| Author: | Vinay Sajip <vinay_sajip at red-dove dot com> |
|---|
This page contains a number of recipes related to logging, which have been founduseful in the past.
Multiple calls tologging.getLogger('someLogger') return a reference to thesame logger object. This is true not only within the same module, but alsoacross modules as long as it is in the same Python interpreter process. It istrue for references to the same object; additionally, application code candefine and configure a parent logger in one module and create (but notconfigure) a child logger in a separate module, and all logger calls to thechild 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:
2005-03-2323:47:11,663-spam_application-INFO-creatinganinstanceofauxiliary_module.Auxiliary2005-03-2323:47:11,665-spam_application.auxiliary.Auxiliary-INFO-creatinganinstanceofAuxiliary2005-03-2323:47:11,665-spam_application-INFO-createdaninstanceofauxiliary_module.Auxiliary2005-03-2323:47:11,668-spam_application-INFO-callingauxiliary_module.Auxiliary.do_something2005-03-2323:47:11,668-spam_application.auxiliary.Auxiliary-INFO-doingsomething2005-03-2323:47:11,669-spam_application.auxiliary.Auxiliary-INFO-donedoingsomething2005-03-2323:47:11,670-spam_application-INFO-finishedauxiliary_module.Auxiliary.do_something2005-03-2323:47:11,671-spam_application-INFO-callingauxiliary_module.some_function()2005-03-2323:47:11,672-spam_application.auxiliary-INFO-receivedacallto'some_function'2005-03-2323:47:11,673-spam_application-INFO-donewithauxiliary_module.some_function()
Loggers are plain Python objects. TheaddHandler() method has nominimum or maximum quota for the number of handlers you may add. Sometimes itwill be beneficial for an application to log all messages of all severities to atext file while simultaneously logging errors or above to the console. To setthis up, simply configure the appropriate handlers. The logging calls in theapplication code will remain unchanged. Here is a slight modification to theprevious 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.warn('warn message')logger.error('error message')logger.critical('critical message')
Notice that the ‘application’ code does not care about multiple handlers. Allthat changed was the addition and configuration of a new handler namedfh.
The ability to create new handlers with higher- or lower-severity filters can bevery helpful when writing and testing an application. Instead of using manyprint statements for debugging, uselogger.debug: Unlike the printstatements, which you will have to delete or comment out later, the logger.debugstatements can remain intact in the source code and remain dormant until youneed them again. At that time, the only change that needs to happen is tomodify the severity level of the logger and/or handler to debug.
Let’s say you want to log to console and file with different message formats andin differing circumstances. Say you want to log messages with levels of DEBUGand 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 consolemessages 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='/temp/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:INFOJackdawslovemybigsphinxofquartz.myapp.area1:INFOHowquicklydaftjumpingzebrasvex.myapp.area2:WARNINGJailzestyvixenwhograbbedpayfromquack.myapp.area2:ERRORThefiveboxingwizardsjumpquickly.
and in the file you will see something like
10-2222:19rootINFOJackdawslovemybigsphinxofquartz.10-2222:19myapp.area1DEBUGQuickzephyrsblow,vexingdaftJim.10-2222:19myapp.area1INFOHowquicklydaftjumpingzebrasvex.10-2222:19myapp.area2WARNINGJailzestyvixenwhograbbedpayfromquack.10-2222:19myapp.area2ERRORThefiveboxingwizardsjumpquickly.
As you can see, the DEBUG message only shows up in the file. The other messagesare sent to both destinations.
This example uses console and file handlers, but you can use any number andcombination of handlers you choose.
