Livro de Receitas do Logging¶
- Autor
Vinay Sajip <vinay_sajip at red-dove dot com>
This page contains a number of recipes related to logging, which have been found useful in the past. For links to tutorial and reference information, please see Outros recursos.
Usando logging em vários módulos¶
Multiple calls to logging.getLogger('someLogger') return a reference to the
same logger object. This is true not only within the same module, but also
across modules as long as it is in the same Python interpreter process. It is
true for references to the same object; additionally, application code can
define and configure a parent logger in one module and create (but not
configure) a child logger in a separate module, and all logger calls to the
child will pass up to the parent. Here is a main module:
import logging
import auxiliary_module
# create logger with 'spam_application'
logger = logging.getLogger('spam_application')
logger.setLevel(logging.DEBUG)
# create file handler which logs even debug messages
fh = logging.FileHandler('spam.log')
fh.setLevel(logging.DEBUG)
# create console handler with a higher log level
ch = logging.StreamHandler()
ch.setLevel(logging.ERROR)
# create formatter and add it to the handlers
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
ch.setFormatter(formatter)
# add the handlers to the logger
logger.addHandler(fh)
logger.addHandler(ch)
logger.info('creating an instance of auxiliary_module.Auxiliary')
a = auxiliary_module.Auxiliary()
logger.info('created an instance of auxiliary_module.Auxiliary')
logger.info('calling auxiliary_module.Auxiliary.do_something')
a.do_something()
logger.info('finished auxiliary_module.Auxiliary.do_something')
logger.info('calling auxiliary_module.some_function()')
auxiliary_module.some_function()
logger.info('done with auxiliary_module.some_function()')
Here is the auxiliary module:
import logging
# create logger
module_logger = logging.getLogger('spam_application.auxiliary')
class Auxiliary:
def __init__(self):
self.logger = logging.getLogger('spam_application.auxiliary.Auxiliary')
self.logger.info('creating an instance of Auxiliary')
def do_something(self):
self.logger.info('doing something')
a = 1 + 1
self.logger.info('done doing something')
def some_function():
module_logger.info('received a call to "some_function"')
O resultado deve ser algo assim:
2005-03-23 23:47:11,663 - spam_application - INFO -
creating an instance of auxiliary_module.Auxiliary
2005-03-23 23:47:11,665 - spam_application.auxiliary.Auxiliary - INFO -
creating an instance of Auxiliary
2005-03-23 23:47:11,665 - spam_application - INFO -
created an instance of auxiliary_module.Auxiliary
2005-03-23 23:47:11,668 - spam_application - INFO -
calling auxiliary_module.Auxiliary.do_something
2005-03-23 23:47:11,668 - spam_application.auxiliary.Auxiliary - INFO -
doing something
2005-03-23 23:47:11,669 - spam_application.auxiliary.Auxiliary - INFO -
done doing something
2005-03-23 23:47:11,670 - spam_application - INFO -
finished auxiliary_module.Auxiliary.do_something
2005-03-23 23:47:11,671 - spam_application - INFO -
calling auxiliary_module.some_function()
2005-03-23 23:47:11,672 - spam_application.auxiliary - INFO -
received a call to 'some_function'
2005-03-23 23:47:11,673 - spam_application - INFO -
done with auxiliary_module.some_function()
Logging from multiple threads¶
Logging from multiple threads requires no special effort. The following example shows logging from the main (initial) thread and another thread:
import logging
import threading
import time
def worker(arg):
while not arg['stop']:
logging.debug('Hi from myfunc')
time.sleep(0.5)
def main():
logging.basicConfig(level=logging.DEBUG, format='%(relativeCreated)6d %(threadName)s %(message)s')
info = {'stop': False}
thread = threading.Thread(target=worker, args=(info,))
thread.start()
while True:
try:
logging.debug('Hello from main')
time.sleep(0.75)
except KeyboardInterrupt:
info['stop'] = True
break
thread.join()
if __name__ == '__main__':
main()
When run, the script should print something like the following:
0 Thread-1 Hi from myfunc
3 MainThread Hello from main
505 Thread-1 Hi from myfunc
755 MainThread Hello from main
1007 Thread-1 Hi from myfunc
1507 MainThread Hello from main
1508 Thread-1 Hi from myfunc
2010 Thread-1 Hi from myfunc
2258 MainThread Hello from main
2512 Thread-1 Hi from myfunc
3009 MainThread Hello from main
3013 Thread-1 Hi from myfunc
3515 Thread-1 Hi from myfunc
3761 MainThread Hello from main
4017 Thread-1 Hi from myfunc
4513 MainThread Hello from main
4518 Thread-1 Hi from myfunc
This shows the logging output interspersed as one might expect. This approach works for more threads than shown here, of course.
Multiple handlers and formatters¶
Loggers are plain Python objects. The addHandler() method has no
minimum or maximum quota for the number of handlers you may add. Sometimes it
will be beneficial for an application to log all messages of all severities to a
text file while simultaneously logging errors or above to the console. To set
this up, simply configure the appropriate handlers. The logging calls in the
application code will remain unchanged. Here is a slight modification to the
previous simple module-based configuration example:
import logging
logger = logging.getLogger('simple_example')
logger.setLevel(logging.DEBUG)
# create file handler which logs even debug messages
fh = logging.FileHandler('spam.log')
fh.setLevel(logging.DEBUG)
# create console handler with a higher log level
ch = logging.StreamHandler()
ch.setLevel(logging.ERROR)
# create formatter and add it to the handlers
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
ch.setFormatter(formatter)
fh.setFormatter(formatter)
# add the handlers to logger
logger.addHandler(ch)
logger.addHandler(fh)
# 'application' code
logger.debug('debug message')
logger.info('info message')
logger.warning('warn message')
logger.error('error message')
logger.critical('critical message')
Notice that the ‘application’ code does not care about multiple handlers. All that changed was the addition and configuration of a new handler named fh.
The ability to create new handlers with higher- or lower-severity filters can be
very helpful when writing and testing an application. Instead of using many
print statements for debugging, use logger.debug: Unlike the print
statements, which you will have to delete or comment out later, the logger.debug
statements can remain intact in the source code and remain dormant until you
need them again. At that time, the only change that needs to happen is to
modify the severity level of the logger and/or handler to debug.
Logging to multiple destinations¶
Let’s say you want to log to console and file with different message formats and in differing circumstances. Say you want to log messages with levels of DEBUG and higher to file, and those messages at level INFO and higher to the console. Let’s also assume that the file should contain timestamps, but the console messages should not. Here’s how you can achieve this:
import logging
# set up logging to file - see previous section for more details
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
datefmt='%m-%d %H:%M',
filename='/tmp/myapp.log',
filemode='w')
# define a Handler which writes INFO messages or higher to the sys.stderr
console = logging.StreamHandler()
console.setLevel(logging.INFO)
# set a format which is simpler for console use
formatter = logging.Formatter('%(name)-12s: %(levelname)-8s %(message)s')
# tell the handler to use this format
console.setFormatter(formatter)
# add the handler to the root logger
logging.getLogger('').addHandler(console)
# Now, we can log to the root logger, or any other logger. First the root...
logging.info('Jackdaws love my big sphinx of quartz.')
# Now, define a couple of other loggers which might represent areas in your
# application:
logger1 = logging.getLogger('myapp.area1')
logger2 = logging.getLogger('myapp.area2')
logger1.debug('Quick zephyrs blow, vexing daft Jim.')
logger1.info('How quickly daft jumping zebras vex.')
logger2.warning('Jail zesty vixen who grabbed pay from quack.')
logger2.error('The five boxing wizards jump quickly.')
When you run this, on the console you will see
root : INFO Jackdaws love my big sphinx of quartz.
myapp.area1 : INFO How quickly daft jumping zebras vex.
myapp.area2 : WARNING Jail zesty vixen who grabbed pay from quack.
myapp.area2 : ERROR The five boxing wizards jump quickly.
and in the file you will see something like
10-22 22:19 root INFO Jackdaws love my big sphinx of quartz.
10-22 22:19 myapp.area1 DEBUG Quick zephyrs blow, vexing daft Jim.
10-22 22:19 myapp.area1 INFO How quickly daft jumping zebras vex.
10-22 22:19 myapp.area2 WARNING Jail zesty vixen who grabbed pay from quack.
10-22 22:19 myapp.area2 ERROR The five boxing wizards jump quickly.
As you can see, the DEBUG message only shows up in the file. The other messages are sent to both destinations.
This example uses console and file handlers, but you can use any number and combination of handlers you choose.
Note that the above choice of log filename /tmp/myapp.log implies use of a
standard location for temporary files on POSIX systems. On Windows, you may need to
choose a different directory name for the log - just ensure that the directory exists
and that you have the permissions to create and update files in it.
Custom handling of levels¶
Sometimes, you might want to do something slightly different from the standard handling of levels in handlers, where all levels above a threshold get processed by a handler. To do this, you need to use filters. Let’s look at a scenario where you want to arrange things as follows:
Send messages of severity
INFOandWARNINGtosys.stdoutSend messages of severity
ERRORand above tosys.stderrSend messages of severity
DEBUGand above to fileapp.log
Suppose you configure logging with the following JSON:
{
"version": 1,
"disable_existing_loggers": false,
"formatters": {
"simple": {
"format": "%(levelname)-8s - %(message)s"
}
},
"handlers": {
"stdout": {
"class": "logging.StreamHandler",
"level": "INFO",
"formatter": "simple",
"stream": "ext://sys.stdout"
},
"stderr": {
"class": "logging.StreamHandler",
"level": "ERROR",
"formatter": "simple",
"stream": "ext://sys.stderr"
},
"file": {
"class": "logging.FileHandler",
"formatter": "simple",
"filename": "app.log",
"mode": "w"
}
},
"root": {
"level": "DEBUG",
"handlers": [
"stderr",
"stdout",
"file"
]
}
}
This configuration does almost what we want, except that sys.stdout would
show messages of severity ERROR and above as well as INFO and
WARNING messages. To prevent this, we can set up a filter which excludes
those messages and add it to the relevant handler. This can be configured by
adding a filters section parallel to formatters and handlers:
{
"filters": {
"warnings_and_below": {
"()" : "__main__.filter_maker",
"level": "WARNING"
}
}
}
and changing the section on the stdout handler to add it:
{
"stdout": {
"class": "logging.StreamHandler",
"level": "INFO",
"formatter": "simple",
"stream": "ext://sys.stdout",
"filters": ["warnings_and_below"]
}
}
A filter is just a function, so we can define the filter_maker (a factory
function) as follows:
def filter_maker(level):
level = getattr(logging, level)
def filter(record):
return record.levelno <= level
return filter
This converts the string argument passed in to a numeric level, and returns a
function which only returns True if the level of the passed in record is
at or below the specified level. Note that in this example I have defined the
filter_maker in a test script main.py that I run from the command line,
so its module will be __main__ - hence the __main__.filter_maker in the
filter configuration. You will need to change that if you define it in a
different module.
With the filter added, we can run main.py, which in full is:
import json
import logging
import logging.config
CONFIG = '''
{
"version": 1,
"disable_existing_loggers": false,
"formatters": {
"simple": {
"format": "%(levelname)-8s - %(message)s"
}
},
"filters": {
"warnings_and_below": {
"()" : "__main__.filter_maker",
"level": "WARNING"
}
},
"handlers": {
"stdout": {
"class": "logging.StreamHandler",
"level": "INFO",
"formatter": "simple",
"stream": "ext://sys.stdout",
"filters": ["warnings_and_below"]
},
"stderr": {
"class": "logging.StreamHandler",
"level": "ERROR",
"formatter": "simple",
"stream": "ext://sys.stderr"
},
"file": {
"class": "logging.FileHandler",
"formatter": "simple",
"filename": "app.log",
"mode": "w"
}
},
"root": {
"level": "DEBUG",
"handlers": [
"stderr",
"stdout",
"file"
]
}
}
'''
def filter_maker(level):
level = getattr(logging, level)
def filter(record):
return record.levelno <= level
return filter
logging.config.dictConfig(json.loads(CONFIG))
logging.debug('A DEBUG message')
logging.info('An INFO message')
logging.warning('A WARNING message')
logging.error('An ERROR message')
logging.critical('A CRITICAL message')
And after running it like this:
python main.py 2>stderr.log >stdout.log
We can see the results are as expected:
$ more *.log
::::::::::::::
app.log
::::::::::::::
DEBUG - A DEBUG message
INFO - An INFO message
WARNING - A WARNING message
ERROR - An ERROR message
CRITICAL - A CRITICAL message
::::::::::::::
stderr.log
::::::::::::::
ERROR - An ERROR message
CRITICAL - A CRITICAL message
::::::::::::::
stdout.log
::::::::::::::
INFO - An INFO message
WARNING - A WARNING message
Configuration server example¶
Here is an example of a module using the logging configuration server:
import logging
import logging.config
import time
import os
# read initial config file
logging.config.fileConfig('logging.conf')
# create and start listener on port 9999
t = logging.config.listen(9999)
t.start()
logger = logging.getLogger('simpleExample')
try:
# loop through logging calls to see the difference
# new configurations make, until Ctrl+C is pressed
while True:
logger.debug('debug message')
logger.info('info message')
logger.warning('warn message')
logger.error('error message')
logger.critical('critical message')
time.sleep(5)
except KeyboardInterrupt:
# cleanup
logging.config.stopListening()
t.join()
And here is a script that takes a filename and sends that file to the server, properly preceded with the binary-encoded length, as the new logging configuration:
#!/usr/bin/env python
import socket, sys, struct
with open(sys.argv[1], 'rb') as f:
data_to_send = f.read()
HOST = 'localhost'
PORT = 9999
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
print('connecting...')
s.connect((HOST, PORT))
print('sending config...')
s.send(struct.pack('>L', len(data_to_send)))
s.send(data_to_send)
s.close()
print('complete')
Dealing with handlers that block¶
Sometimes you have to get your logging handlers to do their work without blocking the thread you’re logging from. This is common in web applications, though of course it also occurs in other scenarios.
A common culprit which demonstrates sluggish behaviour is the
SMTPHandler: sending emails can take a long time, for a
number of reasons outside the developer’s control (for example, a poorly
performing mail or network infrastructure). But almost any network-based
handler can block: Even a SocketHandler operation may do a
DNS query under the hood which is too slow (and this query can be deep in the
socket library code, below the Python layer, and outside your control).
