Author: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
.
Using logging in multiple modules
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:
importloggingimportauxiliary_module# create logger with 'spam_application'logger=logging.getLogger('spam_application')logger.setLevel(logging.DEBUG)# create file handler which logs even debug messagesfh=logging.FileHandler('spam.log')fh.setLevel(logging.DEBUG)# create console handler with a higher log levelch=logging.StreamHandler()ch.setLevel(logging.ERROR)# create formatter and add it to the handlersformatter=logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')fh.setFormatter(formatter)ch.setFormatter(formatter)# add the handlers to the loggerlogger.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:
importlogging# create loggermodule_logger=logging.getLogger('spam_application.auxiliary')classAuxiliary:def__init__(self):self.logger=logging.getLogger('spam_application.auxiliary.Auxiliary')self.logger.info('creating an instance of Auxiliary')defdo_something(self):self.logger.info('doing something')a=1+1self.logger.info('done doing something')defsome_function():module_logger.info('received a call to "some_function"')The output looks like this:
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:
importloggingimportthreadingimporttimedefworker(arg):whilenotarg['stop']:logging.debug('Hi from myfunc')time.sleep(0.5)defmain():logging.basicConfig(level=logging.DEBUG,format='%(relativeCreated)6d%(threadName)s%(message)s')info={'stop':False}thread=threading.Thread(target=worker,args=(info,))thread.start()whileTrue:try:logging.debug('Hello from main')time.sleep(0.75)exceptKeyboardInterrupt:info['stop']=Truebreakthread.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
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:
importlogginglogger=logging.getLogger('simple_example')logger.setLevel(logging.DEBUG)# create file handler which logs even debug messagesfh=logging.FileHandler('spam.log')fh.setLevel(logging.DEBUG)# create console handler with a higher log levelch=logging.StreamHandler()ch.setLevel(logging.ERROR)# create formatter and add it to the handlersformatter=logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')ch.setFormatter(formatter)fh.setFormatter(formatter)# add the handlers to loggerlogger.addHandler(ch)logger.addHandler(fh)# 'application' codelogger.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:
importlogging# set up logging to file - see previous section for more detailslogging.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.stderrconsole=logging.StreamHandler()console.setLevel(logging.INFO)# set a format which is simpler for console useformatter=logging.Formatter('%(name)-12s: %(levelname)-8s%(message)s')# tell the handler to use this formatconsole.setFormatter(formatter)# add the handler to the root loggerlogging.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 INFO and WARNING to sys.stdout
Send messages of severity ERROR and above to sys.stderr
Send messages of severity DEBUG and above to file app.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 only events of this severity and higher will be tracked 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:
deffilter_maker(level):level=getattr(logging,level)deffilter(record):returnrecord.levelno<=levelreturnfilterThis 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:
importjsonimportloggingimportlogging.configCONFIG='''{ "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" ] }}'''deffilter_maker(level):level=getattr(logging,level)deffilter(record):returnrecord.levelno<=levelreturnfilterlogging.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:
pythonmain.py2>stderr.log>stdout.log We can see the results are as expected:
$more*.log :::::::::::::: app.log :::::::::::::: DEBUG-ADEBUGmessage INFO-AnINFOmessage WARNING-AWARNINGmessage ERROR-AnERRORmessage CRITICAL-ACRITICALmessage :::::::::::::: stderr.log :::::::::::::: ERROR-AnERRORmessage CRITICAL-ACRITICALmessage :::::::::::::: stdout.log :::::::::::::: INFO-AnINFOmessage WARNING-AWARNINGmessage Configuration server example
Here is an example of a module using the logging configuration server:
importloggingimportlogging.configimporttimeimportos# read initial config filelogging.config.fileConfig('logging.conf')# create and start listener on port 9999t=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 pressedwhileTrue:logger.debug('debug message')logger.info('info message')logger.warning('warn message')logger.error('error message')logger.critical('critical message')time.sleep(5)exceptKeyboardInterrupt:# cleanuplogging.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 pythonimportsocket,sys,structwithopen(sys.argv[1],'rb')asf:data_to_send=f.read()HOST='localhost'PORT=9999s=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
: 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
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
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
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
, which has been designed as the counterpart to
. 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
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 sizequeue_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! Note
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
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.
Changed in version 3.5: Prior to Python 3.5, the
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.
Changed in version 3.14: The
can be started (and stopped) via the
statement. For example:
withQueueListener(que,handler)aslistener:# The queue listener automatically starts# when the 'with' block is entered.pass# The queue listener automatically stops once# the 'with' block is exited.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
instance to the root logger at the sending end:
importlogging,logging.handlersrootLogger=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 picklerootLogger.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
module. Here is a basic working example:
importpickleimportloggingimportlogging.handlersimportsocketserverimportstructclassLogRecordStreamHandler(socketserver.StreamRequestHandler):"""Handler for a streaming logging request. This basically logs the record using whatever logging policy is configured locally. """defhandle(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. """whileTrue:chunk=self.connection.recv(4)iflen(chunk)<4:breakslen=struct.unpack('>L',chunk)[0]chunk=self.connection.recv(slen)whilelen(chunk)<slen:chunk=chunk+self.connection.recv(slen-len(chunk))obj=self.unPickle(chunk)record=logging.makeLogRecord(obj)self.handleLogRecord(record)defunPickle(self,data):returnpickle.loads(data)defhandleLogRecord(self,record):# if a name is specified, we use the named logger rather than the one# implied by the record.ifself.server.lognameisnotNone:name=self.server.lognameelse:name=record.namelogger=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)classLogRecordSocketReceiver(socketserver.ThreadingTCPServer):""" Simple TCP socket-based logging receiver suitable for testing. """allow_reuse_address=Truedef__init__(self,host='localhost',port=logging.handlers.DEFAULT_TCP_LOGGING_PORT,handler=LogRecordStreamHandler):socketserver.ThreadingTCPServer.__init__(self,(host,port),handler)self.abort=0self.timeout=1self.logname=Nonedefserve_until_stopped(self):importselectabort=0whilenotabort:rd,wr,ex=select.select([self.socket.fileno()],[],[],self.timeout)ifrd:self.handle_request()abort=self.abortdefmain():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
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
.