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.warn('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 loggingconfiguration:
#!/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')
Sometimes you have to get your logging handlers to do their work withoutblocking 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 theSMTPHandler: sending emails can take a long time, for anumber of reasons outside the developer’s control (for example, a poorlyperforming mail or network infrastructure). But almost any network-basedhandler can block: Even aSocketHandler operation may do aDNS query under the hood which is too slow (and this query can be deep in thesocket 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 aQueueHandler to those loggers which are accessed fromperformance-critical threads. They simply write to their queue, which can besized to a large enough capacity or initialized with no upper bound to theirsize. The write to the queue will typically be accepted quickly, though youwill probably need to catch thequeue.Full exception as a precautionin your code. If you are a library developer who has performance-criticalthreads in their code, be sure to document this (together with a suggestion toattach onlyQueueHandlers to your loggers) for the benefit of otherdevelopers who will use your code.
The second part of the solution isQueueListener, which has beendesigned as the counterpart toQueueHandler. AQueueListener is very simple: it’s passed a queue and some handlers,and it fires up an internal thread which listens to its queue for LogRecordssent fromQueueHandlers (or any other source ofLogRecords, for thatmatter). TheLogRecords are removed from the queue and passed to thehandlers for processing.
The advantage of having a separateQueueListener class is that youcan use the same instance to service multipleQueueHandlers. This is moreresource-friendly than, say, having threaded versions of the existing handlerclasses, 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!
Let’s say you want to send logging events across a network, and handle them atthe receiving end. A simple way of doing this is attaching aSocketHandler 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 thesocketservermodule. 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=1def__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 isprinted on the console; on the server side, you should see something like:
AbouttostartTCPserver...59rootINFOJackdawslovemybigsphinxofquartz.59myapp.area1DEBUGQuickzephyrsblow,vexingdaftJim.69myapp.area1INFOHowquicklydaftjumpingzebrasvex.69myapp.area2WARNINGJailzestyvixenwhograbbedpayfromquack.69myapp.area2ERRORThefiveboxingwizardsjumpquickly.
Note that there are some security issues with pickle in some scenarios. Ifthese affect you, you can use an alternative serialization scheme by overridingthemakePickle() method and implementing youralternative there, as well as adapting the above script to use your alternativeserialization.
Sometimes you want logging output to contain contextual information inaddition to the parameters passed to the logging call. For example, in anetworked application, it may be desirable to log client-specific informationin the log (e.g. remote client’s username, or IP address). Although you coulduse theextra parameter to achieve this, it’s not always convenient to passthe information in this way. While it might be tempting to createLogger instances on a per-connection basis, this is not a good ideabecause these instances are not garbage collected. While this is not a problemin practice, when the number ofLogger instances is dependent on thelevel of granularity you want to use in logging an application, it couldbe hard to manage if the number ofLogger instances becomeseffectively unbounded.
An easy way in which you can pass contextual information to be output alongwith logging event information is to use theLoggerAdapter class.This class is designed to look like aLogger, so that you can calldebug(),info(),warning(),error(),exception(),critical() andlog(). These methods have thesame signatures as their counterparts inLogger, so you can use thetwo types of instances interchangeably.
When you create an instance ofLoggerAdapter, you pass it aLogger instance and a dict-like object which contains your contextualinformation. When you call one of the logging methods on an instance ofLoggerAdapter, it delegates the call to the underlying instance ofLogger passed to its constructor, and arranges to pass the contextualinformation in the delegated call. Here’s a snippet from the code ofLoggerAdapter:
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)
Theprocess() method ofLoggerAdapter is where thecontextual information is added to the logging output. It’s passed the messageand keyword arguments of the logging call, and it passes back (potentially)modified versions of these to use in the call to the underlying logger. Thedefault implementation of this method leaves the message alone, but insertsan ‘extra’ key in the keyword argument whose value is the dict-like objectpassed to the constructor. Of course, if you had passed an ‘extra’ keywordargument 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 aremerged into theLogRecord instance’s __dict__, allowing you to usecustomized strings with yourFormatter instances which know aboutthe keys of the dict-like object. If you need a different method, e.g. if youwant to prepend or append the contextual information to the message string,you just need to subclassLoggerAdapter and overrideprocess() 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),kwargs
which you can use like this:
logger=logging.getLogger(__name__)adapter=CustomAdapter(logger,{'connid':some_conn_id})
Then any events that you log to the adapter will have the value ofsome_conn_id prepended to the log messages.