One solution is to use a two-part approach. For the first part, attach only a
QueueHandler to those loggers which are accessed from
performance-critical threads. They simply write to their queue, which can be
sized to a large enough capacity or initialized with no upper bound to their
size. The write to the queue will typically be accepted quickly, though you
will probably need to catch the queue.Full exception as a precaution
in your code. If you are a library developer who has performance-critical
threads in their code, be sure to document this (together with a suggestion to
attach only QueueHandlers to your loggers) for the benefit of other
developers who will use your code.
The second part of the solution is QueueListener, which has been
designed as the counterpart to QueueHandler. A
QueueListener is very simple: it’s passed a queue and some handlers,
and it fires up an internal thread which listens to its queue for LogRecords
sent from QueueHandlers (or any other source of LogRecords, for that
matter). The LogRecords are removed from the queue and passed to the
handlers for processing.
The advantage of having a separate QueueListener class is that you
can use the same instance to service multiple QueueHandlers. This is more
resource-friendly than, say, having threaded versions of the existing handler
classes, which would eat up one thread per handler for no particular benefit.
An example of using these two classes follows (imports omitted):
que = queue.Queue(-1) # no limit on size
queue_handler = QueueHandler(que)
handler = logging.StreamHandler()
listener = QueueListener(que, handler)
root = logging.getLogger()
root.addHandler(queue_handler)
formatter = logging.Formatter('%(threadName)s: %(message)s')
handler.setFormatter(formatter)
listener.start()
# The log output will display the thread which generated
# the event (the main thread) rather than the internal
# thread which monitors the internal queue. This is what
# you want to happen.
root.warning('Look out!')
listener.stop()
which, when run, will produce:
MainThread: Look out!
Nota
Although the earlier discussion wasn’t specifically talking about
async code, but rather about slow logging handlers, it should be noted that
when logging from async code, network and even file handlers could lead to
problems (blocking the event loop) because some logging is done from
asyncio internals. It might be best, if any async code is used in an
application, to use the above approach for logging, so that any blocking code
runs only in the QueueListener thread.
Alterado na versão 3.5: Prior to Python 3.5, the QueueListener always passed every message
received from the queue to every handler it was initialized with. (This was
because it was assumed that level filtering was all done on the other side,
where the queue is filled.) From 3.5 onwards, this behaviour can be changed
by passing a keyword argument respect_handler_level=True to the
listener’s constructor. When this is done, the listener compares the level
of each message with the handler’s level, and only passes a message to a
handler if it’s appropriate to do so.
Sending and receiving logging events across a network¶
Let’s say you want to send logging events across a network, and handle them at
the receiving end. A simple way of doing this is attaching a
SocketHandler instance to the root logger at the sending end:
import logging, logging.handlers
rootLogger = logging.getLogger('')
rootLogger.setLevel(logging.DEBUG)
socketHandler = logging.handlers.SocketHandler('localhost',
logging.handlers.DEFAULT_TCP_LOGGING_PORT)
# don't bother with a formatter, since a socket handler sends the event as
# an unformatted pickle
rootLogger.addHandler(socketHandler)
# Now, we can log to the root logger, or any other logger. First the root...
logging.info('Jackdaws love my big sphinx of quartz.')
# Now, define a couple of other loggers which might represent areas in your
# application:
logger1 = logging.getLogger('myapp.area1')
logger2 = logging.getLogger('myapp.area2')
logger1.debug('Quick zephyrs blow, vexing daft Jim.')
logger1.info('How quickly daft jumping zebras vex.')
logger2.warning('Jail zesty vixen who grabbed pay from quack.')
logger2.error('The five boxing wizards jump quickly.')
At the receiving end, you can set up a receiver using the socketserver
module. Here is a basic working example:
import pickle
import logging
import logging.handlers
import socketserver
import struct
class LogRecordStreamHandler(socketserver.StreamRequestHandler):
"""Handler for a streaming logging request.
This basically logs the record using whatever logging policy is
configured locally.
"""
def handle(self):
"""
Handle multiple requests - each expected to be a 4-byte length,
followed by the LogRecord in pickle format. Logs the record
according to whatever policy is configured locally.
"""
while True:
chunk = self.connection.recv(4)
if len(chunk) < 4:
break
slen = struct.unpack('>L', chunk)[0]
chunk = self.connection.recv(slen)
while len(chunk) < slen:
chunk = chunk + self.connection.recv(slen - len(chunk))
obj = self.unPickle(chunk)
record = logging.makeLogRecord(obj)
self.handleLogRecord(record)
def unPickle(self, data):
return pickle.loads(data)
def handleLogRecord(self, record):
# if a name is specified, we use the named logger rather than the one
# implied by the record.
if self.server.logname is not None:
name = self.server.logname
else:
name = record.name
logger = logging.getLogger(name)
# N.B. EVERY record gets logged. This is because Logger.handle
# is normally called AFTER logger-level filtering. If you want
# to do filtering, do it at the client end to save wasting
# cycles and network bandwidth!
logger.handle(record)
class LogRecordSocketReceiver(socketserver.ThreadingTCPServer):
"""
Simple TCP socket-based logging receiver suitable for testing.
"""
allow_reuse_address = True
def __init__(self, host='localhost',
port=logging.handlers.DEFAULT_TCP_LOGGING_PORT,
handler=LogRecordStreamHandler):
socketserver.ThreadingTCPServer.__init__(self, (host, port), handler)
self.abort = 0
self.timeout = 1
self.logname = None
def serve_until_stopped(self):
import select
abort = 0
while not abort:
rd, wr, ex = select.select([self.socket.fileno()],
[], [],
self.timeout)
if rd:
self.handle_request()
abort = self.abort
def main():
logging.basicConfig(
format='%(relativeCreated)5d %(name)-15s %(levelname)-8s %(message)s')
tcpserver = LogRecordSocketReceiver()
print('About to start TCP server...')
tcpserver.serve_until_stopped()
if __name__ == '__main__':
main()
First run the server, and then the client. On the client side, nothing is printed on the console; on the server side, you should see something like:
About to start TCP server...
59 root INFO Jackdaws love my big sphinx of quartz.
59 myapp.area1 DEBUG Quick zephyrs blow, vexing daft Jim.
69 myapp.area1 INFO How quickly daft jumping zebras vex.
69 myapp.area2 WARNING Jail zesty vixen who grabbed pay from quack.
69 myapp.area2 ERROR The five boxing wizards jump quickly.
Note that there are some security issues with pickle in some scenarios. If
these affect you, you can use an alternative serialization scheme by overriding
the makePickle() method and implementing your
alternative there, as well as adapting the above script to use your alternative
serialization.
Running a logging socket listener in production¶
To run a logging listener in production, you may need to use a process-management tool such as Supervisor. Here is a Gist which provides the bare-bones files to run the above functionality using Supervisor. It consists of the following files:
Arquivo |
Propósito |
|---|---|
|
A Bash script to prepare the environment for testing |
|
The Supervisor configuration file, which has entries for the listener and a multi-process web application |
|
A Bash script to ensure that Supervisor is running with the above configuration |
|
The socket listener program which receives log events and records them to a file |
|
A simple web application which performs logging via a socket connected to the listener |
|
A JSON configuration file for the web application |
|
A Python script to exercise the web application |
The web application uses Gunicorn, which is a popular web application server that starts multiple worker processes to handle requests. This example setup shows how the workers can write to the same log file without conflicting with one another — they all go through the socket listener.
To test these files, do the following in a POSIX environment:
Download the Gist as a ZIP archive using the Download ZIP button.
Unzip the above files from the archive into a scratch directory.
In the scratch directory, run
bash prepare.shto get things ready. This creates arunsubdirectory to contain Supervisor-related and log files, and avenvsubdirectory to contain a virtual environment into whichbottle,gunicornandsupervisorare installed.Run
bash ensure_app.shto ensure that Supervisor is running with the above configuration.Run
venv/bin/python client.pyto exercise the web application, which will lead to records being written to the log.Inspect the log files in the
runsubdirectory. You should see the most recent log lines in files matching the patternapp.log*. They won’t be in any particular order, since they have been handled concurrently by different worker processes in a non-deterministic way.You can shut down the listener and the web application by running
venv/bin/supervisorctl -c supervisor.conf shutdown.
You may need to tweak the configuration files in the unlikely event that the configured ports clash with something else in your test environment.
Adding contextual information to your logging output¶
Sometimes you want logging output to contain contextual information in
addition to the parameters passed to the logging call. For example, in a
networked application, it may be desirable to log client-specific information
in the log (e.g. remote client’s username, or IP address). Although you could
use the extra parameter to achieve this, it’s not always convenient to pass
the information in this way. While it might be tempting to create
Logger instances on a per-connection basis, this is not a good idea
because these instances are not garbage collected. While this is not a problem
in practice, when the number of Logger instances is dependent on the
level of granularity you want to use in logging an application, it could
be hard to manage if the number of Logger instances becomes
effectively unbounded.
Using LoggerAdapters to impart contextual information¶
An easy way in which you can pass contextual information to be output along
with logging event information is to use the LoggerAdapter class.
This class is designed to look like a Logger, so that you can call
debug(), info(), warning(), error(),
exception(), critical() and log(). These methods have the
same signatures as their counterparts in Logger, so you can use the
two types of instances interchangeably.
When you create an instance of LoggerAdapter, you pass it a
Logger instance and a dict-like object which contains your contextual
information. When you call one of the logging methods on an instance of
LoggerAdapter, it delegates the call to the underlying instance of
Logger passed to its constructor, and arranges to pass the contextual
information in the delegated call. Here’s a snippet from the code of
LoggerAdapter:
def debug(self, msg, /, *args, **kwargs):
"""
Delegate a debug call to the underlying logger, after adding
contextual information from this adapter instance.
"""
msg, kwargs = self.process(msg, kwargs)
self.logger.debug(msg, *args, **kwargs)
The process() method of LoggerAdapter is where the
contextual information is added to the logging output. It’s passed the message
and keyword arguments of the logging call, and it passes back (potentially)
modified versions of these to use in the call to the underlying logger. The
default implementation of this method leaves the message alone, but inserts
an ‘extra’ key in the keyword argument whose value is the dict-like object
passed to the constructor. Of course, if you had passed an ‘extra’ keyword
argument in the call to the adapter, it will be silently overwritten.
The advantage of using ‘extra’ is that the values in the dict-like object are
merged into the LogRecord instance’s __dict__, allowing you to use
customized strings with your Formatter instances which know about
the keys of the dict-like object. If you need a different method, e.g. if you
want to prepend or append the contextual information to the message string,
you just need to subclass LoggerAdapter and override
process() to do what you need. Here is a simple example:
class CustomAdapter(logging.LoggerAdapter):
"""
This example adapter expects the passed in dict-like object to have a
'connid' key, whose value in brackets is prepended to the log message.
"""
def process(self, msg, kwargs):
return '[%s] %s' % (self.extra['connid'], msg), kwargs
which you can use like this:
logger = logging.getLogger(__name__)
adapter = CustomAdapter(logger, {'connid': some_conn_id})
Then any events that you log to the adapter will have the value of
some_conn_id prepended to the log messages.
Using objects other than dicts to pass contextual information¶
You don’t need to pass an actual dict to a LoggerAdapter - you could
pass an instance of a class which implements __getitem__ and __iter__ so
that it looks like a dict to logging. This would be useful if you want to
generate values dynamically (whereas the values in a dict would be constant).
Using Filters to impart contextual information¶
You can also add contextual information to log output using a user-defined
Filter. Filter instances are allowed to modify the LogRecords
passed to them, including adding additional attributes which can then be output
using a suitable format string, or if needed a custom Formatter.
For example in a web application, the request being processed (or at least,
the interesting parts of it) can be stored in a threadlocal
(threading.local) variable, and then accessed from a Filter to
add, say, information from the request - say, the remote IP address and remote
user’s username - to the LogRecord, using the attribute names ‘ip’ and
‘user’ as in the LoggerAdapter example above. In that case, the same format
string can be used to get similar output to that shown above. Here’s an example
script:
import logging
from random import choice
class ContextFilter(logging.Filter):
"""
This is a filter which injects contextual information into the log.
Rather than use actual contextual information, we just use random
data in this demo.
"""
USERS = ['jim', 'fred', 'sheila']
IPS = ['123.231.231.123', '127.0.0.1', '192.168.0.1']
def filter(self, record):
record.ip = choice(ContextFilter.IPS)
record.user = choice(ContextFilter.USERS)
return True
if __name__ == '__main__':
levels = (logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR, logging.CRITICAL)
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)-15s %(name)-5s %(levelname)-8s IP: %(ip)-15s User: %(user)-8s %(message)s')
a1 = logging.getLogger('a.b.c')
a2 = logging.getLogger('d.e.f')
f = ContextFilter()
a1.addFilter(f)
a2.addFilter(f)
a1.debug('A debug message')
a1.info('An info message with %s', 'some parameters')
for x in range(10):
lvl = choice(levels)
lvlname = logging.getLevelName(lvl)
a2.log(lvl, 'A message at %s level with %d %s', lvlname, 2, 'parameters')
which, when run, produces something like:
2010-09-06 22:38:15,292 a.b.c DEBUG IP: 123.231.231.123 User: fred A debug message
2010-09-06 22:38:15,300 a.b.c INFO IP: 192.168.0.1 User: sheila An info message with some parameters
2010-09-06 22:38:15,300 d.e.f CRITICAL IP: 127.0.0.1 User: sheila A message at CRITICAL level with 2 parameters
2010-09-06 22:38:15,300 d.e.f ERROR IP: 127.0.0.1 User: jim A message at ERROR level with 2 parameters
2010-09-06 22:38:15,300 d.e.f DEBUG IP: 127.0.0.1 User: sheila A message at DEBUG level with 2 parameters
2010-09-06 22:38:15,300 d.e.f ERROR IP: 123.231.231.123 User: fred A message at ERROR level with 2 parameters
2010-09-06 22:38:15,300 d.e.f CRITICAL IP: 192.168.0.1 User: jim A message at CRITICAL level with 2 parameters
2010-09-06 22:38:15,300 d.e.f CRITICAL IP: 127.0.0.1 User: sheila A message at CRITICAL level with 2 parameters
2010-09-06 22:38:15,300 d.e.f DEBUG IP: 192.168.0.1 User: jim A message at DEBUG level with 2 parameters
2010-09-06 22:38:15,301 d.e.f ERROR IP: 127.0.0.1 User: sheila A message at ERROR level with 2 parameters
2010-09-06 22:38:15,301 d.e.f DEBUG IP: 123.231.231.123 User: fred A message at DEBUG level with 2 parameters
2010-09-06 22:38:15,301 d.e.f INFO IP: 123.231.231.123 User: fred A message at INFO level with 2 parameters
Use of contextvars¶
Since Python 3.7, the contextvars module has provided context-local storage
which works for both threading and asyncio processing needs. This type
of storage may thus be generally preferable to thread-locals. The following example
shows how, in a multi-threaded environment, logs can populated with contextual
information such as, for example, request attributes handled by web applications.