which provides the bare-bones files to run the above functionality using Supervisor. It consists of the following files:
File
Purpose
prepare.sh
A Bash script to prepare the environment for testing
supervisor.conf
The Supervisor configuration file, which has entries for the listener and a multi-process web application
ensure_app.sh
A Bash script to ensure that Supervisor is running with the above configuration
log_listener.py
The socket listener program which receives log events and records them to a file
main.py
A simple web application which performs logging via a socket connected to the listener
webapp.json
A JSON configuration file for the web application
client.py
A Python script to exercise the web application
The web application uses
, 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
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 bashprepare.sh to get things ready. This creates a run subdirectory to contain Supervisor-related and log files, and a venv subdirectory to contain a virtual environment into which bottle, gunicorn and supervisor are installed.
Run bashensure_app.sh to ensure that Supervisor is running with the above configuration.
Run venv/bin/pythonclient.py to exercise the web application, which will lead to records being written to the log.
Inspect the log files in the run subdirectory. You should see the most recent log lines in files matching the pattern app.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-csupervisor.confshutdown.
You may need to tweak the configuration files in the unlikely event that the configured ports clash with something else in your test environment.
The default configuration uses a TCP socket on port 9020. You can use a Unix Domain socket instead of a TCP socket by doing the following:
In listener.json, add a socket key with the path to the domain socket you want to use. If this key is present, the listener listens on the corresponding domain socket and not on a TCP socket (the port key is ignored).
In webapp.json, change the socket handler configuration dictionary so that the host value is the path to the domain socket, and set the port value to null.
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
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
class. This class is designed to look like a
, so that you can call
,
,
,
,
,
and
. 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
, you pass it a
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:
defdebug(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
method of
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
instance’s __dict__, allowing you to use customized strings with your
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
and override
to do what you need. Here is a simple example:
classCustomAdapter(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. """defprocess(self,msg,kwargs):return'[%s] %s'%(self.extra['connid'],msg),kwargswhich 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
- 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 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
.
For example in a web application, the request being processed (or at least, the interesting parts of it) can be stored in a threadlocal (
) 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:
importloggingfromrandomimportchoiceclassContextFilter(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']deffilter(self,record):record.ip=choice(ContextFilter.IPS)record.user=choice(ContextFilter.USERS)returnTrueif__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')forxinrange(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
module has provided context-local storage which works for both
and
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.pyimportloggingimporttimelogger=logging.getLogger(__name__)defuseful():# Just a representative event logged from the librarylogger.debug('Hello from webapplib!')# Just sleep for a bit so other threads get to runtime.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.pyimportargparsefromcontextvarsimportContextVarimportloggingimportosfromrandomimportchoiceimportthreadingimportwebappliblogger=logging.getLogger(__name__)root=logging.getLogger()root.setLevel(logging.DEBUG)classRequest:""" 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=methodself.ip=ipself.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 usernameformatter=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 processingctx_request=ContextVar('request')ctx_appname=ContextVar('appname')classInjectingFilter(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=appdeffilter(self,record):request=ctx_request.get()record.method=request.methodrecord.ip=request.iprecord.user=request.userrecord.appName=appName=ctx_appname.get()returnappName==self.app.nameclassWebApp:""" A dummy web application class which has its own handler and filter for a webapp-specific log. """def__init__(self,name):self.name=namehandler=logging.FileHandler(name+'.log','w')f=InjectingFilter(self)handler.setFormatter(formatter)handler.addFilter(f)root.addHandler(handler)self.num_requests=0defprocess_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+=1logger.debug('Request processing started')webapplib.useful()logger.debug('Request processing finished')defmain():fn=os.path.splitext(os.path.basename(__file__))[0]adhf=argparse.ArgumentDefaultsHelpFormatterap=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_argumentaa('--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 randomlyapp1=WebApp('app1')app2=WebApp('app2')apps=[app1,app2]threads=[]# Add a common handler which will capture all eventshandler=logging.FileHandler('app.log','w')handler.setFormatter(formatter)root.addHandler(handler)# Generate calls to process requestsforiinrange(options.count):try:# Pick an app at random and a request for it to processapp=choice(apps)request=choice(REQUESTS)# Process the request in its own threadt=threading.Thread(target=app.process_request,args=(request,))threads.append(t)t.start()exceptKeyboardInterrupt:break# Wait for the threads to terminatefortinthreads:t.join()forappinapps: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$pythonmain.py app1processed51requests app2processed49requests ~/logging-contextual-webapp$wc-l*.log 153app1.log 147app2.log 300app.log 600total ~/logging-contextual-webapp$head-3app1.log Thread-3(process_request)app1__main__jim192.168.3.21POSTRequestprocessingstarted Thread-3(process_request)app1webapplibjim192.168.3.21POSTHellofromwebapplib! Thread-5(process_request)app1__main__jim192.168.3.21POSTRequestprocessingstarted ~/logging-contextual-webapp$head-3app2.log Thread-1(process_request)app2__main__sheila192.168.2.21GETRequestprocessingstarted Thread-1(process_request)app2webapplibsheila192.168.2.21GETHellofromwebapplib! Thread-2(process_request)app2__main__jim192.168.2.20GETRequestprocessingstarted ~/logging-contextual-webapp$headapp.log Thread-1(process_request)app2__main__sheila192.168.2.21GETRequestprocessingstarted Thread-1(process_request)app2webapplibsheila192.168.2.21GETHellofromwebapplib! Thread-2(process_request)app2__main__jim192.168.2.20GETRequestprocessingstarted Thread-3(process_request)app1__main__jim192.168.3.21POSTRequestprocessingstarted Thread-2(process_request)app2webapplibjim192.168.2.20GETHellofromwebapplib! Thread-3(process_request)app1webapplibjim192.168.3.21POSTHellofromwebapplib! Thread-4(process_request)app2__main__fred192.168.2.22GETRequestprocessingstarted Thread-5(process_request)app1__main__jim192.168.3.21POSTRequestprocessingstarted Thread-4(process_request)app2webapplibfred192.168.2.22GETHellofromwebapplib! Thread-6(process_request)app1__main__jim192.168.3.21POSTRequestprocessingstarted ~/logging-contextual-webapp$grepapp1app1.log|wc-l 153 ~/logging-contextual-webapp$grepapp2app2.log|wc-l 147 ~/logging-contextual-webapp$grepapp1app.log|wc-l 153 ~/logging-contextual-webapp$grepapp2app.log|wc-l 147Imparting contextual information in handlers
Each
has its own chain of filters. If you want to add contextual information to a
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:
importcopyimportloggingdeffilter(record:logging.LogRecord):record=copy.copy(record)record.user='jim'returnrecordif__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
, 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.)