You don’t need to pass an actual dict to aLoggerAdapter - you couldpass an instance of a class which implements__getitem__ and__iter__ sothat it looks like a dict to logging. This would be useful if you want togenerate values dynamically (whereas the values in a dict would be constant).
You can also add contextual information to log output using a user-definedFilter.Filter instances are allowed to modify theLogRecordspassed to them, including adding additional attributes which can then be outputusing a suitable format string, or if needed a customFormatter.
For example in a web application, the request being processed (or at least,the interesting parts of it) can be stored in a threadlocal(threading.local) variable, and then accessed from aFilter toadd, say, information from the request - say, the remote IP address and remoteuser’s username - to theLogRecord, using the attribute names ‘ip’ and‘user’ as in theLoggerAdapter example above. In that case, the same formatstring can be used to get similar output to that shown above. Here’s an examplescript:
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:
2010-09-0622:38:15,292a.b.cDEBUGIP:123.231.231.123User:fredAdebugmessage2010-09-0622:38:15,300a.b.cINFOIP:192.168.0.1User:sheilaAninfomessagewithsomeparameters2010-09-0622:38:15,300d.e.fCRITICALIP:127.0.0.1User:sheilaAmessageatCRITICALlevelwith2parameters2010-09-0622:38:15,300d.e.fERRORIP:127.0.0.1User:jimAmessageatERRORlevelwith2parameters2010-09-0622:38:15,300d.e.fDEBUGIP:127.0.0.1User:sheilaAmessageatDEBUGlevelwith2parameters2010-09-0622:38:15,300d.e.fERRORIP:123.231.231.123User:fredAmessageatERRORlevelwith2parameters2010-09-0622:38:15,300d.e.fCRITICALIP:192.168.0.1User:jimAmessageatCRITICALlevelwith2parameters2010-09-0622:38:15,300d.e.fCRITICALIP:127.0.0.1User:sheilaAmessageatCRITICALlevelwith2parameters2010-09-0622:38:15,300d.e.fDEBUGIP:192.168.0.1User:jimAmessageatDEBUGlevelwith2parameters2010-09-0622:38:15,301d.e.fERRORIP:127.0.0.1User:sheilaAmessageatERRORlevelwith2parameters2010-09-0622:38:15,301d.e.fDEBUGIP:123.231.231.123User:fredAmessageatDEBUGlevelwith2parameters2010-09-0622:38:15,301d.e.fINFOIP:123.231.231.123User:fredAmessageatINFOlevelwith2parameters
Although logging is thread-safe, and logging to a single file from multiplethreads in a single processis supported, logging to a single file frommultiple processes isnot supported, because there is no standard way toserialize access to a single file across multiple processes in Python. If youneed to log to a single file from multiple processes, one way of doing this isto have all the processes log to aSocketHandler, and have aseparate process which implements a socket server which reads from the socketand logs to file. (If you prefer, you can dedicate one thread in one of theexisting processes to perform this function.)This section documents this approach in more detail andincludes a working socket receiver which can be used as a starting point for youto adapt in your own applications.
If you are using a recent version of Python which includes themultiprocessing module, you could write your own handler which uses theLock class from this module to serialize access to thefile from your processes. The existingFileHandler and subclasses donot make use ofmultiprocessing at present, though they may do so in thefuture. Note that at present, themultiprocessing module does not provideworking lock functionality on all platforms (seehttp://bugs.python.org/issue3770).