For the purposes of illustration, say that you have different web applications, each independent of the other but running in the same Python process and using a library common to them. How can each of these applications have their own log, where all logging messages from the library (and other request processing code) are directed to the appropriate application’s log file, while including in the log additional contextual information such as client IP, HTTP request method and client username?
Let’s assume that the library can be simulated by the following code:
# webapplib.py
import logging
import time
logger = logging.getLogger(__name__)
def useful():
# Just a representative event logged from the library
logger.debug('Hello from webapplib!')
# Just sleep for a bit so other threads get to run
time.sleep(0.01)
We can simulate the multiple web applications by means of two simple classes,
Request and WebApp. These simulate how real threaded web applications work -
each request is handled by a thread:
# main.py
import argparse
from contextvars import ContextVar
import logging
import os
from random import choice
import threading
import webapplib
logger = logging.getLogger(__name__)
root = logging.getLogger()
root.setLevel(logging.DEBUG)
class Request:
"""
A simple dummy request class which just holds dummy HTTP request method,
client IP address and client username
"""
def __init__(self, method, ip, user):
self.method = method
self.ip = ip
self.user = user
# A dummy set of requests which will be used in the simulation - we'll just pick
# from this list randomly. Note that all GET requests are from 192.168.2.XXX
# addresses, whereas POST requests are from 192.16.3.XXX addresses. Three users
# are represented in the sample requests.
REQUESTS = [
Request('GET', '192.168.2.20', 'jim'),
Request('POST', '192.168.3.20', 'fred'),
Request('GET', '192.168.2.21', 'sheila'),
Request('POST', '192.168.3.21', 'jim'),
Request('GET', '192.168.2.22', 'fred'),
Request('POST', '192.168.3.22', 'sheila'),
]
# Note that the format string includes references to request context information
# such as HTTP method, client IP and username
formatter = logging.Formatter('%(threadName)-11s %(appName)s %(name)-9s %(user)-6s %(ip)s %(method)-4s %(message)s')
# Create our context variables. These will be filled at the start of request
# processing, and used in the logging that happens during that processing
ctx_request = ContextVar('request')
ctx_appname = ContextVar('appname')
class InjectingFilter(logging.Filter):
"""
A filter which injects context-specific information into logs and ensures
that only information for a specific webapp is included in its log
"""
def __init__(self, app):
self.app = app
def filter(self, record):
request = ctx_request.get()
record.method = request.method
record.ip = request.ip
record.user = request.user
record.appName = appName = ctx_appname.get()
return appName == self.app.name
class WebApp:
"""
A dummy web application class which has its own handler and filter for a
webapp-specific log.
"""
def __init__(self, name):
self.name = name
handler = logging.FileHandler(name + '.log', 'w')
f = InjectingFilter(self)
handler.setFormatter(formatter)
handler.addFilter(f)
root.addHandler(handler)
self.num_requests = 0
def process_request(self, request):
"""
This is the dummy method for processing a request. It's called on a
different thread for every request. We store the context information into
the context vars before doing anything else.
"""
ctx_request.set(request)
ctx_appname.set(self.name)
self.num_requests += 1
logger.debug('Request processing started')
webapplib.useful()
logger.debug('Request processing finished')
def main():
fn = os.path.splitext(os.path.basename(__file__))[0]
adhf = argparse.ArgumentDefaultsHelpFormatter
ap = argparse.ArgumentParser(formatter_class=adhf, prog=fn,
description='Simulate a couple of web '
'applications handling some '
'requests, showing how request '
'context can be used to '
'populate logs')
aa = ap.add_argument
aa('--count', '-c', type=int, default=100, help='How many requests to simulate')
options = ap.parse_args()
# Create the dummy webapps and put them in a list which we can use to select
# from randomly
app1 = WebApp('app1')
app2 = WebApp('app2')
apps = [app1, app2]
threads = []
# Add a common handler which will capture all events
handler = logging.FileHandler('app.log', 'w')
handler.setFormatter(formatter)
root.addHandler(handler)
# Generate calls to process requests
for i in range(options.count):
try:
# Pick an app at random and a request for it to process
app = choice(apps)
request = choice(REQUESTS)
# Process the request in its own thread
t = threading.Thread(target=app.process_request, args=(request,))
threads.append(t)
t.start()
except KeyboardInterrupt:
break
# Wait for the threads to terminate
for t in threads:
t.join()
for app in apps:
print('%s processed %s requests' % (app.name, app.num_requests))
if __name__ == '__main__':
main()
If you run the above, you should find that roughly half the requests go
into app1.log and the rest into app2.log, and the all the requests are
logged to app.log. Each webapp-specific log will contain only log entries for
only that webapp, and the request information will be displayed consistently in the
log (i.e. the information in each dummy request will always appear together in a log
line). This is illustrated by the following shell output:
~/logging-contextual-webapp$ python main.py
app1 processed 51 requests
app2 processed 49 requests
~/logging-contextual-webapp$ wc -l *.log
153 app1.log
147 app2.log
300 app.log
600 total
~/logging-contextual-webapp$ head -3 app1.log
Thread-3 (process_request) app1 __main__ jim 192.168.3.21 POST Request processing started
Thread-3 (process_request) app1 webapplib jim 192.168.3.21 POST Hello from webapplib!
Thread-5 (process_request) app1 __main__ jim 192.168.3.21 POST Request processing started
~/logging-contextual-webapp$ head -3 app2.log
Thread-1 (process_request) app2 __main__ sheila 192.168.2.21 GET Request processing started
Thread-1 (process_request) app2 webapplib sheila 192.168.2.21 GET Hello from webapplib!
Thread-2 (process_request) app2 __main__ jim 192.168.2.20 GET Request processing started
~/logging-contextual-webapp$ head app.log
Thread-1 (process_request) app2 __main__ sheila 192.168.2.21 GET Request processing started
Thread-1 (process_request) app2 webapplib sheila 192.168.2.21 GET Hello from webapplib!
Thread-2 (process_request) app2 __main__ jim 192.168.2.20 GET Request processing started
Thread-3 (process_request) app1 __main__ jim 192.168.3.21 POST Request processing started
Thread-2 (process_request) app2 webapplib jim 192.168.2.20 GET Hello from webapplib!
Thread-3 (process_request) app1 webapplib jim 192.168.3.21 POST Hello from webapplib!
Thread-4 (process_request) app2 __main__ fred 192.168.2.22 GET Request processing started
Thread-5 (process_request) app1 __main__ jim 192.168.3.21 POST Request processing started
Thread-4 (process_request) app2 webapplib fred 192.168.2.22 GET Hello from webapplib!
Thread-6 (process_request) app1 __main__ jim 192.168.3.21 POST Request processing started
~/logging-contextual-webapp$ grep app1 app1.log | wc -l
153
~/logging-contextual-webapp$ grep app2 app2.log | wc -l
147
~/logging-contextual-webapp$ grep app1 app.log | wc -l
153
~/logging-contextual-webapp$ grep app2 app.log | wc -l
147
Imparting contextual information in handlers¶
Each Handler has its own chain of filters.
If you want to add contextual information to a LogRecord without leaking
it to other handlers, you can use a filter that returns
a new LogRecord instead of modifying it in-place, as shown in the following script:
import copy
import logging
def filter(record: logging.LogRecord):
record = copy.copy(record)
record.user = 'jim'
return record
if __name__ == '__main__':
logger = logging.getLogger()
logger.setLevel(logging.INFO)
handler = logging.StreamHandler()
formatter = logging.Formatter('%(message)s from %(user)-8s')
handler.setFormatter(formatter)
handler.addFilter(filter)
logger.addHandler(handler)
logger.info('A log message')
Logging to a single file from multiple processes¶
Although logging is thread-safe, and logging to a single file from multiple
threads in a single process is supported, logging to a single file from
multiple processes is not supported, because there is no standard way to
serialize access to a single file across multiple processes in Python. If you
need to log to a single file from multiple processes, one way of doing this is
to have all the processes log to a SocketHandler, and have a
separate process which implements a socket server which reads from the socket
and logs to file. (If you prefer, you can dedicate one thread in one of the
existing processes to perform this function.)
This section documents this approach in more detail and
includes a working socket receiver which can be used as a starting point for you
to adapt in your own applications.
You could also write your own handler which uses the Lock
class from the multiprocessing module to serialize access to the
file from your processes. The existing FileHandler and subclasses do
not make use of multiprocessing at present, though they may do so in the
future. Note that at present, the multiprocessing module does not provide
working lock functionality on all platforms (see
https://bugs.python.org/issue3770).
Alternatively, you can use a Queue and a QueueHandler to send
all logging events to one of the processes in your multi-process application.
The following example script demonstrates how you can do this; in the example
a separate listener process listens for events sent by other processes and logs
them according to its own logging configuration. Although the example only
demonstrates one way of doing it (for example, you may want to use a listener
thread rather than a separate listener process – the implementation would be
analogous) it does allow for completely different logging configurations for
the listener and the other processes in your application, and can be used as
the basis for code meeting your own specific requirements:
# You'll need these imports in your own code
import logging
import logging.handlers
import multiprocessing
# Next two import lines for this demo only
from random import choice, random
import time
#
# Because you'll want to define the logging configurations for listener and workers, the
# listener and worker process functions take a configurer parameter which is a callable
# for configuring logging for that process. These functions are also passed the queue,
# which they use for communication.
#
# In practice, you can configure the listener however you want, but note that in this
# simple example, the listener does not apply level or filter logic to received records.
# In practice, you would probably want to do this logic in the worker processes, to avoid
# sending events which would be filtered out between processes.
#
# The size of the rotated files is made small so you can see the results easily.
def listener_configurer():
root = logging.getLogger()
h = logging.handlers.RotatingFileHandler('mptest.log', 'a', 300, 10)
f = logging.Formatter('%(asctime)s %(processName)-10s %(name)s %(levelname)-8s %(message)s')
h.setFormatter(f)
root.addHandler(h)
# This is the listener process top-level loop: wait for logging events
# (LogRecords)on the queue and handle them, quit when you get a None for a
# LogRecord.
def listener_process(queue, configurer):
configurer()
while True:
try:
record = queue.get()
if record is None: # We send this as a sentinel to tell the listener to quit.
break
logger = logging.getLogger(record.name)
logger.handle(record) # No level or filter logic applied - just do it!
except Exception:
import sys, traceback
print('Whoops! Problem:', file=sys.stderr)
traceback.print_exc(file=sys.stderr)
# Arrays used for random selections in this demo
LEVELS = [logging.DEBUG, logging.INFO, logging.WARNING,
logging.ERROR, logging.CRITICAL]
LOGGERS = ['a.b.c', 'd.e.f']
MESSAGES = [
'Random message #1',
'Random message #2',
'Random message #3',
]
# The worker configuration is done at the start of the worker process run.
# Note that on Windows you can't rely on fork semantics, so each process
# will run the logging configuration code when it starts.
def worker_configurer(queue):
h = logging.handlers.QueueHandler(queue) # Just the one handler needed
root = logging.getLogger()
root.addHandler(h)
# send all messages, for demo; no other level or filter logic applied.
root.setLevel(logging.DEBUG)
# This is the worker process top-level loop, which just logs ten events with
# random intervening delays before terminating.
# The print messages are just so you know it's doing something!
def worker_process(queue, configurer):
configurer(queue)
name = multiprocessing.current_process().name
print('Worker started: %s' % name)
for i in range(10):
time.sleep(random())
logger = logging.getLogger(choice(LOGGERS))
level = choice(LEVELS)
message = choice(MESSAGES)
logger.log(level, message)
print('Worker finished: %s' % name)
# Here's where the demo gets orchestrated. Create the queue, create and start
# the listener, create ten workers and start them, wait for them to finish,
# then send a None to the queue to tell the listener to finish.
def main():
queue = multiprocessing.Queue(-1)
listener = multiprocessing.Process(target=listener_process,
args=(queue, listener_configurer))
listener.start()
workers = []
for i in range(10):
worker = multiprocessing.Process(target=worker_process,
args=(queue, worker_configurer))
workers.append(worker)
worker.start()
for w in workers:
w.join()
queue.put_nowait(None)
listener.join()
if __name__ == '__main__':
main()
A variant of the above script keeps the logging in the main process, in a separate thread:
import logging
import logging.config
import logging.handlers
from multiprocessing import Process, Queue
import random
import threading
import time
def logger_thread(q):
while True:
record = q.get()
if record is None:
break
logger = logging.getLogger(record.name)
logger.handle(record)
def worker_process(q):
qh = logging.handlers.QueueHandler(q)
root = logging.getLogger()
root.setLevel(logging.DEBUG)
root.addHandler(qh)
levels = [logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR,
logging.CRITICAL]
loggers = ['foo', 'foo.bar', 'foo.bar.baz',
'spam', 'spam.ham', 'spam.ham.eggs']
for i in range(100):
lvl = random.choice(levels)
logger = logging.getLogger(random.choice(loggers))
logger.log(lvl, 'Message no. %d', i)
if __name__ == '__main__':
q = Queue()
d = {
'version': 1,
'formatters': {
'detailed': {
'class': 'logging.Formatter',
'format': '%(asctime)s %(name)-15s %(levelname)-8s %(processName)-10s %(message)s'
}
},
'handlers': {
'console': {
'class': 'logging.StreamHandler',
'level': 'INFO',
},
'file': {
'class': 'logging.FileHandler',
'filename': 'mplog.log',
'mode': 'w',
'formatter': 'detailed',
},
'foofile': {
'class': 'logging.FileHandler',
'filename': 'mplog-foo.log',
'mode': 'w',
'formatter': 'detailed',
},
'errors': {
'class': 'logging.FileHandler',
'filename': 'mplog-errors.log',
'mode': 'w',
'level': 'ERROR',
'formatter': 'detailed',
},
},
'loggers': {
'foo': {
'handlers': ['foofile']
}
},
'root': {
'level': 'DEBUG',
'handlers': ['console', 'file', 'errors']
},
}
workers = []
for i in range(5):
wp = Process(target=worker_process, name='worker %d' % (i + 1), args=(q,))
workers.append(wp)
wp.start()
logging.config.dictConfig(d)
lp = threading.Thread(target=logger_thread, args=(q,))
lp.start()
# At this point, the main process could do some useful work of its own
# Once it's done that, it can wait for the workers to terminate...
for wp in workers:
wp.join()
# And now tell the logging thread to finish up, too
q.put(None)
lp.join()
This variant shows how you can e.g. apply configuration for particular loggers
- e.g. the foo logger has a special handler which stores all events in the
foo subsystem in a file mplog-foo.log. This will be used by the logging
machinery in the main process (even though the logging events are generated in
the worker processes) to direct the messages to the appropriate destinations.