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
class from the
module to serialize access to the file from your processes. The stdlib
and subclasses do not make use of multiprocessing.
Alternatively, you can use a Queue and a
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 codeimportloggingimportlogging.handlersimportmultiprocessing# Next two import lines for this demo onlyfromrandomimportchoice,randomimporttime## 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.deflistener_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.deflistener_process(queue,configurer):configurer()whileTrue:try:record=queue.get()ifrecordisNone:# We send this as a sentinel to tell the listener to quit.breaklogger=logging.getLogger(record.name)logger.handle(record)# No level or filter logic applied - just do it!exceptException:importsys,tracebackprint('Whoops! Problem:',file=sys.stderr)traceback.print_exc(file=sys.stderr)# Arrays used for random selections in this demoLEVELS=[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.defworker_configurer(queue):h=logging.handlers.QueueHandler(queue)# Just the one handler neededroot=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!defworker_process(queue,configurer):configurer(queue)name=multiprocessing.current_process().nameprint('Worker started: %s'%name)foriinrange(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.defmain():queue=multiprocessing.Queue(-1)listener=multiprocessing.Process(target=listener_process,args=(queue,listener_configurer))listener.start()workers=[]foriinrange(10):worker=multiprocessing.Process(target=worker_process,args=(queue,worker_configurer))workers.append(worker)worker.start()forwinworkers: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:
importloggingimportlogging.configimportlogging.handlersfrommultiprocessingimportProcess,Queueimportrandomimportthreadingimporttimedeflogger_thread(q):whileTrue:record=q.get()ifrecordisNone:breaklogger=logging.getLogger(record.name)logger.handle(record)defworker_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']foriinrange(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=[]foriinrange(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...forwpinworkers:wp.join()# And now tell the logging thread to finish up, tooq.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 aboveand you can then replace the worker creation from this:
workers=[]foriinrange(10):worker=multiprocessing.Process(target=worker_process,args=(queue,worker_configurer))workers.append(worker)worker.start()forwinworkers:w.join()to this (remembering to first import
):
withconcurrent.futures.ProcessPoolExecutor(max_workers=10)asexecutor:foriinrange(10):executor.submit(worker_process,queue,worker_configurer)Deploying web applications using Gunicorn and uWSGI
When deploying web applications using
or
(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
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
:
importglobimportloggingimportlogging.handlersLOG_FILENAME='logging_rotatingfile_example.out'# Set up a specific logger with our desired output levelmy_logger=logging.getLogger('MyLogger')my_logger.setLevel(logging.DEBUG)# Add the log message handler to the loggerhandler=logging.handlers.RotatingFileHandler(LOG_FILENAME,maxBytes=20,backupCount=5)my_logger.addHandler(handler)# Log some messagesforiinrange(20):my_logger.debug('i = %d'%i)# See what files are createdlogfiles=glob.glob('%s*'%LOG_FILENAME)forfilenameinlogfiles: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:
(added in Python 2.4) and
(added in Python 2.6).
Logging (as of 3.2) provides improved support for these two additional formatting styles. The
class been enhanced to take an additional, optional keyword parameter named style. This defaults to '%', but other possible values are '{' and '$', which correspond to the other two formatting styles. Backwards compatibility is maintained by default (as you would expect), but by explicitly specifying a style parameter, you get the ability to specify format strings which work with
or
. Here’s an example console session to show the possibilities:
>>> importlogging>>> root=logging.getLogger()>>> root.setLevel(logging.DEBUG)>>> handler=logging.StreamHandler()>>> bf=logging.Formatter('{asctime}{name}{levelname:8s}{message}',... style='{')>>> handler.setFormatter(bf)>>> root.addHandler(handler)>>> logger=logging.getLogger('foo.bar')>>> logger.debug('This is a DEBUG message')2010-10-28 15:11:55,341 foo.bar DEBUG This is a DEBUG message>>> logger.critical('This is a CRITICAL message')2010-10-28 15:12:11,526 foo.bar CRITICAL This is a CRITICAL message>>> df=logging.Formatter('$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
or
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:
classBraceMessage:def__init__(self,fmt,/,*args,**kwargs):self.fmt=fmtself.args=argsself.kwargs=kwargsdef__str__(self):returnself.fmt.format(*self.args,**self.kwargs)classDollarMessage:def__init__(self,fmt,/,**kwargs):self.fmt=fmtself.kwargs=kwargsdef__str__(self):fromstringimportTemplatereturnTemplate(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
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):
>>> fromwhereverimportBraceMessageas__>>> print(__('Message with {0}{name}',2,name='placeholders'))Message with 2 placeholders>>> classPoint: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)>>> fromwhereverimportDollarMessageas__>>> 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
to achieve a similar effect to the above, as in the following example:
importloggingclassMessage:def__init__(self,fmt,args):self.fmt=fmtself.args=argsdef__str__(self):returnself.fmt.format(*self.args)classStyleAdapter(logging.LoggerAdapter):deflog(self,level,msg,/,*args,stacklevel=1,**kwargs):ifself.isEnabledFor(level):msg,kwargs=self.process(msg,kwargs)self.logger.log(level,Message(msg,args),**kwargs,stacklevel=stacklevel+1)logger=StyleAdapter(logging.getLogger(__name__))defmain():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 3.8 or later.