Alternatively, you can use aQueue and aQueueHandler to sendall logging events to one of the processes in your multi-process application.The following example script demonstrates how you can do this; in the examplea separate listener process listens for events sent by other processes and logsthem according to its own logging configuration. Although the example onlydemonstrates one way of doing it (for example, you may want to use a listenerthread rather than a separate listener process – the implementation would beanalogous) it does allow for completely different logging configurations forthe listener and the other processes in your application, and can be used asthe 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!except(KeyboardInterrupt,SystemExit):raiseexcept: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)root.setLevel(logging.DEBUG)# send all messages, for demo; no other level or filter logic applied.# 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 aseparate 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. thefoo logger has a special handler which stores all events in thefoo subsystem in a filemplog-foo.log. This will be used by the loggingmachinery in the main process (even though the logging events are generated inthe worker processes) to direct the messages to the appropriate destinations.
Sometimes you want to let a log file grow to a certain size, then open a newfile and log to that. You may want to keep a certain number of these files, andwhen that many files have been created, rotate the files so that the number offiles and the size of the files both remain bounded. For this usage pattern, thelogging package provides aRotatingFileHandler:
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 theapplication:
logging_rotatingfile_example.outlogging_rotatingfile_example.out.1logging_rotatingfile_example.out.2logging_rotatingfile_example.out.3logging_rotatingfile_example.out.4logging_rotatingfile_example.out.5
The most current file is alwayslogging_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 extremeexample. You would want to setmaxBytes to an appropriate value.
When logging was added to the Python standard library, the only way offormatting messages with variable content was to use the %-formattingmethod. Since then, Python has gained two new formatting approaches:string.Template (added in Python 2.4) andstr.format()(added in Python 2.6).
Logging (as of 3.2) provides improved support for these two additionalformatting styles. TheFormatter class been enhanced to take anadditional, optional keyword parameter namedstyle. This defaults to'%', but other possible values are'{' and'$', which correspondto the other two formatting styles. Backwards compatibility is maintained bydefault (as you would expect), but by explicitly specifying a style parameter,you get the ability to specify format strings which work withstr.format() orstring.Template. Here’s an example consolesession to show the possibilities:
>>>importlogging>>>root=logging.getLogger()>>>root.setLevel(logging.DEBUG)>>>handler=logging.StreamHandler()>>>bf=logging.Formatter('{asctime} {name} {levelname:8s} {message}',...style='{')>>>handler.setFormatter(bf)>>>root.addHandler(handler)>>>logger=logging.getLogger('foo.bar')>>>logger.debug('This is a DEBUG message')2010-10-28 15:11:55,341 foo.bar DEBUG This is a DEBUG message>>>logger.critical('This is a CRITICAL message')2010-10-28 15:12:11,526 foo.bar CRITICAL This is a CRITICAL message>>>df=logging.Formatter('$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 iscompletely 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 takepositional parameters for the actual logging message itself, with keywordparameters used only for determining options for how to handle the actuallogging call (e.g. theexc_info keyword parameter to indicate thattraceback information should be logged, or theextra keyword parameterto indicate additional contextual information to be added to the log). Soyou cannot directly make logging calls usingstr.format() orstring.Template syntax, because internally the logging packageuses %-formatting to merge the format string and the variable arguments.There would no changing this while preserving backward compatibility, sinceall logging calls which are out there in existing code will be using %-formatstrings.
There is, however, a way that you can use {}- and $- formatting to constructyour individual log messages. Recall that for a message you can use anarbitrary object as a message format string, and that the logging package willcallstr() on that object to get the actual format string. Consider thefollowing two classes:
classBraceMessage:def__init__(self,fmt,*args,**kwargs):self.fmt=fmtself.args=argsself.kwargs=kwargsdef__str__(self):returnself.fmt.format(*self.args,**self.kwargs)classDollarMessage:def__init__(self,fmt,**kwargs):self.fmt=fmtself.kwargs=kwargsdef__str__(self):fromstringimportTemplatereturnTemplate(self.fmt).substitute(**self.kwargs)
Either of these can be used in place of a format string, to allow {}- or$-formatting to be used to build the actual “message” part which appears in theformatted 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 logsomething, but it’s quite palatable if you use an alias such as __ (doubleunderscore – not to be confused with _, the single underscore used as asynonym/alias forgettext.gettext() or its brethren).