Using concurrent.futures.ProcessPoolExecutor¶
If you want to use concurrent.futures.ProcessPoolExecutor to start
your worker processes, you need to create the queue slightly differently.
Instead of
queue = multiprocessing.Queue(-1)
you should use
queue = multiprocessing.Manager().Queue(-1) # also works with the examples above
and you can then replace the worker creation from this:
workers = []
for i in range(10):
worker = multiprocessing.Process(target=worker_process,
args=(queue, worker_configurer))
workers.append(worker)
worker.start()
for w in workers:
w.join()
to this (remembering to first import concurrent.futures):
with concurrent.futures.ProcessPoolExecutor(max_workers=10) as executor:
for i in range(10):
executor.submit(worker_process, queue, worker_configurer)
Deploying Web applications using Gunicorn and uWSGI¶
When deploying Web applications using Gunicorn or uWSGI (or similar), multiple worker
processes are created to handle client requests. In such environments, avoid creating
file-based handlers directly in your web application. Instead, use a
SocketHandler to log from the web application to a listener in a separate
process. This can be set up using a process management tool such as Supervisor - see
Running a logging socket listener in production for more details.
Using file rotation¶
Sometimes you want to let a log file grow to a certain size, then open a new
file and log to that. You may want to keep a certain number of these files, and
when that many files have been created, rotate the files so that the number of
files and the size of the files both remain bounded. For this usage pattern, the
logging package provides a RotatingFileHandler:
import glob
import logging
import logging.handlers
LOG_FILENAME = 'logging_rotatingfile_example.out'
# Set up a specific logger with our desired output level
my_logger = logging.getLogger('MyLogger')
my_logger.setLevel(logging.DEBUG)
# Add the log message handler to the logger
handler = logging.handlers.RotatingFileHandler(
LOG_FILENAME, maxBytes=20, backupCount=5)
my_logger.addHandler(handler)
# Log some messages
for i in range(20):
my_logger.debug('i = %d' % i)
# See what files are created
logfiles = glob.glob('%s*' % LOG_FILENAME)
for filename in logfiles:
print(filename)
The result should be 6 separate files, each with part of the log history for the application:
logging_rotatingfile_example.out
logging_rotatingfile_example.out.1
logging_rotatingfile_example.out.2
logging_rotatingfile_example.out.3
logging_rotatingfile_example.out.4
logging_rotatingfile_example.out.5
The most current file is always logging_rotatingfile_example.out,
and each time it reaches the size limit it is renamed with the suffix
.1. Each of the existing backup files is renamed to increment the suffix
(.1 becomes .2, etc.) and the .6 file is erased.
Obviously this example sets the log length much too small as an extreme example. You would want to set maxBytes to an appropriate value.
Use of alternative formatting styles¶
When logging was added to the Python standard library, the only way of
formatting messages with variable content was to use the %-formatting
method. Since then, Python has gained two new formatting approaches:
string.Template (added in Python 2.4) and str.format()
(added in Python 2.6).
Logging (as of 3.2) provides improved support for these two additional
formatting styles. The Formatter class been enhanced to take an
additional, optional keyword parameter named style. This defaults to
'%', but other possible values are '{' and '$', which correspond
to the other two formatting styles. Backwards compatibility is maintained by
default (as you would expect), but by explicitly specifying a style parameter,
you get the ability to specify format strings which work with
str.format() or string.Template. Here’s an example console
session to show the possibilities:
>>> import logging
>>> root = logging.getLogger()
>>> root.setLevel(logging.DEBUG)
>>> handler = logging.StreamHandler()
>>> bf = logging.Formatter('{asctime} {name} {levelname:8s} {message}',
... style='{')
>>> handler.setFormatter(bf)
>>> root.addHandler(handler)
>>> logger = logging.getLogger('foo.bar')
>>> logger.debug('This is a DEBUG message')
2010-10-28 15:11:55,341 foo.bar DEBUG This is a DEBUG message
>>> logger.critical('This is a CRITICAL message')
2010-10-28 15:12:11,526 foo.bar CRITICAL This is a CRITICAL message
>>> df = logging.Formatter('$asctime $name ${levelname} $message',
... style='$')
>>> handler.setFormatter(df)
>>> logger.debug('This is a DEBUG message')
2010-10-28 15:13:06,924 foo.bar DEBUG This is a DEBUG message
>>> logger.critical('This is a CRITICAL message')
2010-10-28 15:13:11,494 foo.bar CRITICAL This is a CRITICAL message
>>>
Note that the formatting of logging messages for final output to logs is completely independent of how an individual logging message is constructed. That can still use %-formatting, as shown here:
>>> logger.error('This is an%s %s %s', 'other,', 'ERROR,', 'message')
2010-10-28 15:19:29,833 foo.bar ERROR This is another, ERROR, message
>>>
Logging calls (logger.debug(), logger.info() etc.) only take
positional parameters for the actual logging message itself, with keyword
parameters used only for determining options for how to handle the actual
logging call (e.g. the exc_info keyword parameter to indicate that
traceback information should be logged, or the extra keyword parameter
to indicate additional contextual information to be added to the log). So
you cannot directly make logging calls using str.format() or
string.Template syntax, because internally the logging package
uses %-formatting to merge the format string and the variable arguments.
There would be no changing this while preserving backward compatibility, since
all logging calls which are out there in existing code will be using %-format
strings.
There is, however, a way that you can use {}- and $- formatting to construct
your individual log messages. Recall that for a message you can use an
arbitrary object as a message format string, and that the logging package will
call str() on that object to get the actual format string. Consider the
following two classes:
class BraceMessage:
def __init__(self, fmt, /, *args, **kwargs):
self.fmt = fmt
self.args = args
self.kwargs = kwargs
def __str__(self):
return self.fmt.format(*self.args, **self.kwargs)
class DollarMessage:
def __init__(self, fmt, /, **kwargs):
self.fmt = fmt
self.kwargs = kwargs
def __str__(self):
from string import Template
return Template(self.fmt).substitute(**self.kwargs)
Either of these can be used in place of a format string, to allow {}- or
$-formatting to be used to build the actual “message” part which appears in the
formatted log output in place of “%(message)s” or “{message}” or “$message”.
It’s a little unwieldy to use the class names whenever you want to log
something, but it’s quite palatable if you use an alias such as __ (double
underscore — not to be confused with _, the single underscore used as a
synonym/alias for gettext.gettext() or its brethren).
The above classes are not included in Python, though they’re easy enough to
copy and paste into your own code. They can be used as follows (assuming that
they’re declared in a module called wherever):
>>> from wherever import BraceMessage as __
>>> print(__('Message with {0} {name}', 2, name='placeholders'))
Message with 2 placeholders
>>> class Point: pass
...
>>> p = Point()
>>> p.x = 0.5
>>> p.y = 0.5
>>> print(__('Message with coordinates: ({point.x:.2f}, {point.y:.2f})',
... point=p))
Message with coordinates: (0.50, 0.50)
>>> from wherever import DollarMessage as __
>>> print(__('Message with $num $what', num=2, what='placeholders'))
Message with 2 placeholders
>>>
While the above examples use print() to show how the formatting works, you
would of course use logger.debug() or similar to actually log using this
approach.
One thing to note is that you pay no significant performance penalty with this approach: the actual formatting happens not when you make the logging call, but when (and if) the logged message is actually about to be output to a log by a handler. So the only slightly unusual thing which might trip you up is that the parentheses go around the format string and the arguments, not just the format string. That’s because the __ notation is just syntax sugar for a constructor call to one of the XXXMessage classes.
If you prefer, you can use a LoggerAdapter to achieve a similar effect
to the above, as in the following example:
import logging
class Message:
def __init__(self, fmt, args):
self.fmt = fmt
self.args = args
def __str__(self):
return self.fmt.format(*self.args)
class StyleAdapter(logging.LoggerAdapter):
def __init__(self, logger, extra=None):
super().__init__(logger, extra or {})
def log(self, level, msg, /, *args, **kwargs):
if self.isEnabledFor(level):
msg, kwargs = self.process(msg, kwargs)
self.logger._log(level, Message(msg, args), (), **kwargs)
logger = StyleAdapter(logging.getLogger(__name__))
def main():
logger.debug('Hello, {}', 'world!')
if __name__ == '__main__':
logging.basicConfig(level=logging.DEBUG)
main()
The above script should log the message Hello, world! when run with
Python 3.2 or later.
Personalizando o LogRecord¶
Every logging event is represented by a LogRecord instance.
When an event is logged and not filtered out by a logger’s level, a
LogRecord is created, populated with information about the event and
then passed to the handlers for that logger (and its ancestors, up to and
including the logger where further propagation up the hierarchy is disabled).
Before Python 3.2, there were only two places where this creation was done:
Logger.makeRecord(), which is called in the normal process of logging an event. This invokedLogRecorddirectly to create an instance.makeLogRecord(), which is called with a dictionary containing attributes to be added to the LogRecord. This is typically invoked when a suitable dictionary has been received over the network (e.g. in pickle form via aSocketHandler, or in JSON form via anHTTPHandler).
This has usually meant that if you need to do anything special with a
LogRecord, you’ve had to do one of the following.
Create your own
Loggersubclass, which overridesLogger.makeRecord(), and set it usingsetLoggerClass()before any loggers that you care about are instantiated.Add a
Filterto a logger or handler, which does the necessary special manipulation you need when itsfilter()method is called.
The first approach would be a little unwieldy in the scenario where (say)
several different libraries wanted to do different things. Each would attempt
to set its own Logger subclass, and the one which did this last would
win.
The second approach works reasonably well for many cases, but does not allow
you to e.g. use a specialized subclass of LogRecord. Library
developers can set a suitable filter on their loggers, but they would have to
remember to do this every time they introduced a new logger (which they would
do simply by adding new packages or modules and doing
logger = logging.getLogger(__name__)
at module level). It’s probably one too many things to think about. Developers
could also add the filter to a NullHandler attached to their
top-level logger, but this would not be invoked if an application developer
attached a handler to a lower-level library logger — so output from that
handler would not reflect the intentions of the library developer.
In Python 3.2 and later, LogRecord creation is done through a
factory, which you can specify. The factory is just a callable you can set with
setLogRecordFactory(), and interrogate with
getLogRecordFactory(). The factory is invoked with the same
signature as the LogRecord constructor, as LogRecord
is the default setting for the factory.
This approach allows a custom factory to control all aspects of LogRecord creation. For example, you could return a subclass, or just add some additional attributes to the record once created, using a pattern similar to this:
old_factory = logging.getLogRecordFactory()
def record_factory(*args, **kwargs):
record = old_factory(*args, **kwargs)
record.custom_attribute = 0xdecafbad
return record
logging.setLogRecordFactory(record_factory)
This pattern allows different libraries to chain factories together, and as
long as they don’t overwrite each other’s attributes or unintentionally
overwrite the attributes provided as standard, there should be no surprises.
However, it should be borne in mind that each link in the chain adds run-time
overhead to all logging operations, and the technique should only be used when
the use of a Filter does not provide the desired result.
Subclassing QueueHandler - a ZeroMQ example¶
You can use a QueueHandler subclass to send messages to other kinds
of queues, for example a ZeroMQ ‘publish’ socket. In the example below,the
socket is created separately and passed to the handler (as its ‘queue’):
import zmq # using pyzmq, the Python binding for ZeroMQ
import json # for serializing records portably
ctx = zmq.Context()
sock = zmq.Socket(ctx, zmq.PUB) # or zmq.PUSH, or other suitable value
sock.bind('tcp://*:5556') # or wherever
class ZeroMQSocketHandler(QueueHandler):
def enqueue(self, record):
self.queue.send_json(record.__dict__)
handler = ZeroMQSocketHandler(sock)
Of course there are other ways of organizing this, for example passing in the data needed by the handler to create the socket:
class ZeroMQSocketHandler(QueueHandler):
def __init__(self, uri, socktype=zmq.PUB, ctx=None):
self.ctx = ctx or zmq.Context()
socket = zmq.Socket(self.ctx, socktype)
socket.bind(uri)
super().__init__(socket)
def enqueue(self, record):
self.queue.send_json(record.__dict__)
def close(self):
self.queue.close()
Subclassing QueueListener - a ZeroMQ example¶
You can also subclass QueueListener to get messages from other kinds
of queues, for example a ZeroMQ ‘subscribe’ socket. Here’s an example:
class ZeroMQSocketListener(QueueListener):
def __init__(self, uri, /, *handlers, **kwargs):
self.ctx = kwargs.get('ctx') or zmq.Context()
socket = zmq.Socket(self.ctx, zmq.SUB)
socket.setsockopt_string(zmq.SUBSCRIBE, '') # subscribe to everything
socket.connect(uri)
super().__init__(socket, *handlers, **kwargs)
def dequeue(self):
msg = self.queue.recv_json()
return logging.makeLogRecord(msg)
Ver também
- Módulo
logging Referência da API para o módulo de logging.
- Módulo
logging.config API de configuração para o módulo logging.