Customizing LogRecord
Every logging event is represented by a
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:
, which is called in the normal process of logging an event. This invoked
directly to create an instance.
, 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 a
, or in JSON form via an
).
This has usually meant that if you need to do anything special with a
, you’ve had to do one of the following.
Create your own
subclass, which overrides
, and set it using
before any loggers that you care about are instantiated.
Add a
to a logger or handler, which does the necessary special manipulation you need when its
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
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
. 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
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,
creation is done through a factory, which you can specify. The factory is just a callable you can set with
, and interrogate with
. The factory is invoked with the same signature as the LogRecord constructor, as
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()defrecord_factory(*args,**kwargs):record=old_factory(*args,**kwargs)record.custom_attribute=0xdecafbadreturnrecordlogging.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
does not provide the desired result.
Subclassing QueueHandler and QueueListener- a ZeroMQ example
Subclass QueueHandler
You can use a
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’):
importzmq# using pyzmq, the Python binding for ZeroMQimportjson# for serializing records portablyctx=zmq.Context()sock=zmq.Socket(ctx,zmq.PUB)# or zmq.PUSH, or other suitable valuesock.bind('tcp://*:5556')# or whereverclassZeroMQSocketHandler(QueueHandler):defenqueue(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:
classZeroMQSocketHandler(QueueHandler):def__init__(self,uri,socktype=zmq.PUB,ctx=None):self.ctx=ctxorzmq.Context()socket=zmq.Socket(self.ctx,socktype)socket.bind(uri)super().__init__(socket)defenqueue(self,record):self.queue.send_json(record.__dict__)defclose(self):self.queue.close()Subclass QueueListener
You can also subclass
to get messages from other kinds of queues, for example a ZeroMQ ‘subscribe’ socket. Here’s an example:
classZeroMQSocketListener(QueueListener):def__init__(self,uri,/,*handlers,**kwargs):self.ctx=kwargs.get('ctx')orzmq.Context()socket=zmq.Socket(self.ctx,zmq.SUB)socket.setsockopt_string(zmq.SUBSCRIBE,'')# subscribe to everythingsocket.connect(uri)super().__init__(socket,*handlers,**kwargs)defdequeue(self):msg=self.queue.recv_json()returnlogging.makeLogRecord(msg)Subclassing QueueHandler and QueueListener- a pynng example
In a similar way to the above section, we can implement a listener and handler using
, which is a Python binding to
, billed as a spiritual successor to ZeroMQ. The following snippets illustrate – you can test them in an environment which has pynng installed. Just for variety, we present the listener first.
Subclass QueueListener
# listener.pyimportjsonimportloggingimportlogging.handlersimportpynngDEFAULT_ADDR="tcp://localhost:13232"interrupted=FalseclassNNGSocketListener(logging.handlers.QueueListener):def__init__(self,uri,/,*handlers,**kwargs):# Have a timeout for interruptibility, and open a# subscriber socketsocket=pynng.Sub0(listen=uri,recv_timeout=500)# The b'' subscription matches all topicstopics=kwargs.pop('topics',None)orb''socket.subscribe(topics)# We treat the socket as a queuesuper().__init__(socket,*handlers,**kwargs)defdequeue(self,block):data=None# Keep looping while not interrupted and no data received over the# socketwhilenotinterrupted:try:data=self.queue.recv(block=block)breakexceptpynng.Timeout:passexceptpynng.Closed:# sometimes happens when you hit Ctrl-CbreakifdataisNone:returnNone# Get the logging event sent from a publisherevent=json.loads(data.decode('utf-8'))returnlogging.makeLogRecord(event)defenqueue_sentinel(self):# Not used in this implementation, as the socket isn't really a# queuepasslogging.getLogger('pynng').propagate=Falselistener=NNGSocketListener(DEFAULT_ADDR,logging.StreamHandler(),topics=b'')listener.start()print('Press Ctrl-C to stop.')try:whileTrue:passexceptKeyboardInterrupt:interrupted=Truefinally:listener.stop()Subclass QueueHandler
# sender.pyimportjsonimportloggingimportlogging.handlersimporttimeimportrandomimportpynngDEFAULT_ADDR="tcp://localhost:13232"classNNGSocketHandler(logging.handlers.QueueHandler):def__init__(self,uri):socket=pynng.Pub0(dial=uri,send_timeout=500)super().__init__(socket)defenqueue(self,record):# Send the record as UTF-8 encoded JSONd=dict(record.__dict__)data=json.dumps(d)self.queue.send(data.encode('utf-8'))defclose(self):self.queue.close()logging.getLogger('pynng').propagate=Falsehandler=NNGSocketHandler(DEFAULT_ADDR)# Make sure the process ID is in the outputlogging.basicConfig(level=logging.DEBUG,handlers=[logging.StreamHandler(),handler],format='%(levelname)-8s%(name)10s%(process)6s%(message)s')levels=(logging.DEBUG,logging.INFO,logging.WARNING,logging.ERROR,logging.CRITICAL)logger_names=('myapp','myapp.lib1','myapp.lib2')msgno=1whileTrue:# Just randomly select some loggers and levels and log awaylevel=random.choice(levels)logger=logging.getLogger(random.choice(logger_names))logger.log(level,'Message no. %5d'%msgno)msgno+=1delay=random.random()*2+0.5time.sleep(delay)You can run the above two snippets in separate command shells. If we run the listener in one shell and run the sender in two separate shells, we should see something like the following. In the first sender shell:
$ pythonsender.py DEBUG myapp 613 Message no. 1WARNING myapp.lib2 613 Message no. 2CRITICAL myapp.lib2 613 Message no. 3WARNING myapp.lib2 613 Message no. 4CRITICAL myapp.lib1 613 Message no. 5DEBUG myapp 613 Message no. 6CRITICAL myapp.lib1 613 Message no. 7INFO myapp.lib1 613 Message no. 8(and so on)In the second sender shell:
$ pythonsender.py INFO myapp.lib2 657 Message no. 1CRITICAL myapp.lib2 657 Message no. 2CRITICAL myapp 657 Message no. 3CRITICAL myapp.lib1 657 Message no. 4INFO myapp.lib1 657 Message no. 5WARNING myapp.lib2 657 Message no. 6CRITICAL myapp 657 Message no. 7DEBUG myapp.lib1 657 Message no. 8(and so on)In the listener shell:
$ pythonlistener.py Press Ctrl-C to stop.DEBUG myapp 613 Message no. 1WARNING myapp.lib2 613 Message no. 2INFO myapp.lib2 657 Message no. 1CRITICAL myapp.lib2 613 Message no. 3CRITICAL myapp.lib2 657 Message no. 2CRITICAL myapp 657 Message no. 3WARNING myapp.lib2 613 Message no. 4CRITICAL myapp.lib1 613 Message no. 5CRITICAL myapp.lib1 657 Message no. 4INFO myapp.lib1 657 Message no. 5DEBUG myapp 613 Message no. 6WARNING myapp.lib2 657 Message no. 6CRITICAL myapp 657 Message no. 7CRITICAL myapp.lib1 613 Message no. 7INFO myapp.lib1 613 Message no. 8DEBUG myapp.lib1 657 Message no. 8(and so on)As you can see, the logging from the two sender processes is interleaved in the listener’s output.
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
to put the configuration into effect:
LOGGING={'version':1,'disable_existing_loggers':False,'formatters':{'verbose':{'format':'{levelname}{asctime}{module}{process:d}{thread:d}{message}','style':'{',},'simple':{'format':'{levelname}{message}','style':'{',},},'filters':{'special':{'()':'project.logging.SpecialFilter','foo':'bar',},},'handlers':{'console':{'level':'INFO','class':'logging.StreamHandler','formatter':'simple',},'mail_admins':{'level':'ERROR','class':'django.utils.log.AdminEmailHandler','filters':['special']}},'loggers':{'django':{'handlers':['console'],'propagate':True,},'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
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:
importgzipimportloggingimportlogging.handlersimportosimportshutildefnamer(name):returnname+".gz"defrotator(source,dest):withopen(source,'rb')asf_in:withgzip.open(dest,'wb')asf_out:shutil.copyfileobj(f_in,f_out)os.remove(source)rh=logging.handlers.RotatingFileHandler('rotated.log',maxBytes=128,backupCount=5)rh.rotator=rotatorrh.namer=namerroot=logging.getLogger()root.setLevel(logging.INFO)root.addHandler(rh)f=logging.Formatter('%(asctime)s%(message)s')rh.setFormatter(f)foriinrange(1000):root.info(f'Message no. {i+1}')After running this, you will see six new files, five of which are compressed:
$ lsrotated.log* rotated.log rotated.log.2.gz rotated.log.4.gzrotated.log.1.gz rotated.log.3.gz rotated.log.5.gz$ zcatrotated.log.1.gz 2023-01-20 02:28:17,767 Message no. 9962023-01-20 02:28:17,767 Message no. 9972023-01-20 02:28:17,767 Message no. 998A 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:
importloggingimportlogging.configimportlogging.handlersfrommultiprocessingimportProcess,Queue,Event,current_processimportosimportrandomimporttimeclassMyHandler:""" 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. """defhandle(self,record):ifrecord.name=="root":logger=logging.getLogger()else:logger=logging.getLogger(record.name)iflogger.isEnabledFor(record.levelno):# The process name is transformed just to show that it's the listener# doing the logging to files and consolerecord.processName='%s (for %s)'%(current_process().name,record.processName)logger.handle(record)deflistener_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()ifos.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()defworker_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']ifos.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 ...')foriinrange(100):lvl=random.choice(levels)logger=logging.getLogger(random.choice(loggers))logger.log(lvl,'Message no. %d',i)time.sleep(0.01)defmain():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=[]foriinrange(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.forwpinworkers: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
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
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
-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
instance to your
instance, 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
-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:
importjsonimportloggingclassStructuredMessage:def__init__(self,message,/,**kwargs):self.message=messageself.kwargs=kwargsdef__str__(self):return'%s >>> %s'%(self.message,json.dumps(self.kwargs))_=StructuredMessage# optional, to improve readabilitylogging.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:
importjsonimportloggingclassEncoder(json.JSONEncoder):defdefault(self,o):ifisinstance(o,set):returntuple(o)elifisinstance(o,str):returno.encode('unicode_escape').decode('ascii')returnsuper().default(o)classStructuredMessage:def__init__(self,message,/,**kwargs):self.message=messageself.kwargs=kwargsdef__str__(self):s=Encoder().encode(self.kwargs)return'%s >>> %s'%(self.message,s)_=StructuredMessage# optional, to improve readabilitydefmain():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
There are times when you want to customize logging handlers in particular ways, and if you use
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
, but the file handlers in the stdlib don’t offer built-in support. You can customize handler creation using a plain function such as:
defowned_file_handler(filename,mode='a',encoding=None,owner=None):ifowner:ifnotos.path.exists(filename):open(filename,'a').close()shutil.chown(filename,*owner)returnlogging.FileHandler(filename,mode,encoding)You can then specify, in a logging configuration passed to
, 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:
importlogging,logging.config,os,shutildefowned_file_handler(filename,mode='a',encoding=None,owner=None):ifowner:ifnotos.path.exists(filename):open(filename,'a').close()shutil.chown(filename,*owner)returnlogging.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:
$ sudopython3.3chowntest.py $ catchowntest.log 2013-11-05 09:34:51,128 DEBUG mylogger A debug message$ ls-lchowntest.log -rw-r--r-- 1 pulse pulse 55 2013-11-05 09:34 chowntest.logNote that this example uses Python 3.3 because that’s where
makes an appearance. This approach should work with any Python version that supports
- 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.