The above classes are not included in Python, though they’re easy enough tocopy and paste into your own code. They can be used as follows (assuming thatthey’re declared in a module calledwherever):
>>>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 useprint() to show how the formatting works, youwould of course uselogger.debug() or similar to actually log using thisapproach.
One thing to note is that you pay no significant performance penalty with thisapproach: the actual formatting happens not when you make the logging call, butwhen (and if) the logged message is actually about to be output to a log by ahandler. So the only slightly unusual thing which might trip you up is that theparentheses go around the format string and the arguments, not just the formatstring. That’s because the __ notation is just syntax sugar for a constructorcall to one of the XXXMessage classes.
If you prefer, you can use aLoggerAdapter to achieve a similar effectto the above, as in the following example:
importloggingclassMessage(object):def__init__(self,fmt,args):self.fmt=fmtself.args=argsdef__str__(self):returnself.fmt.format(*self.args)classStyleAdapter(logging.LoggerAdapter):def__init__(self,logger,extra=None):super(StyleAdapter,self).__init__(logger,extraor{})deflog(self,level,msg,*args,**kwargs):ifself.isEnabledFor(level):msg,kwargs=self.process(msg,kwargs)self.logger._log(level,Message(msg,args),(),**kwargs)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 messageHello,world! when run withPython 3.2 or later.
Every logging event is represented by aLogRecord instance.When an event is logged and not filtered out by a logger’s level, aLogRecord is created, populated with information about the event andthen passed to the handlers for that logger (and its ancestors, up to andincluding the logger where further propagation up the hierarchy is disabled).Before Python 3.2, there were only two places where this creation was done:
This has usually meant that if you need to do anything special with aLogRecord, you’ve had to do one of the following.
The first approach would be a little unwieldy in the scenario where (say)several different libraries wanted to do different things. Each would attemptto set its ownLogger subclass, and the one which did this last wouldwin.
The second approach works reasonably well for many cases, but does not allowyou to e.g. use a specialized subclass ofLogRecord. Librarydevelopers can set a suitable filter on their loggers, but they would have toremember to do this every time they introduced a new logger (which they woulddo 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. Developerscould also add the filter to aNullHandler attached to theirtop-level logger, but this would not be invoked if an application developerattached a handler to a lower-level library logger – so output from thathandler would not reflect the intentions of the library developer.
In Python 3.2 and later,LogRecord creation is done through afactory, which you can specify. The factory is just a callable you can set withsetLogRecordFactory(), and interrogate withgetLogRecordFactory(). The factory is invoked with the samesignature as theLogRecord constructor, asLogRecordis the default setting for the factory.
This approach allows a custom factory to control all aspects of LogRecordcreation. For example, you could return a subclass, or just add some additionalattributes 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 aslong as they don’t overwrite each other’s attributes or unintentionallyoverwrite 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-timeoverhead to all logging operations, and the technique should only be used whenthe use of aFilter does not provide the desired result.
