or LOG_PID. On the other hands, LOG_CONS and LOG_PERROR can be achieved with adding more handlers, and LOG_NDELAY is already set by default, because the connection opens when the handler is instantiated.

Python multiprocessing logging - why multiprocessing.get_logger

I've been struggled with multiprocessing logging for some time, and for many reasons.
One of my reason is, why another get_logger.
Of course I've seen this question and it seems the logger that multiprocessing.get_logger returns do some "process-shared locks" magic to make logging handling smooth.
So, today I looked into the multiprocessing code of Python 2.7 (/multiprocessing/util.py), and found that this logger is just a plain logging.Logger, and there's barely any magic around it.
Here's the description in Python documentation, right before the
get_logger function:
Some support for logging is available. Note, however, that the logging
package does not use process shared locks so it is possible (depending
on the handler type) for messages from different processes to get
mixed up.
So when you use a wrong logging handler, even the get_logger logger may go wrong?
I've used a program uses get_logger for logging for some time.
It prints logs to StreamHandler and (seems) never gets mixed up.
Now My theory is:
multiprocessing.get_logger don't do process-shared locks at all
StreamHandler works for multiprocessing, but FileHandler doesn't
major purpose of this get_logger logger is for tracking processes'
life-cycle, and provide a easy-to-get and ready-to-use logger
that already logs process's name/id kinds of stuff
Here's the question:
Is my theory right?
How/Why/When do you use this get_logger?

Yes, I believe you're right that multiprocessing.get_logger() doesn't do process-shared locks - as you say, the docs even state this. Despite all the upvotes, it looks like the question you link to is flawed in stating that it does (to give it the benefit of doubt, it was written over a decade ago - so perhaps that was the case at one point).
Why does multiprocessing.get_logger() exist then? The docs say that it:
Returns the logger used by multiprocessing. If necessary, a new one will be created.
When first created the logger has level logging.NOTSET and no default handler. Messages sent to this logger will not by default propagate to the root logger.
i.e. by default the multiprocessing module will not produce any log output since its logger's logging level is set to NOTSET so no log messages are produced.
If you were to have a problem with your code that you suspected to be an issue with multiprocessing, that lack of log output wouldn't be helpful for debugging, and that's what multiprocessing.get_logger() exists for - it returns the logger used by the multiprocessing module itself so that you can override the default logging configuration to get some logs from it and see what it's doing.
Since you asked for how to use multiprocessing.get_logger(), you'd call it like so and configure the logger in the usual fashion, for example:
logger = multiprocessing.get_logger()
formatter = logging.Formatter('[%(levelname)s/%(processName)s] %(message)s')
handler = logging.StreamHandler()
handler.setFormatter(formatter)
logger.addHandler(handler)
logger.setLevel(logging.INFO)
# now run your multiprocessing code
That said, you may actually want to use multiprocessing.log_to_stderr() instead for convenience - as per the docs:
This function performs a call to get_logger() but in addition to returning the logger created by get_logger, it adds a handler which sends output to sys.stderr using format '[%(levelname)s/%(processName)s] %(message)s'
i.e. it saves you needing to set up quite so much logging config yourself, and you can instead start debugging your multiprocessing issue with just:
logger = multiprocessing.log_to_stderr()
logger.setLevel(logging.INFO)
# now run your multiprocessing code
To reiterate though, that's just a normal module logger that's being configured and used, i.e. there's nothing special or process-safe about it. It just lets you see what's happening inside the multiprocessing module itself.

This answer is not about get_logger specifically, but perhaps you can use the approach suggested in this post? Note that the QueueHandler/QueueListener classes are available for earlier Python versions via the logutils package (available on PyPI, too).

How to add contextual information to log lines from multiprocessing workers?

I have a pool of worker processes (using multiprocessing.Pool) and want to log from these to a single log file. I am aware of logging servers, syslog, etc. but they all seem to require some changes to how my app is installed, monitored, logs processed etc. which I would like to avoid.
I am using CPython 2.6 on Linux.
Finally I stumbled into a solution which almost works for me. The basic idea is that you start a log listener process, set up a queue between it and the worker processes, and the workers log into the queue (using QueueHandler), and the listener then formats and serializes the log lines to a file.
This is all working so far according to the solution linked above.
But then I wanted to have the workers log some contextual information, for example a job token, for every log line. In pool.apply_async() method I can pass in the contextual info I want to be logged. Note that I am only interested in the contextual information while the worker is doing the specific job; when it is idle there should not be any contextual information if the worker wants to log something. So basically the log listener has log format specified as something like:
"%(job_token)s %(process)d %(asctime)s %(msg)"
and the workers are supposed to provide job_token as contextual info in the log record (the other format specifiers are standard).
I have looked at custom log filters. With custom filter I can create a filter when the job starts and apply the filter to the root logger, but I am using 3rd party modules which create their own loggers (typically at module import time), and my custom filter is not applied to them.
Is there a way to make this work in the above setup? Or is there some alternative way to make this work (remember that I would still prefer a single log file, no separate log servers, job-specific contextual information for worker log lines)?

Filters can be applied to handlers as well as loggers - so you can just apply the filter to your QueueHandler. If this handler is attached to the root logger in your processes, then any logging by third party modules should also be handled by the handler, so you should get the context in those logged events, too.