- Módulo
logging.handlers Manipuladores úteis incluídos no módulo logging.
An example dictionary-based configuration¶
Below is an example of a logging configuration dictionary - it’s taken from
the documentation on the Django project.
This dictionary is passed to dictConfig() to put the configuration into effect:
LOGGING = {
'version': 1,
'disable_existing_loggers': True,
'formatters': {
'verbose': {
'format': '%(levelname)s %(asctime)s %(module)s %(process)d %(thread)d %(message)s'
},
'simple': {
'format': '%(levelname)s %(message)s'
},
},
'filters': {
'special': {
'()': 'project.logging.SpecialFilter',
'foo': 'bar',
}
},
'handlers': {
'null': {
'level':'DEBUG',
'class':'django.utils.log.NullHandler',
},
'console':{
'level':'DEBUG',
'class':'logging.StreamHandler',
'formatter': 'simple'
},
'mail_admins': {
'level': 'ERROR',
'class': 'django.utils.log.AdminEmailHandler',
'filters': ['special']
}
},
'loggers': {
'django': {
'handlers':['null'],
'propagate': True,
'level':'INFO',
},
'django.request': {
'handlers': ['mail_admins'],
'level': 'ERROR',
'propagate': False,
},
'myproject.custom': {
'handlers': ['console', 'mail_admins'],
'level': 'INFO',
'filters': ['special']
}
}
}
For more information about this configuration, you can see the relevant section of the Django documentation.
Using a rotator and namer to customize log rotation processing¶
An example of how you can define a namer and rotator is given in the following runnable script, which shows gzip compression of the log file:
import gzip
import logging
import logging.handlers
import os
import shutil
def namer(name):
return name + ".gz"
def rotator(source, dest):
with open(source, 'rb') as f_in:
with gzip.open(dest, 'wb') as f_out:
shutil.copyfileobj(f_in, f_out)
os.remove(source)
rh = logging.handlers.RotatingFileHandler('rotated.log', maxBytes=128, backupCount=5)
rh.rotator = rotator
rh.namer = namer
root = logging.getLogger()
root.setLevel(logging.INFO)
root.addHandler(rh)
f = logging.Formatter('%(asctime)s %(message)s')
rh.setFormatter(f)
for i in range(1000):
root.info(f'Message no. {i + 1}')
After running this, you will see six new files, five of which are compressed:
$ ls rotated.log*
rotated.log rotated.log.2.gz rotated.log.4.gz
rotated.log.1.gz rotated.log.3.gz rotated.log.5.gz
$ zcat rotated.log.1.gz
2023-01-20 02:28:17,767 Message no. 996
2023-01-20 02:28:17,767 Message no. 997
2023-01-20 02:28:17,767 Message no. 998
A more elaborate multiprocessing example¶
The following working example shows how logging can be used with multiprocessing using configuration files. The configurations are fairly simple, but serve to illustrate how more complex ones could be implemented in a real multiprocessing scenario.
In the example, the main process spawns a listener process and some worker processes. Each of the main process, the listener and the workers have three separate configurations (the workers all share the same configuration). We can see logging in the main process, how the workers log to a QueueHandler and how the listener implements a QueueListener and a more complex logging configuration, and arranges to dispatch events received via the queue to the handlers specified in the configuration. Note that these configurations are purely illustrative, but you should be able to adapt this example to your own scenario.
Here’s the script - the docstrings and the comments hopefully explain how it works:
import logging
import logging.config
import logging.handlers
from multiprocessing import Process, Queue, Event, current_process
import os
import random
import time
class MyHandler:
"""
A simple handler for logging events. It runs in the listener process and
dispatches events to loggers based on the name in the received record,
which then get dispatched, by the logging system, to the handlers
configured for those loggers.
"""
def handle(self, record):
if record.name == "root":
logger = logging.getLogger()
else:
logger = logging.getLogger(record.name)
if logger.isEnabledFor(record.levelno):
# The process name is transformed just to show that it's the listener
# doing the logging to files and console
record.processName = '%s (for %s)' % (current_process().name, record.processName)
logger.handle(record)
def listener_process(q, stop_event, config):
"""
This could be done in the main process, but is just done in a separate
process for illustrative purposes.
This initialises logging according to the specified configuration,
starts the listener and waits for the main process to signal completion
via the event. The listener is then stopped, and the process exits.
"""
logging.config.dictConfig(config)
listener = logging.handlers.QueueListener(q, MyHandler())
listener.start()
if os.name == 'posix':
# On POSIX, the setup logger will have been configured in the
# parent process, but should have been disabled following the
# dictConfig call.
# On Windows, since fork isn't used, the setup logger won't
# exist in the child, so it would be created and the message
# would appear - hence the "if posix" clause.
logger = logging.getLogger('setup')
logger.critical('Should not appear, because of disabled logger ...')
stop_event.wait()
listener.stop()
def worker_process(config):
"""
A number of these are spawned for the purpose of illustration. In
practice, they could be a heterogeneous bunch of processes rather than
ones which are identical to each other.
This initialises logging according to the specified configuration,
and logs a hundred messages with random levels to randomly selected
loggers.
A small sleep is added to allow other processes a chance to run. This
is not strictly needed, but it mixes the output from the different
processes a bit more than if it's left out.
"""
logging.config.dictConfig(config)
levels = [logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR,
logging.CRITICAL]
loggers = ['foo', 'foo.bar', 'foo.bar.baz',
'spam', 'spam.ham', 'spam.ham.eggs']
if os.name == 'posix':
# On POSIX, the setup logger will have been configured in the
# parent process, but should have been disabled following the
# dictConfig call.
# On Windows, since fork isn't used, the setup logger won't
# exist in the child, so it would be created and the message
# would appear - hence the "if posix" clause.
logger = logging.getLogger('setup')
logger.critical('Should not appear, because of disabled logger ...')
for i in range(100):
lvl = random.choice(levels)
logger = logging.getLogger(random.choice(loggers))
logger.log(lvl, 'Message no. %d', i)
time.sleep(0.01)
def main():
q = Queue()
# The main process gets a simple configuration which prints to the console.
config_initial = {
'version': 1,
'handlers': {
'console': {
'class': 'logging.StreamHandler',
'level': 'INFO'
}
},
'root': {
'handlers': ['console'],
'level': 'DEBUG'
}
}
# The worker process configuration is just a QueueHandler attached to the
# root logger, which allows all messages to be sent to the queue.
# We disable existing loggers to disable the "setup" logger used in the
# parent process. This is needed on POSIX because the logger will
# be there in the child following a fork().
config_worker = {
'version': 1,
'disable_existing_loggers': True,
'handlers': {
'queue': {
'class': 'logging.handlers.QueueHandler',
'queue': q
}
},
'root': {
'handlers': ['queue'],
'level': 'DEBUG'
}
}
# The listener process configuration shows that the full flexibility of
# logging configuration is available to dispatch events to handlers however
# you want.
# We disable existing loggers to disable the "setup" logger used in the
# parent process. This is needed on POSIX because the logger will
# be there in the child following a fork().
config_listener = {
'version': 1,
'disable_existing_loggers': True,
'formatters': {
'detailed': {
'class': 'logging.Formatter',
'format': '%(asctime)s %(name)-15s %(levelname)-8s %(processName)-10s %(message)s'
},
'simple': {
'class': 'logging.Formatter',
'format': '%(name)-15s %(levelname)-8s %(processName)-10s %(message)s'
}
},
'handlers': {
'console': {
'class': 'logging.StreamHandler',
'formatter': 'simple',
'level': 'INFO'
},
'file': {
'class': 'logging.FileHandler',
'filename': 'mplog.log',
'mode': 'w',
'formatter': 'detailed'
},
'foofile': {
'class': 'logging.FileHandler',
'filename': 'mplog-foo.log',
'mode': 'w',
'formatter': 'detailed'
},
'errors': {
'class': 'logging.FileHandler',
'filename': 'mplog-errors.log',
'mode': 'w',
'formatter': 'detailed',
'level': 'ERROR'
}
},
'loggers': {
'foo': {
'handlers': ['foofile']
}
},
'root': {
'handlers': ['console', 'file', 'errors'],
'level': 'DEBUG'
}
}
# Log some initial events, just to show that logging in the parent works
# normally.
logging.config.dictConfig(config_initial)
logger = logging.getLogger('setup')
logger.info('About to create workers ...')
workers = []
for i in range(5):
wp = Process(target=worker_process, name='worker %d' % (i + 1),
args=(config_worker,))
workers.append(wp)
wp.start()
logger.info('Started worker: %s', wp.name)
logger.info('About to create listener ...')
stop_event = Event()
lp = Process(target=listener_process, name='listener',
args=(q, stop_event, config_listener))
lp.start()
logger.info('Started listener')
# We now hang around for the workers to finish their work.
for wp in workers:
wp.join()
# Workers all done, listening can now stop.
# Logging in the parent still works normally.
logger.info('Telling listener to stop ...')
stop_event.set()
lp.join()
logger.info('All done.')
if __name__ == '__main__':
main()
Inserting a BOM into messages sent to a SysLogHandler¶
RFC 5424 requires that a Unicode message be sent to a syslog daemon as a set of bytes which have the following structure: an optional pure-ASCII component, followed by a UTF-8 Byte Order Mark (BOM), followed by Unicode encoded using UTF-8. (See the relevant section of the specification.)
In Python 3.1, code was added to
SysLogHandler to insert a BOM into the message, but
unfortunately, it was implemented incorrectly, with the BOM appearing at the
beginning of the message and hence not allowing any pure-ASCII component to
appear before it.
As this behaviour is broken, the incorrect BOM insertion code is being removed from Python 3.2.4 and later. However, it is not being replaced, and if you want to produce RFC 5424-compliant messages which include a BOM, an optional pure-ASCII sequence before it and arbitrary Unicode after it, encoded using UTF-8, then you need to do the following:
Attach a
Formatterinstance to yourSysLogHandlerinstance, with a format string such as:'ASCII section\ufeffUnicode section'
The Unicode code point U+FEFF, when encoded using UTF-8, will be encoded as a UTF-8 BOM – the byte-string
b'\xef\xbb\xbf'.Replace the ASCII section with whatever placeholders you like, but make sure that the data that appears in there after substitution is always ASCII (that way, it will remain unchanged after UTF-8 encoding).
Replace the Unicode section with whatever placeholders you like; if the data which appears there after substitution contains characters outside the ASCII range, that’s fine – it will be encoded using UTF-8.
The formatted message will be encoded using UTF-8 encoding by
SysLogHandler. If you follow the above rules, you should be able to produce
RFC 5424-compliant messages. If you don’t, logging may not complain, but your
messages will not be RFC 5424-compliant, and your syslog daemon may complain.
Implementing structured logging¶
Although most logging messages are intended for reading by humans, and thus not readily machine-parseable, there might be circumstances where you want to output messages in a structured format which is capable of being parsed by a program (without needing complex regular expressions to parse the log message). This is straightforward to achieve using the logging package. There are a number of ways in which this could be achieved, but the following is a simple approach which uses JSON to serialise the event in a machine-parseable manner:
import json
import logging
class StructuredMessage:
def __init__(self, message, /, **kwargs):
self.message = message
self.kwargs = kwargs
def __str__(self):
return '%s >>> %s' % (self.message, json.dumps(self.kwargs))
_ = StructuredMessage # optional, to improve readability
logging.basicConfig(level=logging.INFO, format='%(message)s')
logging.info(_('message 1', foo='bar', bar='baz', num=123, fnum=123.456))
If the above script is run, it prints:
message 1 >>> {"fnum": 123.456, "num": 123, "bar": "baz", "foo": "bar"}
Note that the order of items might be different according to the version of Python used.
If you need more specialised processing, you can use a custom JSON encoder, as in the following complete example:
import json
import logging
class Encoder(json.JSONEncoder):
def default(self, o):
if isinstance(o, set):
return tuple(o)
elif isinstance(o, str):
return o.encode('unicode_escape').decode('ascii')
return super().default(o)
class StructuredMessage:
def __init__(self, message, /, **kwargs):
self.message = message
self.kwargs = kwargs
def __str__(self):
s = Encoder().encode(self.kwargs)
return '%s >>> %s' % (self.message, s)
_ = StructuredMessage # optional, to improve readability
def main():
logging.basicConfig(level=logging.INFO, format='%(message)s')
logging.info(_('message 1', set_value={1, 2, 3}, snowman='\u2603'))
if __name__ == '__main__':
main()
When the above script is run, it prints:
message 1 >>> {"snowman": "\u2603", "set_value": [1, 2, 3]}
Note that the order of items might be different according to the version of Python used.
Customizing handlers with dictConfig()¶
There are times when you want to customize logging handlers in particular ways,
and if you use dictConfig() you may be able to do this without
subclassing. As an example, consider that you may want to set the ownership of a
log file. On POSIX, this is easily done using shutil.chown(), but the file
handlers in the stdlib don’t offer built-in support. You can customize handler
creation using a plain function such as:
def owned_file_handler(filename, mode='a', encoding=None, owner=None):
if owner:
if not os.path.exists(filename):
open(filename, 'a').close()
shutil.chown(filename, *owner)
return logging.FileHandler(filename, mode, encoding)
You can then specify, in a logging configuration passed to dictConfig(),
that a logging handler be created by calling this function:
LOGGING = {
'version': 1,
'disable_existing_loggers': False,
'formatters': {
'default': {
'format': '%(asctime)s %(levelname)s %(name)s %(message)s'
},
},
'handlers': {
'file':{
# The values below are popped from this dictionary and
# used to create the handler, set the handler's level and
# its formatter.
'()': owned_file_handler,
'level':'DEBUG',
'formatter': 'default',
# The values below are passed to the handler creator callable
# as keyword arguments.
'owner': ['pulse', 'pulse'],
'filename': 'chowntest.log',
'mode': 'w',
'encoding': 'utf-8',
},
},
'root': {
'handlers': ['file'],
'level': 'DEBUG',
},
}
In this example I am setting the ownership using the pulse user and group,
just for the purposes of illustration. Putting it together into a working
script, chowntest.py:
import logging, logging.config, os, shutil
def owned_file_handler(filename, mode='a', encoding=None, owner=None):
if owner:
if not os.path.exists(filename):
open(filename, 'a').close()
shutil.chown(filename, *owner)
return logging.FileHandler(filename, mode, encoding)
LOGGING = {
'version': 1,
'disable_existing_loggers': False,
'formatters': {
'default': {
'format': '%(asctime)s %(levelname)s %(name)s %(message)s'
},
},
'handlers': {
'file':{
# The values below are popped from this dictionary and
# used to create the handler, set the handler's level and
# its formatter.
'()': owned_file_handler,
'level':'DEBUG',
'formatter': 'default',
# The values below are passed to the handler creator callable
# as keyword arguments.
'owner': ['pulse', 'pulse'],
'filename': 'chowntest.log',
'mode': 'w',
'encoding': 'utf-8',
},
},
'root': {
'handlers': ['file'],
'level': 'DEBUG',
},
}
logging.config.dictConfig(LOGGING)
logger = logging.getLogger('mylogger')
logger.debug('A debug message')
To run this, you will probably need to run as root:
$ sudo python3.3 chowntest.py
$ cat chowntest.log
2013-11-05 09:34:51,128 DEBUG mylogger A debug message
$ ls -l chowntest.log
-rw-r--r-- 1 pulse pulse 55 2013-11-05 09:34 chowntest.log
Note that this example uses Python 3.3 because that’s where shutil.chown()
makes an appearance. This approach should work with any Python version that
supports dictConfig() - namely, Python 2.7, 3.2 or later. With pre-3.3
versions, you would need to implement the actual ownership change using e.g.
os.chown().