You can use aQueueHandler subclass to send messages to other kindsof queues, for example a ZeroMQ ‘publish’ socket. In the example below,thesocket 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):data=json.dumps(record.__dict__)self.queue.send(data)handler=ZeroMQSocketHandler(sock)
Of course there are other ways of organizing this, for example passing in thedata 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)QueueHandler.__init__(self,socket)defenqueue(self,record):data=json.dumps(record.__dict__)self.queue.send(data)defclose(self):self.queue.close()
You can also subclassQueueListener to get messages from other kindsof 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(zmq.SUBSCRIBE,'')# subscribe to everythingsocket.connect(uri)defdequeue(self):msg=self.queue.recv()returnlogging.makeLogRecord(json.loads(msg))
See also
Below is an example of a logging configuration dictionary - it’s taken fromthedocumentation on the Django project.This dictionary is passed todictConfig() to put the configuration into effect:
LOGGING={'version':1,'disable_existing_loggers':True,'formatters':{'verbose':{'format':'%(levelname)s %(asctime)s %(module)s %(process)d %(thread)d %(message)s'},'simple':{'format':'%(levelname)s %(message)s'},},'filters':{'special':{'()':'project.logging.SpecialFilter','foo':'bar',}},'handlers':{'null':{'level':'DEBUG','class':'django.utils.log.NullHandler',},'console':{'level':'DEBUG','class':'logging.StreamHandler','formatter':'simple'},'mail_admins':{'level':'ERROR','class':'django.utils.log.AdminEmailHandler','filters':['special']}},'loggers':{'django':{'handlers':['null'],'propagate':True,'level':'INFO',},'django.request':{'handlers':['mail_admins'],'level':'ERROR','propagate':False,},'myproject.custom':{'handlers':['console','mail_admins'],'level':'INFO','filters':['special']}}}
For more information about this configuration, you can see therelevantsectionof the Django documentation.
An example of how you can define a namer and rotator is given in the followingsnippet, which shows zlib-based compression of the log file:
defnamer(name):returnname+".gz"defrotator(source,dest):withopen(source,"rb")assf:data=sf.read()compressed=zlib.compress(data,9)withopen(dest,"wb")asdf:df.write(compressed)os.remove(source)rh=logging.handlers.RotatingFileHandler(...)rh.rotator=rotatorrh.namer=namer
These are not “true” .gz files, as they are bare compressed data, with no“container” such as you’d find in an actual gzip file. This snippet is justfor illustration purposes.
The following working example shows how logging can be used with multiprocessingusing configuration files. The configurations are fairly simple, but serve toillustrate how more complex ones could be implemented in a real multiprocessingscenario.
In the example, the main process spawns a listener process and some workerprocesses. Each of the main process, the listener and the workers have threeseparate configurations (the workers all share the same configuration). We cansee logging in the main process, how the workers log to a QueueHandler and howthe listener implements a QueueListener and a more complex loggingconfiguration, and arranges to dispatch events received via the queue to thehandlers specified in the configuration. Note that these configurations arepurely illustrative, but you should be able to adapt this example to your ownscenario.
Here’s the script - the docstrings and the comments hopefully explain how itworks:
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):logger=logging.getLogger(record.name)# 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 heterogenous 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,'formatters':{'detailed':{'class':'logging.Formatter','format':'%(asctime)s %(name)-15s %(levelname)-8s %(processName)-10s %(message)s'}},'handlers':{'console':{'class':'logging.StreamHandler','level':'INFO',},},'root':{'level':'DEBUG','handlers':['console']},}# 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':{'level':'DEBUG','handlers':['queue']},}# 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','level':'INFO','formatter':'simple',},'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']},}# 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()
RFC 5424 requires that aUnicode message be sent to a syslog daemon as a set of bytes which have thefollowing structure: an optional pure-ASCII component, followed by a UTF-8 ByteOrder Mark (BOM), followed by Unicode encoded using UTF-8. (See therelevantsection of the specification.)
In Python 3.1, code was added toSysLogHandler to insert a BOM into the message, butunfortunately, it was implemented incorrectly, with the BOM appearing at thebeginning of the message and hence not allowing any pure-ASCII component toappear before it.
As this behaviour is broken, the incorrect BOM insertion code is being removedfrom Python 3.2.4 and later. However, it is not being replaced, and if youwant to produce RFC 5424-compliant messages which include a BOM, an optionalpure-ASCII sequence before it and arbitrary Unicode after it, encoded usingUTF-8, then you need to do the following:
Attach aFormatter instance to yourSysLogHandler instance, with a format stringsuch as:
'ASCII section\ufeffUnicode section'
The Unicode code point U+FEFF, when encoded using UTF-8, will beencoded as a UTF-8 BOM – the byte-stringb'\xef\xbb\xbf'.