.
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
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
.
Of course, the approach could also be extended to types of handler other than a
- 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
gained a style keyword parameter which, while defaulting to % for backward compatibility, allowed the specification of { or $ to support the formatting approaches supported by
and
. 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 (
,
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
or
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
changes mentioned above, the logging package gained the ability to allow users to set their own
subclasses, using the
function. You can use this to set your own subclass of LogRecord, which does the Right Thing by overriding the
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
and
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
Using arbitrary objects as messages
) that when logging you can use an arbitrary object as a message format string, and that the logging package will call
on that object to get the actual format string. Consider the following two classes:
classBraceMessage:def__init__(self,fmt,/,*args,**kwargs):self.fmt=fmtself.args=argsself.kwargs=kwargsdef__str__(self):returnself.fmt.format(*self.args,**self.kwargs)classDollarMessage:def__init__(self,fmt,/,**kwargs):self.fmt=fmtself.kwargs=kwargsdef__str__(self):fromstringimportTemplatereturnTemplate(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
:
>>> __=BraceMessage>>> print(__('Message with {0}{1}',2,'placeholders'))Message with 2 placeholders>>> classPoint: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
:
>>> __=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
You can configure filters using
, though it might not be obvious at first glance how to do it (hence this recipe). Since
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
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:
importloggingimportlogging.configimportsysclassMyFilter(logging.Filter):def__init__(self,param=None):self.param=paramdeffilter(self,record):ifself.paramisNone:allow=Trueelse:allow=self.paramnotinrecord.msgifallow:record.msg='changed: '+record.msgreturnallowLOGGING={'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
. For example, you could have used the text 'ext://__main__.MyFilter' instead of MyFilter in the above example.
As well as for filters, this technique can also be used to configure custom handlers and formatters. See
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:
importloggingclassOneLineExceptionFormatter(logging.Formatter):defformatException(self,exc_info):""" Format an exception so that it prints on a single line. """result=super().formatException(exc_info)returnrepr(result)# or format into one line however you want todefformat(self,record):s=super().format(record)ifrecord.exc_text:s=s.replace('\n','')+'|'returnsdefconfigure_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)defmain():configure_logging()logging.info('Sample message')try:x=1/0exceptZeroDivisionErrorase: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: division by zero|'Traceback (most recent call last):\n File "logtest7.py", line 30, in main\n x = 1 / 0\nZeroDivisionError: division by zero'| While the above treatment is simplistic, it points the way to how exception information can be formatted to your liking. The
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
. 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:
importloggingimportsubprocessimportsysclassTTSHandler(logging.Handler):defemit(self,record):msg=self.format(record)# Speak slowly in a female English voicecmd=['espeak','-s150','-ven+f3',msg]p=subprocess.Popen(cmd,stdout=subprocess.PIPE,stderr=subprocess.STDOUT)# wait for the program to finishp.communicate()defconfigure_logging():h=TTSHandler()root=logging.getLogger()root.addHandler(h)# the default formatter just returns the messageroot.setLevel(logging.DEBUG)defmain():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
which writes to sys.stderr, logging.ERROR and 100 respectively.
Here’s the script:
importloggingfromlogging.handlersimportMemoryHandlerimportsyslogger=logging.getLogger(__name__)logger.addHandler(logging.NullHandler())deflog_if_errors(logger,target_handler=None,flush_level=None,capacity=None):iftarget_handlerisNone:target_handler=logging.StreamHandler()ifflush_levelisNone:flush_level=logging.ERRORifcapacityisNone:capacity=100handler=MemoryHandler(capacity,flushLevel=flush_level,target=target_handler)defdecorator(fn):defwrapper(*args,**kwargs):logger.addHandler(handler)try:returnfn(*args,**kwargs)exceptException:logger.exception('call failed')raisefinally:super(MemoryHandler,handler).flush()logger.removeHandler(handler)returnwrapperreturndecoratordefwrite_line(s):sys.stderr.write('%s\n'%s)deffoo(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')iffail: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')returnfaildecorated_foo=log_if_errors(logger)(foo)if__name__=='__main__':logger.setLevel(logging.DEBUG)write_line('Calling undecorated foo with False')assertnotfoo(False)write_line('Calling undecorated foo with True')assertfoo(True)write_line('Calling decorated foo with False')assertnotdecorated_foo(False)write_line('Calling decorated foo with True')assertdecorated_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)deffoo(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
. 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.)