In practice, the handler-creating function may be in a utility module somewhere in your project. Instead of the line in the configuration:
'()': owned_file_handler,
you could use e.g.:
'()': 'ext://project.util.owned_file_handler',
where project.util can be replaced with the actual name of the package
where the function resides. In the above working script, using
'ext://__main__.owned_file_handler' should work. Here, the actual callable
is resolved by dictConfig() from the ext:// specification.
This example hopefully also points the way to how you could implement other
types of file change - e.g. setting specific POSIX permission bits - in the
same way, using os.chmod().
Of course, the approach could also be extended to types of handler other than a
FileHandler - for example, one of the rotating file handlers,
or a different type of handler altogether.
Using particular formatting styles throughout your application¶
In Python 3.2, the Formatter gained a style keyword
parameter which, while defaulting to % for backward compatibility, allowed
the specification of { or $ to support the formatting approaches
supported by str.format() and string.Template. Note that this
governs the formatting of logging messages for final output to logs, and is
completely orthogonal to how an individual logging message is constructed.
Logging calls (debug(), info() etc.) only take
positional parameters for the actual logging message itself, with keyword
parameters used only for determining options for how to handle the logging call
(e.g. the exc_info keyword parameter to indicate that traceback information
should be logged, or the extra keyword parameter to indicate additional
contextual information to be added to the log). So you cannot directly make
logging calls using str.format() or string.Template syntax,
because internally the logging package uses %-formatting to merge the format
string and the variable arguments. There would be no changing this while preserving
backward compatibility, since all logging calls which are out there in existing
code will be using %-format strings.
There have been suggestions to associate format styles with specific loggers, but that approach also runs into backward compatibility problems because any existing code could be using a given logger name and using %-formatting.
For logging to work interoperably between any third-party libraries and your code, decisions about formatting need to be made at the level of the individual logging call. This opens up a couple of ways in which alternative formatting styles can be accommodated.
Using LogRecord factories¶
In Python 3.2, along with the Formatter changes mentioned
above, the logging package gained the ability to allow users to set their own
LogRecord subclasses, using the setLogRecordFactory() function.
You can use this to set your own subclass of LogRecord, which does the
Right Thing by overriding the getMessage() method. The base
class implementation of this method is where the msg % args formatting
happens, and where you can substitute your alternate formatting; however, you
should be careful to support all formatting styles and allow %-formatting as
the default, to ensure interoperability with other code. Care should also be
taken to call str(self.msg), just as the base implementation does.
Refer to the reference documentation on setLogRecordFactory() and
LogRecord for more information.
Using custom message objects¶
There is another, perhaps simpler way that you can use {}- and $- formatting to
construct your individual log messages. You may recall (from
Usando objetos arbitrários como mensagens) that when logging you can use an arbitrary
object as a message format string, and that the logging package will call
str() on that object to get the actual format string. Consider the
following two classes:
class BraceMessage:
def __init__(self, fmt, /, *args, **kwargs):
self.fmt = fmt
self.args = args
self.kwargs = kwargs
def __str__(self):
return self.fmt.format(*self.args, **self.kwargs)
class DollarMessage:
def __init__(self, fmt, /, **kwargs):
self.fmt = fmt
self.kwargs = kwargs
def __str__(self):
from string import Template
return Template(self.fmt).substitute(**self.kwargs)
Either of these can be used in place of a format string, to allow {}- or
$-formatting to be used to build the actual “message” part which appears in the
formatted log output in place of “%(message)s” or “{message}” or “$message”.
If you find it a little unwieldy to use the class names whenever you want to log
something, you can make it more palatable if you use an alias such as M or
_ for the message (or perhaps __, if you are using _ for
localization).
Examples of this approach are given below. Firstly, formatting with
str.format():
>>> __ = BraceMessage
>>> print(__('Message with {0} {1}', 2, 'placeholders'))
Message with 2 placeholders
>>> class Point: pass
...
>>> p = Point()
>>> p.x = 0.5
>>> p.y = 0.5
>>> print(__('Message with coordinates: ({point.x:.2f}, {point.y:.2f})', point=p))
Message with coordinates: (0.50, 0.50)
Secondly, formatting with string.Template:
>>> __ = DollarMessage
>>> print(__('Message with $num $what', num=2, what='placeholders'))
Message with 2 placeholders
>>>
One thing to note is that you pay no significant performance penalty with this
approach: the actual formatting happens not when you make the logging call, but
when (and if) the logged message is actually about to be output to a log by a
handler. So the only slightly unusual thing which might trip you up is that the
parentheses go around the format string and the arguments, not just the format
string. That’s because the __ notation is just syntax sugar for a constructor
call to one of the XXXMessage classes shown above.
Configuring filters with dictConfig()¶
You can configure filters using dictConfig(), though it
might not be obvious at first glance how to do it (hence this recipe). Since
Filter is the only filter class included in the standard
library, and it is unlikely to cater to many requirements (it’s only there as a
base class), you will typically need to define your own Filter
subclass with an overridden filter() method. To do this,
specify the () key in the configuration dictionary for the filter,
specifying a callable which will be used to create the filter (a class is the
most obvious, but you can provide any callable which returns a
Filter instance). Here is a complete example:
import logging
import logging.config
import sys
class MyFilter(logging.Filter):
def __init__(self, param=None):
self.param = param
def filter(self, record):
if self.param is None:
allow = True
else:
allow = self.param not in record.msg
if allow:
record.msg = 'changed: ' + record.msg
return allow
LOGGING = {
'version': 1,
'filters': {
'myfilter': {
'()': MyFilter,
'param': 'noshow',
}
},
'handlers': {
'console': {
'class': 'logging.StreamHandler',
'filters': ['myfilter']
}
},
'root': {
'level': 'DEBUG',
'handlers': ['console']
},
}
if __name__ == '__main__':
logging.config.dictConfig(LOGGING)
logging.debug('hello')
logging.debug('hello - noshow')
This example shows how you can pass configuration data to the callable which constructs the instance, in the form of keyword parameters. When run, the above script will print:
changed: hello
which shows that the filter is working as configured.
A couple of extra points to note:
If you can’t refer to the callable directly in the configuration (e.g. if it lives in a different module, and you can’t import it directly where the configuration dictionary is), you can use the form
ext://...as described in Access to external objects. For example, you could have used the text'ext://__main__.MyFilter'instead ofMyFilterin the above example.As well as for filters, this technique can also be used to configure custom handlers and formatters. See User-defined objects for more information on how logging supports using user-defined objects in its configuration, and see the other cookbook recipe Customizing handlers with dictConfig() above.
Customized exception formatting¶
There might be times when you want to do customized exception formatting - for argument’s sake, let’s say you want exactly one line per logged event, even when exception information is present. You can do this with a custom formatter class, as shown in the following example:
import logging
class OneLineExceptionFormatter(logging.Formatter):
def formatException(self, exc_info):
"""
Format an exception so that it prints on a single line.
"""
result = super().formatException(exc_info)
return repr(result) # or format into one line however you want to
def format(self, record):
s = super().format(record)
if record.exc_text:
s = s.replace('\n', '') + '|'
return s
def configure_logging():
fh = logging.FileHandler('output.txt', 'w')
f = OneLineExceptionFormatter('%(asctime)s|%(levelname)s|%(message)s|',
'%d/%m/%Y %H:%M:%S')
fh.setFormatter(f)
root = logging.getLogger()
root.setLevel(logging.DEBUG)
root.addHandler(fh)
def main():
configure_logging()
logging.info('Sample message')
try:
x = 1 / 0
except ZeroDivisionError as e:
logging.exception('ZeroDivisionError: %s', e)
if __name__ == '__main__':
main()
When run, this produces a file with exactly two lines:
28/01/2015 07:21:23|INFO|Sample message|
28/01/2015 07:21:23|ERROR|ZeroDivisionError: integer division or modulo by zero|'Traceback (most recent call last):\n File "logtest7.py", line 30, in main\n x = 1 / 0\nZeroDivisionError: integer division or modulo by zero'|
While the above treatment is simplistic, it points the way to how exception
information can be formatted to your liking. The traceback module may be
helpful for more specialized needs.
Speaking logging messages¶
There might be situations when it is desirable to have logging messages rendered
in an audible rather than a visible format. This is easy to do if you have
text-to-speech (TTS) functionality available in your system, even if it doesn’t have
a Python binding. Most TTS systems have a command line program you can run, and
this can be invoked from a handler using subprocess. It’s assumed here
that TTS command line programs won’t expect to interact with users or take a
long time to complete, and that the frequency of logged messages will be not so
high as to swamp the user with messages, and that it’s acceptable to have the
messages spoken one at a time rather than concurrently, The example implementation
below waits for one message to be spoken before the next is processed, and this
might cause other handlers to be kept waiting. Here is a short example showing
the approach, which assumes that the espeak TTS package is available:
import logging
import subprocess
import sys
class TTSHandler(logging.Handler):
def emit(self, record):
msg = self.format(record)
# Speak slowly in a female English voice
cmd = ['espeak', '-s150', '-ven+f3', msg]
p = subprocess.Popen(cmd, stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
# wait for the program to finish
p.communicate()
def configure_logging():
h = TTSHandler()
root = logging.getLogger()
root.addHandler(h)
# the default formatter just returns the message
root.setLevel(logging.DEBUG)
def main():
logging.info('Hello')
logging.debug('Goodbye')
if __name__ == '__main__':
configure_logging()
sys.exit(main())
When run, this script should say “Hello” and then “Goodbye” in a female voice.
The above approach can, of course, be adapted to other TTS systems and even other systems altogether which can process messages via external programs run from a command line.
Buffering logging messages and outputting them conditionally¶
There might be situations where you want to log messages in a temporary area and only output them if a certain condition occurs. For example, you may want to start logging debug events in a function, and if the function completes without errors, you don’t want to clutter the log with the collected debug information, but if there is an error, you want all the debug information to be output as well as the error.
Here is an example which shows how you could do this using a decorator for your
functions where you want logging to behave this way. It makes use of the
logging.handlers.MemoryHandler, which allows buffering of logged events
until some condition occurs, at which point the buffered events are flushed
- passed to another handler (the target handler) for processing. By default,
the MemoryHandler flushed when its buffer gets filled up or an event whose
level is greater than or equal to a specified threshold is seen. You can use this
recipe with a more specialised subclass of MemoryHandler if you want custom
flushing behavior.
The example script has a simple function, foo, which just cycles through
all the logging levels, writing to sys.stderr to say what level it’s about
to log at, and then actually logging a message at that level. You can pass a
parameter to foo which, if true, will log at ERROR and CRITICAL levels -
otherwise, it only logs at DEBUG, INFO and WARNING levels.
The script just arranges to decorate foo with a decorator which will do the
conditional logging that’s required. The decorator takes a logger as a parameter
and attaches a memory handler for the duration of the call to the decorated
function. The decorator can be additionally parameterised using a target handler,
a level at which flushing should occur, and a capacity for the buffer (number of
records buffered). These default to a StreamHandler which
writes to sys.stderr, logging.ERROR and 100 respectively.
Here’s the script:
import logging
from logging.handlers import MemoryHandler
import sys
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
def log_if_errors(logger, target_handler=None, flush_level=None, capacity=None):
if target_handler is None:
target_handler = logging.StreamHandler()
if flush_level is None:
flush_level = logging.ERROR
if capacity is None:
capacity = 100
handler = MemoryHandler(capacity, flushLevel=flush_level, target=target_handler)
def decorator(fn):
def wrapper(*args, **kwargs):
logger.addHandler(handler)
try:
return fn(*args, **kwargs)
except Exception:
logger.exception('call failed')
raise
finally:
super(MemoryHandler, handler).flush()
logger.removeHandler(handler)
return wrapper
return decorator
def write_line(s):
sys.stderr.write('%s\n' % s)
def foo(fail=False):
write_line('about to log at DEBUG ...')
logger.debug('Actually logged at DEBUG')
write_line('about to log at INFO ...')
logger.info('Actually logged at INFO')
write_line('about to log at WARNING ...')
logger.warning('Actually logged at WARNING')
if fail:
write_line('about to log at ERROR ...')
logger.error('Actually logged at ERROR')
write_line('about to log at CRITICAL ...')
logger.critical('Actually logged at CRITICAL')
return fail
decorated_foo = log_if_errors(logger)(foo)
if __name__ == '__main__':
logger.setLevel(logging.DEBUG)
write_line('Calling undecorated foo with False')
assert not foo(False)
write_line('Calling undecorated foo with True')
assert foo(True)
write_line('Calling decorated foo with False')
assert not decorated_foo(False)
write_line('Calling decorated foo with True')
assert decorated_foo(True)
When this script is run, the following output should be observed:
Calling undecorated foo with False
about to log at DEBUG ...
about to log at INFO ...
about to log at WARNING ...