Replace the ASCII section with whatever placeholders you like, but make surethat the data that appears in there after substitution is always ASCII (thatway, it will remain unchanged after UTF-8 encoding).
Replace the Unicode section with whatever placeholders you like; if the datawhich appears there after substitution contains characters outside the ASCIIrange, that’s fine – it will be encoded using UTF-8.
The formatted messagewill be encoded using UTF-8 encoding bySysLogHandler. If you follow the above rules, you should be able to produceRFC 5424-compliant messages. If you don’t, logging may not complain, but yourmessages will not be RFC 5424-compliant, and your syslog daemon may complain.
Although most logging messages are intended for reading by humans, and thus notreadily machine-parseable, there might be cirumstances where you want to outputmessages in a structured format whichis capable of being parsed by a program(without needing complex regular expressions to parse the log message). This isstraightforward to achieve using the logging package. There are a number ofways in which this could be achieved, but the following is a simple approachwhich uses JSON to serialise the event in a machine-parseable manner:
importjsonimportloggingclassStructuredMessage(object):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:
message1>>>{"fnum":123.456,"num":123,"bar":"baz","foo":"bar"}
Note that the order of items might be different according to the version ofPython used.
If you need more specialised processing, you can use a custom JSON encoder,as in the following complete example:
from__future__importunicode_literalsimportjsonimportlogging# This next bit is to ensure the script runs unchanged on 2.x and 3.xtry:unicodeexceptNameError:unicode=strclassEncoder(json.JSONEncoder):defdefault(self,o):ifisinstance(o,set):returntuple(o)elifisinstance(o,unicode):returno.encode('unicode_escape').decode('ascii')returnsuper(Encoder,self).default(o)classStructuredMessage(object):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=set([1,2,3]),snowman='\u2603'))if__name__=='__main__':main()
When the above script is run, it prints:
message1>>>{"snowman":"\u2603","set_value":[1,2,3]}
Note that the order of items might be different according to the version ofPython used.
There are times when you want to customize logging handlers in particular ways,and if you usedictConfig() you may be able to do this withoutsubclassing. As an example, consider that you may want to set the ownership of alog file. On POSIX, this is easily done usingshutil.chown(), but the filehandlers in the stdlib don’t offer built-in support. You can customize handlercreation 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 todictConfig(),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 thepulse user and group,just for the purposes of illustration. Putting it together into a workingscript,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 asroot:
$ sudo python3.3 chowntest.py$ cat chowntest.log2013-11-05 09:34:51,128 DEBUG mylogger A debug message$ ls -l chowntest.log-rw-r--r-- 1 pulse pulse 55 2013-11-05 09:34 chowntest.log
Note that this example uses Python 3.3 because that’s whereshutil.chown()makes an appearance. This approach should work with any Python version thatsupportsdictConfig() - namely, Python 2.7, 3.2 or later. With pre-3.3versions, you would need to implement the actual ownership change using e.g.os.chown().
In practice, the handler-creating function may be in a utility module somewherein your project. Instead of the line in the configuration:
'()':owned_file_handler,
you could use e.g.:
'()':'ext://project.util.owned_file_handler',
whereproject.util can be replaced with the actual name of the packagewhere the function resides. In the above working script, using'ext://__main__.owned_file_handler' should work. Here, the actual callableis resolved bydictConfig() from theext:// specification.
This example hopefully also points the way to how you could implement othertypes of file change - e.g. setting specific POSIX permission bits - in thesame way, usingos.chmod().
Of course, the approach could also be extended to types of handler other than aFileHandler - for example, one of the rotating file handlers,or a different type of handler altogether.