importloggingimportlogging.handlersimportsmtplibclassBufferingSMTPHandler(logging.handlers.BufferingHandler):def__init__(self,mailhost,port,username,password,fromaddr,toaddrs,subject,capacity):logging.handlers.BufferingHandler.__init__(self,capacity)self.mailhost=mailhostself.mailport=portself.username=usernameself.password=passwordself.fromaddr=fromaddrifisinstance(toaddrs,str):toaddrs=[toaddrs]self.toaddrs=toaddrsself.subject=subjectself.setFormatter(logging.Formatter("%(asctime)s%(levelname)-5s%(message)s"))defflush(self):iflen(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)forrecordinself.buffer:s=self.format(record)msg=msg+s+"\r\n"smtp.sendmail(self.fromaddr,self.toaddrs,msg)smtp.quit()exceptException:iflogging.raiseExceptions:raiseself.buffer=[]if__name__=='__main__':importargparseap=argparse.ArgumentParser()aa=ap.add_argumentaa('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)foriinrange(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:
importloggingimporttimeclassUTCFormatter(logging.Formatter):converter=time.gmtimeand you can then use the UTCFormatter in your code instead of
. If you want to do that via configuration, you can use the
API with an approach illustrated by the following complete example:
importloggingimportlogging.configimporttimeclassUTCFormatter(logging.Formatter):converter=time.gmtimeLOGGING={'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:
importloggingimportsysclassLoggingContext:def__init__(self,logger,level=None,handler=None,close=True):self.logger=loggerself.level=levelself.handler=handlerself.close=closedef__enter__(self):ifself.levelisnotNone:self.old_level=self.logger.levelself.logger.setLevel(self.level)ifself.handler:self.logger.addHandler(self.handler)def__exit__(self,et,ev,tb):ifself.levelisnotNone:self.logger.setLevel(self.old_level)ifself.handler:self.logger.removeHandler(self.handler)ifself.handlerandself.close:self.handler.close()# implicit return of None => don't swallow exceptionsIf 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.')withLoggingContext(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)withLoggingContext(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:
$ pythonlogctx.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:
$ pythonlogctx.py2>/dev/null 5. This should appear twice - once on stderr and once on stdout.Once again, but piping stdout to /dev/null, we get:
$ pythonlogctx.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:
importargparseimportimportlibimportloggingimportosimportsysdefmain(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)return1# Could get fancy here and load configuration from file or dictionarylogging.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.pyimportlogginglogger=logging.getLogger(__name__)defcommand(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.pyimportlogginglogger=logging.getLogger(__name__)defcommand(options):n=len(options.names)ifn==1:plural=''services='\'%s\''%options.names[0]else:plural='s'services=', '.join('\'%s\''%namefornameinoptions.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.pyimportlogginglogger=logging.getLogger(__name__)defcommand(options):n=len(options.names)ifn==1:plural=''services='\'%s\''%options.names[0]else:plural='s'services=', '.join('\'%s\''%namefornameinoptions.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:
$ pythonapp.pystartfoo INFO start Started the 'foo' service.$ pythonapp.pystopfoobar INFO stop Stopped the 'foo' and 'bar' services.$ pythonapp.pyrestartfoobarbaz 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:
$ pythonapp.py--log-levelDEBUGstartfoo DEBUG start About to start fooINFO start Started the 'foo' service.$ pythonapp.py--log-levelDEBUGstopfoobar DEBUG stop About to stop 'foo' and 'bar'INFO stop Stopped the 'foo' and 'bar' services.$ pythonapp.py--log-levelDEBUGrestartfoobarbaz DEBUG restart About to restart 'foo', 'bar' and 'baz'INFO restart Restarted the 'foo', 'bar' and 'baz' services.And if we want less:
$ pythonapp.py--log-levelWARNINGstartfoo $ pythonapp.py--log-levelWARNINGstopfoobar $ pythonapp.py--log-levelWARNINGrestartfoobarbaz 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
framework is a popular cross-platform UI framework with Python bindings using
or
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
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 any of PySide6, PyQt6, 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.
importloggingimportrandomimportsysimporttime# Deal with minor differences between different Qt packagestry:fromPySide6importQtCore,QtGui,QtWidgetsSignal=QtCore.SignalSlot=QtCore.SlotexceptImportError:try:fromPyQt6importQtCore,QtGui,QtWidgetsSignal=QtCore.pyqtSignalSlot=QtCore.pyqtSlotexceptImportError:try:fromPySide2importQtCore,QtGui,QtWidgetsSignal=QtCore.SignalSlot=QtCore.SlotexceptImportError:fromPyQt5importQtCore,QtGui,QtWidgetsSignal=QtCore.pyqtSignalSlot=QtCore.pyqtSlotlogger=logging.getLogger(__name__)## Signals need to be contained in a QObject or subclass in order to be correctly# initialized.#classSignaller(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.#classQtHandler(logging.Handler):def__init__(self,slotfunc,*args,**kwargs):super().__init__(*args,**kwargs)self.signaller=Signaller()self.signaller.signal.connect(slotfunc)defemit(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.#defctname():returnQtCore.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.#classWorker(QtCore.QObject):@Slot()defstart(self):extra={'qThreadName':ctname()}logger.debug('Started work',extra=extra)i=1# Let the thread run until interrupted. This allows reasonably clean# thread termination.whilenotQtCore.QThread.currentThread().isInterruptionRequested():delay=0.5+random.random()*2time.sleep(delay)try:ifrandom.random()<0.1:raiseValueError('Exception raised: %d'%i)else:level=random.choice(LEVELS)logger.log(level,'Message after delay of %3.1f: %d',delay,i,extra=extra)exceptValueErrorase:logger.exception('Failed: %s',e,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#classWindow(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=appself.textedit=te=QtWidgets.QPlainTextEdit(self)# Set whatever the default monospace font is for the platformf=QtGui.QFont('nosuchfont')ifhasattr(f,'Monospace'):f.setStyleHint(f.Monospace)else:f.setStyleHint(f.StyleHint.Monospace)# for Qt6te.setFont(f)te.setReadOnly(True)PB=QtWidgets.QPushButtonself.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 exitapp.aboutToQuit.connect(self.force_quit)# Lay out all the widgetslayout=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 signalsself.