Calling undecorated foo with True
about to log at DEBUG ...
about to log at INFO ...
about to log at WARNING ...
about to log at ERROR ...
about to log at CRITICAL ...
Calling decorated foo with False
about to log at DEBUG ...
about to log at INFO ...
about to log at WARNING ...
Calling decorated foo with True
about to log at DEBUG ...
about to log at INFO ...
about to log at WARNING ...
about to log at ERROR ...
Actually logged at DEBUG
Actually logged at INFO
Actually logged at WARNING
Actually logged at ERROR
about to log at CRITICAL ...
Actually logged at CRITICAL
As you can see, actual logging output only occurs when an event is logged whose severity is ERROR or greater, but in that case, any previous events at lower severities are also logged.
You can of course use the conventional means of decoration:
@log_if_errors(logger)
def foo(fail=False):
...
Sending logging messages to email, with buffering¶
To illustrate how you can send log messages via email, so that a set number of
messages are sent per email, you can subclass
BufferingHandler. In the following example, which you can
adapt to suit your specific needs, a simple test harness is provided which allows you
to run the script with command line arguments specifying what you typically need to
send things via SMTP. (Run the downloaded script with the -h argument to see the
required and optional arguments.)
import logging
import logging.handlers
import smtplib
class BufferingSMTPHandler(logging.handlers.BufferingHandler):
def __init__(self, mailhost, port, username, password, fromaddr, toaddrs,
subject, capacity):
logging.handlers.BufferingHandler.__init__(self, capacity)
self.mailhost = mailhost
self.mailport = port
self.username = username
self.password = password
self.fromaddr = fromaddr
if isinstance(toaddrs, str):
toaddrs = [toaddrs]
self.toaddrs = toaddrs
self.subject = subject
self.setFormatter(logging.Formatter("%(asctime)s %(levelname)-5s %(message)s"))
def flush(self):
if len(self.buffer) > 0:
try:
smtp = smtplib.SMTP(self.mailhost, self.mailport)
smtp.starttls()
smtp.login(self.username, self.password)
msg = "From: %s\r\nTo: %s\r\nSubject: %s\r\n\r\n" % (self.fromaddr, ','.join(self.toaddrs), self.subject)
for record in self.buffer:
s = self.format(record)
msg = msg + s + "\r\n"
smtp.sendmail(self.fromaddr, self.toaddrs, msg)
smtp.quit()
except Exception:
if logging.raiseExceptions:
raise
self.buffer = []
if __name__ == '__main__':
import argparse
ap = argparse.ArgumentParser()
aa = ap.add_argument
aa('host', metavar='HOST', help='SMTP server')
aa('--port', '-p', type=int, default=587, help='SMTP port')
aa('user', metavar='USER', help='SMTP username')
aa('password', metavar='PASSWORD', help='SMTP password')
aa('to', metavar='TO', help='Addressee for emails')
aa('sender', metavar='SENDER', help='Sender email address')
aa('--subject', '-s',
default='Test Logging email from Python logging module (buffering)',
help='Subject of email')
options = ap.parse_args()
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
h = BufferingSMTPHandler(options.host, options.port, options.user,
options.password, options.sender,
options.to, options.subject, 10)
logger.addHandler(h)
for i in range(102):
logger.info("Info index = %d", i)
h.flush()
h.close()
If you run this script and your SMTP server is correctly set up, you should find that it sends eleven emails to the addressee you specify. The first ten emails will each have ten log messages, and the eleventh will have two messages. That makes up 102 messages as specified in the script.
Formatting times using UTC (GMT) via configuration¶
Sometimes you want to format times using UTC, which can be done using a class
such as UTCFormatter, shown below:
import logging
import time
class UTCFormatter(logging.Formatter):
converter = time.gmtime
and you can then use the UTCFormatter in your code instead of
Formatter. If you want to do that via configuration, you can
use the dictConfig() API with an approach illustrated by
the following complete example:
import logging
import logging.config
import time
class UTCFormatter(logging.Formatter):
converter = time.gmtime
LOGGING = {
'version': 1,
'disable_existing_loggers': False,
'formatters': {
'utc': {
'()': UTCFormatter,
'format': '%(asctime)s %(message)s',
},
'local': {
'format': '%(asctime)s %(message)s',
}
},
'handlers': {
'console1': {
'class': 'logging.StreamHandler',
'formatter': 'utc',
},
'console2': {
'class': 'logging.StreamHandler',
'formatter': 'local',
},
},
'root': {
'handlers': ['console1', 'console2'],
}
}
if __name__ == '__main__':
logging.config.dictConfig(LOGGING)
logging.warning('The local time is %s', time.asctime())
When this script is run, it should print something like:
2015-10-17 12:53:29,501 The local time is Sat Oct 17 13:53:29 2015
2015-10-17 13:53:29,501 The local time is Sat Oct 17 13:53:29 2015
showing how the time is formatted both as local time and UTC, one for each handler.
Using a context manager for selective logging¶
There are times when it would be useful to temporarily change the logging configuration and revert it back after doing something. For this, a context manager is the most obvious way of saving and restoring the logging context. Here is a simple example of such a context manager, which allows you to optionally change the logging level and add a logging handler purely in the scope of the context manager:
import logging
import sys
class LoggingContext:
def __init__(self, logger, level=None, handler=None, close=True):
self.logger = logger
self.level = level
self.handler = handler
self.close = close
def __enter__(self):
if self.level is not None:
self.old_level = self.logger.level
self.logger.setLevel(self.level)
if self.handler:
self.logger.addHandler(self.handler)
def __exit__(self, et, ev, tb):
if self.level is not None:
self.logger.setLevel(self.old_level)
if self.handler:
self.logger.removeHandler(self.handler)
if self.handler and self.close:
self.handler.close()
# implicit return of None => don't swallow exceptions
If you specify a level value, the logger’s level is set to that value in the scope of the with block covered by the context manager. If you specify a handler, it is added to the logger on entry to the block and removed on exit from the block. You can also ask the manager to close the handler for you on block exit - you could do this if you don’t need the handler any more.
To illustrate how it works, we can add the following block of code to the above:
if __name__ == '__main__':
logger = logging.getLogger('foo')
logger.addHandler(logging.StreamHandler())
logger.setLevel(logging.INFO)
logger.info('1. This should appear just once on stderr.')
logger.debug('2. This should not appear.')
with LoggingContext(logger, level=logging.DEBUG):
logger.debug('3. This should appear once on stderr.')
logger.debug('4. This should not appear.')
h = logging.StreamHandler(sys.stdout)
with LoggingContext(logger, level=logging.DEBUG, handler=h, close=True):
logger.debug('5. This should appear twice - once on stderr and once on stdout.')
logger.info('6. This should appear just once on stderr.')
logger.debug('7. This should not appear.')
We initially set the logger’s level to INFO, so message #1 appears and
message #2 doesn’t. We then change the level to DEBUG temporarily in the
following with block, and so message #3 appears. After the block exits, the
logger’s level is restored to INFO and so message #4 doesn’t appear. In the
next with block, we set the level to DEBUG again but also add a handler
writing to sys.stdout. Thus, message #5 appears twice on the console (once
via stderr and once via stdout). After the with statement’s
completion, the status is as it was before so message #6 appears (like message
#1) whereas message #7 doesn’t (just like message #2).
If we run the resulting script, the result is as follows:
$ python logctx.py
1. This should appear just once on stderr.
3. This should appear once on stderr.
5. This should appear twice - once on stderr and once on stdout.
5. This should appear twice - once on stderr and once on stdout.
6. This should appear just once on stderr.
If we run it again, but pipe stderr to /dev/null, we see the following,
which is the only message written to stdout:
$ python logctx.py 2>/dev/null
5. This should appear twice - once on stderr and once on stdout.
Once again, but piping stdout to /dev/null, we get:
$ python logctx.py >/dev/null
1. This should appear just once on stderr.
3. This should appear once on stderr.
5. This should appear twice - once on stderr and once on stdout.
6. This should appear just once on stderr.
In this case, the message #5 printed to stdout doesn’t appear, as expected.
Of course, the approach described here can be generalised, for example to attach logging filters temporarily. Note that the above code works in Python 2 as well as Python 3.
A CLI application starter template¶
Here’s an example which shows how you can:
Use a logging level based on command-line arguments
Dispatch to multiple subcommands in separate files, all logging at the same level in a consistent way
Make use of simple, minimal configuration
Suppose we have a command-line application whose job is to stop, start or
restart some services. This could be organised for the purposes of illustration
as a file app.py that is the main script for the application, with individual
commands implemented in start.py, stop.py and restart.py. Suppose
further that we want to control the verbosity of the application via a
command-line argument, defaulting to logging.INFO. Here’s one way that
app.py could be written:
import argparse
import importlib
import logging
import os
import sys
def main(args=None):
scriptname = os.path.basename(__file__)
parser = argparse.ArgumentParser(scriptname)
levels = ('DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL')
parser.add_argument('--log-level', default='INFO', choices=levels)
subparsers = parser.add_subparsers(dest='command',
help='Available commands:')
start_cmd = subparsers.add_parser('start', help='Start a service')
start_cmd.add_argument('name', metavar='NAME',
help='Name of service to start')
stop_cmd = subparsers.add_parser('stop',
help='Stop one or more services')
stop_cmd.add_argument('names', metavar='NAME', nargs='+',
help='Name of service to stop')
restart_cmd = subparsers.add_parser('restart',
help='Restart one or more services')
restart_cmd.add_argument('names', metavar='NAME', nargs='+',
help='Name of service to restart')
options = parser.parse_args()
# the code to dispatch commands could all be in this file. For the purposes
# of illustration only, we implement each command in a separate module.
try:
mod = importlib.import_module(options.command)
cmd = getattr(mod, 'command')
except (ImportError, AttributeError):
print('Unable to find the code for command \'%s\'' % options.command)
return 1
# Could get fancy here and load configuration from file or dictionary
logging.basicConfig(level=options.log_level,
format='%(levelname)s %(name)s %(message)s')
cmd(options)
if __name__ == '__main__':
sys.exit(main())
And the start, stop and restart commands can be implemented in
separate modules, like so for starting:
# start.py
import logging
logger = logging.getLogger(__name__)
def command(options):
logger.debug('About to start %s', options.name)
# actually do the command processing here ...
logger.info('Started the \'%s\' service.', options.name)
and thus for stopping:
# stop.py
import logging
logger = logging.getLogger(__name__)
def command(options):
n = len(options.names)
if n == 1:
plural = ''
services = '\'%s\'' % options.names[0]
else:
plural = 's'
services = ', '.join('\'%s\'' % name for name in options.names)
i = services.rfind(', ')
services = services[:i] + ' and ' + services[i + 2:]
logger.debug('About to stop %s', services)
# actually do the command processing here ...
logger.info('Stopped the %s service%s.', services, plural)
and similarly for restarting:
# restart.py
import logging
logger = logging.getLogger(__name__)
def command(options):
n = len(options.names)
if n == 1:
plural = ''
services = '\'%s\'' % options.names[0]
else:
plural = 's'
services = ', '.join('\'%s\'' % name for name in options.names)
i = services.rfind(', ')
services = services[:i] + ' and ' + services[i + 2:]
logger.debug('About to restart %s', services)
# actually do the command processing here ...
logger.info('Restarted the %s service%s.', services, plural)
If we run this application with the default log level, we get output like this:
$ python app.py start foo
INFO start Started the 'foo' service.
$ python app.py stop foo bar
INFO stop Stopped the 'foo' and 'bar' services.
$ python app.py restart foo bar baz
INFO restart Restarted the 'foo', 'bar' and 'baz' services.
The first word is the logging level, and the second word is the module or package name of the place where the event was logged.
If we change the logging level, then we can change the information sent to the log. For example, if we want more information:
$ python app.py --log-level DEBUG start foo
DEBUG start About to start foo
INFO start Started the 'foo' service.
$ python app.py --log-level DEBUG stop foo bar
DEBUG stop About to stop 'foo' and 'bar'
INFO stop Stopped the 'foo' and 'bar' services.
$ python app.py --log-level DEBUG restart foo bar baz
DEBUG restart About to restart 'foo', 'bar' and 'baz'
INFO restart Restarted the 'foo', 'bar' and 'baz' services.
And if we want less:
$ python app.py --log-level WARNING start foo
$ python app.py --log-level WARNING stop foo bar
$ python app.py --log-level WARNING restart foo bar baz
In this case, the commands don’t print anything to the console, since nothing
at WARNING level or above is logged by them.
A Qt GUI for logging¶
A question that comes up from time to time is about how to log to a GUI application. The Qt framework is a popular cross-platform UI framework with Python bindings using PySide2 or PyQt5 libraries.
The following example shows how to log to a Qt GUI. This introduces a simple
QtHandler class which takes a callable, which should be a slot in the main
thread that does GUI updates. A worker thread is also created to show how you
can log to the GUI from both the UI itself (via a button for manual logging)
as well as a worker thread doing work in the background (here, just logging
messages at random levels with random short delays in between).
The worker thread is implemented using Qt’s QThread class rather than the
threading module, as there are circumstances where one has to use
QThread, which offers better integration with other Qt components.
The code should work with recent releases of either PySide2 or PyQt5.