In Python 3.2, theFormatter gained astyle keywordparameter which, while defaulting to% for backward compatibility, allowedthe specification of{ or$ to support the formatting approachessupported bystr.format() andstring.Template. Note that thisgoverns the formatting of logging messages for final output to logs, and iscompletely orthogonal to how an individual logging message is constructed.
Logging calls (debug(),info() etc.) only takepositional parameters for the actual logging message itself, with keywordparameters used only for determining options for how to handle the logging call(e.g. theexc_info keyword parameter to indicate that traceback informationshould be logged, or theextra keyword parameter to indicate additionalcontextual information to be added to the log). So you cannot directly makelogging calls usingstr.format() orstring.Template syntax,because internally the logging package uses %-formatting to merge the formatstring and the variable arguments. There would no changing this while preservingbackward compatibility, since all logging calls which are out there in existingcode 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 anyexisting code could be using a given logger name and using %-formatting.
For logging to work interoperably between any third-party libraries and yourcode, decisions about formatting need to be made at the level of theindividual logging call. This opens up a couple of ways in which alternativeformatting styles can be accommodated.
In Python 3.2, along with theFormatter changes mentionedabove, the logging package gained the ability to allow users to set their ownLogRecord subclasses, using thesetLogRecordFactory() function.You can use this to set your own subclass ofLogRecord, which does theRight Thing by overriding thegetMessage() method. The baseclass implementation of this method is where themsg%args formattinghappens, and where you can substitute your alternate formatting; however, youshould be careful to support all formatting styles and allow %-formatting asthe default, to ensure interoperability with other code. Care should also betaken to callstr(self.msg), just as the base implementation does.
Refer to the reference documentation onsetLogRecordFactory() andLogRecord for more information.
There is another, perhaps simpler way that you can use {}- and $- formatting toconstruct your individual log messages. You may recall (fromUsing arbitrary objects as messages) that when logging you can use an arbitraryobject as a message format string, and that the logging package will callstr() on that object to get the actual format string. Consider thefollowing two classes:
classBraceMessage(object):def__init__(self,fmt,*args,**kwargs):self.fmt=fmtself.args=argsself.kwargs=kwargsdef__str__(self):returnself.fmt.format(*self.args,**self.kwargs)classDollarMessage(object):def__init__(self,fmt,**kwargs):self.fmt=fmtself.kwargs=kwargsdef__str__(self):fromstringimportTemplatereturnTemplate(self.fmt).substitute(**self.kwargs)
Either of these can be used in place of a format string, to allow {}- or$-formatting to be used to build the actual “message” part which appears in theformatted 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 logsomething, you can make it more palatable if you use an alias such asM or_ for the message (or perhaps__, if you are using_ forlocalization).
Examples of this approach are given below. Firstly, formatting withstr.format():
>>>__=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 withstring.Template:
>>>__=DollarMessage>>>print(__('Message with $num $what',num=2,what='placeholders'))Message with 2 placeholders>>>
One thing to note is that you pay no significant performance penalty with thisapproach: the actual formatting happens not when you make the logging call, butwhen (and if) the logged message is actually about to be output to a log by ahandler. So the only slightly unusual thing which might trip you up is that theparentheses go around the format string and the arguments, not just the formatstring. That’s because the __ notation is just syntax sugar for a constructorcall to one of theXXXMessage classes shown above.
Youcan configure filters usingdictConfig(), though itmight not be obvious at first glance how to do it (hence this recipe). SinceFilter is the only filter class included in the standardlibrary, and it is unlikely to cater to many requirements (it’s only there as abase class), you will typically need to define your ownFiltersubclass with an overriddenfilter() 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 themost obvious, but you can provide any callable which returns aFilter 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 whichconstructs the instance, in the form of keyword parameters. When run, the abovescript will print:
changed:hello
which shows that the filter is working as configured.
A couple of extra points to note:
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