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 workerself.start_thread()self.work_button.clicked.connect(self.worker.start)# Once started, the button should be disabledself.work_button.clicked.connect(lambda:self.work_button.setEnabled(False))defstart_thread(self):self.worker=Worker()self.worker_thread=QtCore.QThread()self.worker.setObjectName('Worker')self.worker_thread.setObjectName('WorkerThread')# for qThreadNameself.worker.moveToThread(self.worker_thread)# This will start an event loop in the worker threadself.worker_thread.start()defkill_thread(self):# Just tell the worker to stop, then tell it to quit and wait for that# to happenself.worker_thread.requestInterruption()ifself.worker_thread.isRunning():self.worker_thread.quit()self.worker_thread.wait()else:print('worker has already exited.')defforce_quit(self):# For use when the window is closedifself.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)defupdate_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()defmanual_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()defclear_display(self):self.textedit.clear()defmain():QtCore.QThread.currentThread().setObjectName('MainThread')logging.getLogger().setLevel(logging.DEBUG)app=QtWidgets.QApplication(sys.argv)example=Window(app)example.show()ifhasattr(app,'exec'):rc=app.exec()else:rc=app.exec_()sys.exit(rc)if__name__=='__main__':main()Logging to syslog with RFC5424 support
Although
dates from 2009, most syslog servers are configured by default to use the older
, which hails from 2001. When logging was added to Python in 2003, it supported the earlier (and only existing) protocol at the time. Since RFC 5424 came out, as there has not been widespread deployment of it in syslog servers, the
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:
importdatetimeasdtimportlogging.handlersimportreimportsocketimporttimeclassSysLogHandler5424(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)defformat(self,record):version=1asctime=dt.datetime.fromtimestamp(record.created).isoformat()m=self.tz_offset.match(time.strftime('%z'))has_offset=Falseifmandtime.timezone:hrs,mins=m.groups()ifint(hrs)orint(mins):has_offset=Trueifnothas_offset:asctime+='Z'else:asctime+=f'{hrs}:{mins}'try:hostname=socket.gethostname()exceptException:hostname='-'appname=self.appnameor'-'procid=record.processmsgid='-'msg=super().format(record)sdata='-'ifhasattr(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 environmentsparts=[]defreplacer(m):g=m.groups()return'\\'+g[0]forsdid,dvinsd.items():part=f'[{sdid}'fork,vindv.items():s=str(v)s=self.escaped.sub(replacer,s)part+=f' {k}="{s}"'part+=']'parts.append(part)sdata=''.join(parts)returnf'{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 specific 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=1logger.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:
importloggingclassLoggerWriter:def__init__(self,logger,level):self.logger=loggerself.level=leveldefwrite(self,message):ifmessage!='\n':# avoid printing bare newlines, if you likeself.logger.log(self.level,message)defflush(self):# doesn't actually do anything, but might be expected of a file-like# object - so optional depending on your situationpassdefclose(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 exceptionpassdefmain():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:
importsyssys.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
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, the examples above show output according to the format used by
, 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)1/0then 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 multiple 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 slightly better implementation of LoggerWriter:
classBufferingLoggerWriter(LoggerWriter):def__init__(self,logger,level):super().__init__(logger,level)self.buffer=''defwrite(self,message):if'\n'notinmessage:self.buffer+=messageelse:parts=message.split('\n')ifself.buffer:s=self.buffer+parts.pop(0)self.logger.log(self.level,s)self.buffer=parts.pop()forpartinparts: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 How to uniformly handle newlines in logging output
Usually, messages that are logged (say to console or file) consist of a single line of text. However, sometimes there is a need to handle messages with multiple lines - whether because a logging format string contains newlines, or logged data contains newlines. If you want to handle such messages uniformly, so that each line in the logged message appears uniformly formatted as if it was logged separately, you can do this using a handler mixin, as in the following snippet:
# Assume this is in a module mymixins.pyimportcopyclassMultilineMixin:defemit(self,record):s=record.getMessage()if'\n'notins:super().emit(record)else:lines=s.splitlines()rec=copy.copy(record)rec.args=Noneforlineinlines:rec.msg=linesuper().emit(rec)You can use the mixin as in the following script:
importloggingfrommymixinsimportMultilineMixinlogger=logging.getLogger(__name__)classStreamHandler(MultilineMixin,logging.StreamHandler):passif__name__=='__main__':logging.basicConfig(level=logging.DEBUG,format='%(asctime)s%(levelname)-9s%(message)s',handlers=[StreamHandler()])logger.debug('Single line')logger.debug('Multiple lines:\nfool me once ...')logger.debug('Another single line')logger.debug('Multiple lines:\n%s','fool me ...\ncan\'t get fooled again')The script, when run, prints something like:
2025-07-02 13:54:47,234 DEBUG Single line 2025-07-02 13:54:47,234 DEBUG Multiple lines: 2025-07-02 13:54:47,234 DEBUG fool me once ... 2025-07-02 13:54:47,234 DEBUG Another single line 2025-07-02 13:54:47,234 DEBUG Multiple lines: 2025-07-02 13:54:47,234 DEBUG fool me ... 2025-07-02 13:54:47,234 DEBUG can't get fooled again If, on the other hand, you are concerned about
, you can use a formatter which escapes newlines, as per the following example:
importlogginglogger=logging.getLogger(__name__)classEscapingFormatter(logging.Formatter):defformat(self,record):s=super().format(record)returns.replace('\n',r'\n')if__name__=='__main__':h=logging.StreamHandler()h.setFormatter(EscapingFormatter('%(asctime)s%(levelname)-9s%(message)s'))logging.basicConfig(level=logging.DEBUG,handlers=[h])logger.debug('Single line')logger.debug('Multiple lines:\nfool me once ...')logger.debug('Another single line')logger.debug('Multiple lines:\n%s','fool me ...\ncan\'t get fooled again')You can, of course, use whatever escaping scheme makes the most sense for you. The script, when run, should produce output like this:
2025-07-09 06:47:33,783 DEBUG Single line 2025-07-09 06:47:33,783 DEBUG Multiple lines:\nfool me once ... 2025-07-09 06:47:33,783 DEBUG Another single line 2025-07-09 06:47:33,783 DEBUG Multiple lines:\nfool me ...\ncan't get fooled again Escaping behaviour can’t be the stdlib default , as it would break backwards compatibility.
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
module, 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
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
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
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).
Other resources