You should be able to adapt the approach to earlier versions of Qt. Please
refer to the comments in the code snippet for more detailed information.
import datetime
import logging
import random
import sys
import time
# Deal with minor differences between PySide2 and PyQt5
try:
from PySide2 import QtCore, QtGui, QtWidgets
Signal = QtCore.Signal
Slot = QtCore.Slot
except ImportError:
from PyQt5 import QtCore, QtGui, QtWidgets
Signal = QtCore.pyqtSignal
Slot = QtCore.pyqtSlot
logger = logging.getLogger(__name__)
#
# Signals need to be contained in a QObject or subclass in order to be correctly
# initialized.
#
class Signaller(QtCore.QObject):
signal = Signal(str, logging.LogRecord)
#
# Output to a Qt GUI is only supposed to happen on the main thread. So, this
# handler is designed to take a slot function which is set up to run in the main
# thread. In this example, the function takes a string argument which is a
# formatted log message, and the log record which generated it. The formatted
# string is just a convenience - you could format a string for output any way
# you like in the slot function itself.
#
# You specify the slot function to do whatever GUI updates you want. The handler
# doesn't know or care about specific UI elements.
#
class QtHandler(logging.Handler):
def __init__(self, slotfunc, *args, **kwargs):
super().__init__(*args, **kwargs)
self.signaller = Signaller()
self.signaller.signal.connect(slotfunc)
def emit(self, record):
s = self.format(record)
self.signaller.signal.emit(s, record)
#
# This example uses QThreads, which means that the threads at the Python level
# are named something like "Dummy-1". The function below gets the Qt name of the
# current thread.
#
def ctname():
return QtCore.QThread.currentThread().objectName()
#
# Used to generate random levels for logging.
#
LEVELS = (logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR,
logging.CRITICAL)
#
# This worker class represents work that is done in a thread separate to the
# main thread. The way the thread is kicked off to do work is via a button press
# that connects to a slot in the worker.
#
# Because the default threadName value in the LogRecord isn't much use, we add
# a qThreadName which contains the QThread name as computed above, and pass that
# value in an "extra" dictionary which is used to update the LogRecord with the
# QThread name.
#
# This example worker just outputs messages sequentially, interspersed with
# random delays of the order of a few seconds.
#
class Worker(QtCore.QObject):
@Slot()
def start(self):
extra = {'qThreadName': ctname() }
logger.debug('Started work', extra=extra)
i = 1
# Let the thread run until interrupted. This allows reasonably clean
# thread termination.
while not QtCore.QThread.currentThread().isInterruptionRequested():
delay = 0.5 + random.random() * 2
time.sleep(delay)
level = random.choice(LEVELS)
logger.log(level, 'Message after delay of %3.1f: %d', delay, i, extra=extra)
i += 1
#
# Implement a simple UI for this cookbook example. This contains:
#
# * A read-only text edit window which holds formatted log messages
# * A button to start work and log stuff in a separate thread
# * A button to log something from the main thread
# * A button to clear the log window
#
class Window(QtWidgets.QWidget):
COLORS = {
logging.DEBUG: 'black',
logging.INFO: 'blue',
logging.WARNING: 'orange',
logging.ERROR: 'red',
logging.CRITICAL: 'purple',
}
def __init__(self, app):
super().__init__()
self.app = app
self.textedit = te = QtWidgets.QPlainTextEdit(self)
# Set whatever the default monospace font is for the platform
f = QtGui.QFont('nosuchfont')
f.setStyleHint(f.Monospace)
te.setFont(f)
te.setReadOnly(True)
PB = QtWidgets.QPushButton
self.work_button = PB('Start background work', self)
self.log_button = PB('Log a message at a random level', self)
self.clear_button = PB('Clear log window', self)
self.handler = h = QtHandler(self.update_status)
# Remember to use qThreadName rather than threadName in the format string.
fs = '%(asctime)s %(qThreadName)-12s %(levelname)-8s %(message)s'
formatter = logging.Formatter(fs)
h.setFormatter(formatter)
logger.addHandler(h)
# Set up to terminate the QThread when we exit
app.aboutToQuit.connect(self.force_quit)
# Lay out all the widgets
layout = QtWidgets.QVBoxLayout(self)
layout.addWidget(te)
layout.addWidget(self.work_button)
layout.addWidget(self.log_button)
layout.addWidget(self.clear_button)
self.setFixedSize(900, 400)
# Connect the non-worker slots and signals
self.log_button.clicked.connect(self.manual_update)
self.clear_button.clicked.connect(self.clear_display)
# Start a new worker thread and connect the slots for the worker
self.start_thread()
self.work_button.clicked.connect(self.worker.start)
# Once started, the button should be disabled
self.work_button.clicked.connect(lambda : self.work_button.setEnabled(False))
def start_thread(self):
self.worker = Worker()
self.worker_thread = QtCore.QThread()
self.worker.setObjectName('Worker')
self.worker_thread.setObjectName('WorkerThread') # for qThreadName
self.worker.moveToThread(self.worker_thread)
# This will start an event loop in the worker thread
self.worker_thread.start()
def kill_thread(self):
# Just tell the worker to stop, then tell it to quit and wait for that
# to happen
self.worker_thread.requestInterruption()
if self.worker_thread.isRunning():
self.worker_thread.quit()
self.worker_thread.wait()
else:
print('worker has already exited.')
def force_quit(self):
# For use when the window is closed
if self.worker_thread.isRunning():
self.kill_thread()
# The functions below update the UI and run in the main thread because
# that's where the slots are set up
@Slot(str, logging.LogRecord)
def update_status(self, status, record):
color = self.COLORS.get(record.levelno, 'black')
s = '<pre><font color="%s">%s</font></pre>' % (color, status)
self.textedit.appendHtml(s)
@Slot()
def manual_update(self):
# This function uses the formatted message passed in, but also uses
# information from the record to format the message in an appropriate
# color according to its severity (level).
level = random.choice(LEVELS)
extra = {'qThreadName': ctname() }
logger.log(level, 'Manually logged!', extra=extra)
@Slot()
def clear_display(self):
self.textedit.clear()
def main():
QtCore.QThread.currentThread().setObjectName('MainThread')
logging.getLogger().setLevel(logging.DEBUG)
app = QtWidgets.QApplication(sys.argv)
example = Window(app)
example.show()
sys.exit(app.exec_())
if __name__=='__main__':
main()
Logging to syslog with RFC5424 support¶
Although RFC 5424 dates from 2009, most syslog servers are configured by detault to
use the older RFC 3164, which hails from 2001. When logging was added to Python
in 2003, it supported the earlier (and only existing) protocol at the time. Since
RFC5424 came out, as there has not been widespread deployment of it in syslog
servers, the SysLogHandler functionality has not been
updated.
RFC 5424 contains some useful features such as support for structured data, and if you need to be able to log to a syslog server with support for it, you can do so with a subclassed handler which looks something like this:
import datetime
import logging.handlers
import re
import socket
import time
class SysLogHandler5424(logging.handlers.SysLogHandler):
tz_offset = re.compile(r'([+-]\d{2})(\d{2})$')
escaped = re.compile(r'([\]"\\])')
def __init__(self, *args, **kwargs):
self.msgid = kwargs.pop('msgid', None)
self.appname = kwargs.pop('appname', None)
super().__init__(*args, **kwargs)
def format(self, record):
version = 1
asctime = datetime.datetime.fromtimestamp(record.created).isoformat()
m = self.tz_offset.match(time.strftime('%z'))
has_offset = False
if m and time.timezone:
hrs, mins = m.groups()
if int(hrs) or int(mins):
has_offset = True
if not has_offset:
asctime += 'Z'
else:
asctime += f'{hrs}:{mins}'
try:
hostname = socket.gethostname()
except Exception:
hostname = '-'
appname = self.appname or '-'
procid = record.process
msgid = '-'
msg = super().format(record)
sdata = '-'
if hasattr(record, 'structured_data'):
sd = record.structured_data
# This should be a dict where the keys are SD-ID and the value is a
# dict mapping PARAM-NAME to PARAM-VALUE (refer to the RFC for what these
# mean)
# There's no error checking here - it's purely for illustration, and you
# can adapt this code for use in production environments
parts = []
def replacer(m):
g = m.groups()
return '\\' + g[0]
for sdid, dv in sd.items():
part = f'[{sdid}'
for k, v in dv.items():
s = str(v)
s = self.escaped.sub(replacer, s)
part += f' {k}="{s}"'
part += ']'
parts.append(part)
sdata = ''.join(parts)
return f'{version} {asctime} {hostname} {appname} {procid} {msgid} {sdata} {msg}'
You’ll need to be familiar with RFC 5424 to fully understand the above code, and it may be that you have slightly different needs (e.g. for how you pass structural data to the log). Nevertheless, the above should be adaptable to your speciric needs. With the above handler, you’d pass structured data using something like this:
sd = {
'foo@12345': {'bar': 'baz', 'baz': 'bozz', 'fizz': r'buzz'},
'foo@54321': {'rab': 'baz', 'zab': 'bozz', 'zzif': r'buzz'}
}
extra = {'structured_data': sd}
i = 1
logger.debug('Message %d', i, extra=extra)
How to treat a logger like an output stream¶
Sometimes, you need to interface to a third-party API which expects a file-like object to write to, but you want to direct the API’s output to a logger. You can do this using a class which wraps a logger with a file-like API. Here’s a short script illustrating such a class:
import logging
class LoggerWriter:
def __init__(self, logger, level):
self.logger = logger
self.level = level
def write(self, message):
if message != '\n': # avoid printing bare newlines, if you like
self.logger.log(self.level, message)
def flush(self):
# doesn't actually do anything, but might be expected of a file-like
# object - so optional depending on your situation
pass
def close(self):
# doesn't actually do anything, but might be expected of a file-like
# object - so optional depending on your situation. You might want
# to set a flag so that later calls to write raise an exception
pass
def main():
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger('demo')
info_fp = LoggerWriter(logger, logging.INFO)
debug_fp = LoggerWriter(logger, logging.DEBUG)
print('An INFO message', file=info_fp)
print('A DEBUG message', file=debug_fp)
if __name__ == "__main__":
main()
When this script is run, it prints
INFO:demo:An INFO message
DEBUG:demo:A DEBUG message
You could also use LoggerWriter to redirect sys.stdout and
sys.stderr by doing something like this:
import sys
sys.stdout = LoggerWriter(logger, logging.INFO)
sys.stderr = LoggerWriter(logger, logging.WARNING)
You should do this after configuring logging for your needs. In the above
example, the basicConfig() call does this (using the
sys.stderr value before it is overwritten by a LoggerWriter
instance). Then, you’d get this kind of result:
>>> print('Foo')
INFO:demo:Foo
>>> print('Bar', file=sys.stderr)
WARNING:demo:Bar
>>>
Of course, these above examples show output according to the format used by
basicConfig(), but you can use a different formatter when you
configure logging.
Note that with the above scheme, you are somewhat at the mercy of buffering and
the sequence of write calls which you are intercepting. For example, with the
definition of LoggerWriter above, if you have the snippet
sys.stderr = LoggerWriter(logger, logging.WARNING)
1 / 0
then running the script results in
WARNING:demo:Traceback (most recent call last):
WARNING:demo: File "/home/runner/cookbook-loggerwriter/test.py", line 53, in <module>
WARNING:demo:
WARNING:demo:main()
WARNING:demo: File "/home/runner/cookbook-loggerwriter/test.py", line 49, in main
WARNING:demo:
WARNING:demo:1 / 0
WARNING:demo:ZeroDivisionError
WARNING:demo::
WARNING:demo:division by zero
As you can see, this output isn’t ideal. That’s because the underlying code
which writes to sys.stderr makes mutiple writes, each of which results in a
separate logged line (for example, the last three lines above). To get around
this problem, you need to buffer things and only output log lines when newlines
are seen. Let’s use a slghtly better implementation of LoggerWriter:
class BufferingLoggerWriter(LoggerWriter):
def __init__(self, logger, level):
super().__init__(logger, level)
self.buffer = ''
def write(self, message):
if '\n' not in message:
self.buffer += message
else:
parts = message.split('\n')
if self.buffer:
s = self.buffer + parts.pop(0)
self.logger.log(self.level, s)
self.buffer = parts.pop()
for part in parts:
self.logger.log(self.level, part)
This just buffers up stuff until a newline is seen, and then logs complete lines. With this approach, you get better output:
WARNING:demo:Traceback (most recent call last):
WARNING:demo: File "/home/runner/cookbook-loggerwriter/main.py", line 55, in <module>
WARNING:demo: main()
WARNING:demo: File "/home/runner/cookbook-loggerwriter/main.py", line 52, in main
WARNING:demo: 1/0
WARNING:demo:ZeroDivisionError: division by zero
Patterns to avoid¶
Although the preceding sections have described ways of doing things you might need to do or deal with, it is worth mentioning some usage patterns which are unhelpful, and which should therefore be avoided in most cases. The following sections are in no particular order.
Opening the same log file multiple times¶
On Windows, you will generally not be able to open the same file multiple times as this will lead to a “file is in use by another process” error. However, on POSIX platforms you’ll not get any errors if you open the same file multiple times. This could be done accidentally, for example by:
Adding a file handler more than once which references the same file (e.g. by a copy/paste/forget-to-change error).
Opening two files that look different, as they have different names, but are the same because one is a symbolic link to the other.
Forking a process, following which both parent and child have a reference to the same file. This might be through use of the
multiprocessingmodule, for example.
Opening a file multiple times might appear to work most of the time, but can lead to a number of problems in practice:
Logging output can be garbled because multiple threads or processes try to write to the same file. Although logging guards against concurrent use of the same handler instance by multiple threads, there is no such protection if concurrent writes are attempted by two different threads using two different handler instances which happen to point to the same file.
An attempt to delete a file (e.g. during file rotation) silently fails, because there is another reference pointing to it. This can lead to confusion and wasted debugging time - log entries end up in unexpected places, or are lost altogether. Or a file that was supposed to be moved remains in place, and grows in size unexpectedly despite size-based rotation being supposedly in place.
Use the techniques outlined in Logging to a single file from multiple processes to circumvent such issues.
Using loggers as attributes in a class or passing them as parameters¶
While there might be unusual cases where you’ll need to do this, in general
there is no point because loggers are singletons. Code can always access a
given logger instance by name using logging.getLogger(name), so passing
instances around and holding them as instance attributes is pointless. Note
that in other languages such as Java and C#, loggers are often static class
attributes. However, this pattern doesn’t make sense in Python, where the
module (and not the class) is the unit of software decomposition.
Adding handlers other than NullHandler to a logger in a library¶
Configuring logging by adding handlers, formatters and filters is the
responsibility of the application developer, not the library developer. If you
are maintaining a library, ensure that you don’t add handlers to any of your
loggers other than a NullHandler instance.
Creating a lot of loggers¶
Loggers are singletons that are never freed during a script execution, and so creating lots of loggers will use up memory which can’t then be freed. Rather than create a logger per e.g. file processed or network connection made, use the existing mechanisms for passing contextual information into your logs and restrict the loggers created to those describing areas within your application (generally modules, but occasionally slightly more fine-grained than that).
Outros recursos¶
Ver também
- Módulo
logging Referência da API para o módulo de logging.
- Módulo
logging.config API de configuração para o módulo logging.
- Módulo
logging.handlers Manipuladores úteis incluídos no módulo logging.