Navigationlogging — Logging facility for Python¶New in version 2.3. This module defines functions and classes which implement a flexible error logging system for applications. Logging is performed by calling methods on instances of the Logger class (hereafter called loggers). Each instance has a name, and they are conceptually arranged in a namespace hierarchy using dots (periods) as separators. For example, a logger named “scan” is the parent of loggers “scan.text”, “scan.html” and “scan.pdf”. Logger names can be anything you want, and indicate the area of an application in which a logged message originates. Logged messages also have levels of importance associated with them. The default levels provided are DEBUG, INFO, WARNING, ERROR and CRITICAL. As a convenience, you indicate the importance of a logged message by calling an appropriate method of Logger. The methods are debug(), info(), warning(), error() and critical(), which mirror the default levels. You are not constrained to use these levels: you can specify your own and use a more general Logger method, log(), which takes an explicit level argument. Logging tutorial¶The key benefit of having the logging API provided by a standard library module is that all Python modules can participate in logging, so your application log can include messages from third-party modules. It is, of course, possible to log messages with different verbosity levels or to different destinations. Support for writing log messages to files, HTTP GET/POST locations, email via SMTP, generic sockets, or OS-specific logging mechanisms are all supported by the standard module. You can also create your own log destination class if you have special requirements not met by any of the built-in classes. Simple examples¶Most applications are probably going to want to log to a file, so let’s start with that case. Using the basicConfig() function, we can set up the default handler so that debug messages are written to a file: import logging
LOG_FILENAME = '/tmp/logging_example.out'
logging.basicConfig(filename=LOG_FILENAME,level=logging.DEBUG,)
logging.debug('This message should go to the log file')
And now if we open the file and look at what we have, we should find the log message: DEBUG:root:This message should go to the log file If you run the script repeatedly, the additional log messages are appended to the file. To create a new file each time, you can pass a filemode argument to basicConfig() with a value of 'w'. Rather than managing the file size yourself, though, it is simpler to use a RotatingFileHandler: import glob
import logging
import logging.handlers
LOG_FILENAME = '/tmp/logging_rotatingfile_example.out'
# Set up a specific logger with our desired output level
my_logger = logging.getLogger('MyLogger')
my_logger.setLevel(logging.DEBUG)
# Add the log message handler to the logger
handler = logging.handlers.RotatingFileHandler(
LOG_FILENAME, maxBytes=20, backupCount=5)
my_logger.addHandler(handler)
# Log some messages
for i in range(20):
my_logger.debug('i = %d' % i)
# See what files are created
logfiles = glob.glob('%s*' % LOG_FILENAME)
for filename in logfiles:
print filename
The result should be 6 separate files, each with part of the log history for the application: /tmp/logging_rotatingfile_example.out /tmp/logging_rotatingfile_example.out.1 /tmp/logging_rotatingfile_example.out.2 /tmp/logging_rotatingfile_example.out.3 /tmp/logging_rotatingfile_example.out.4 /tmp/logging_rotatingfile_example.out.5 The most current file is always /tmp/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 .5 file is erased. Obviously this example sets the log length much much too small as an extreme example. You would want to set maxBytes to an appropriate value. Another useful feature of the logging API is the ability to produce different messages at different log levels. This allows you to instrument your code with debug messages, for example, but turning the log level down so that those debug messages are not written for your production system. The default levels are CRITICAL, ERROR, WARNING, INFO, DEBUG and UNSET. The logger, handler, and log message call each specify a level. The log message is only emitted if the handler and logger are configured to emit messages of that level or lower. For example, if a message is CRITICAL, and the logger is set to ERROR, the message is emitted. If a message is a WARNING, and the logger is set to produce only ERRORs, the message is not emitted: import logging
import sys
LEVELS = {'debug': logging.DEBUG,
'info': logging.INFO,
'warning': logging.WARNING,
'error': logging.ERROR,
'critical': logging.CRITICAL}
if len(sys.argv) > 1:
level_name = sys.argv[1]
level = LEVELS.get(level_name, logging.NOTSET)
logging.basicConfig(level=level)
logging.debug('This is a debug message')
logging.info('This is an info message')
logging.warning('This is a warning message')
logging.error('This is an error message')
logging.critical('This is a critical error message')
Run the script with an argument like ‘debug’ or ‘warning’ to see which messages show up at different levels: $ python logging_level_example.py debug DEBUG:root:This is a debug message INFO:root:This is an info message WARNING:root:This is a warning message ERROR:root:This is an error message CRITICAL:root:This is a critical error message $ python logging_level_example.py info INFO:root:This is an info message WARNING:root:This is a warning message ERROR:root:This is an error message CRITICAL:root:This is a critical error message You will notice that these log messages all have root embedded in them. The logging module supports a hierarchy of loggers with different names. An easy way to tell where a specific log message comes from is to use a separate logger object for each of your modules. Each new logger “inherits” the configuration of its parent, and log messages sent to a logger include the name of that logger. Optionally, each logger can be configured differently, so that messages from different modules are handled in different ways. Let’s look at a simple example of how to log from different modules so it is easy to trace the source of the message: import logging
logging.basicConfig(level=logging.WARNING)
logger1 = logging.getLogger('package1.module1')
logger2 = logging.getLogger('package2.module2')
logger1.warning('This message comes from one module')
logger2.warning('And this message comes from another module')
And the output: $ python logging_modules_example.py WARNING:package1.module1:This message comes from one module WARNING:package2.module2:And this message comes from another module There are many more options for configuring logging, including different log message formatting options, having messages delivered to multiple destinations, and changing the configuration of a long-running application on the fly using a socket interface. All of these options are covered in depth in the library module documentation. Loggers¶The logging library takes a modular approach and offers the several categories of components: loggers, handlers, filters, and formatters. Loggers expose the interface that application code directly uses. Handlers send the log records to the appropriate destination. Filters provide a finer grained facility for determining which log records to send on to a handler. Formatters specify the layout of the resultant log record. Logger objects have a threefold job. First, they expose several methods to application code so that applications can log messages at runtime. Second, logger objects determine which log messages to act upon based upon severity (the default filtering facility) or filter objects. Third, logger objects pass along relevant log messages to all interested log handlers. The most widely used methods on logger objects fall into two categories: configuration and message sending.
With the logger object configured, the following methods create log messages:
getLogger() returns a reference to a logger instance with the specified if it it is provided, or root if not. The names are period-separated hierarchical structures. Multiple calls to getLogger() with the same name will return a reference to the same logger object. Loggers that are further down in the hierarchical list are children of loggers higher up in the list. For example, given a logger with a name of foo, loggers with names of foo.bar, foo.bar.baz, and foo.bam are all children of foo. Child loggers propagate messages up to their parent loggers. Because of this, it is unnecessary to define and configure all the loggers an application uses. It is sufficient to configure a top-level logger and create child loggers as needed. Handlers¶Handler objects are responsible for dispatching the appropriate log messages (based on the log messages’ severity) to the handler’s specified destination. Logger objects can add zero or more handler objects to themselves with an addHandler() method. As an example scenario, an application may want to send all log messages to a log file, all log messages of error or higher to stdout, and all messages of critical to an email address. This scenario requires three individual handlers where each handler is responsible for sending messages of a specific severity to a specific location. The standard library includes quite a few handler types; this tutorial uses only StreamHandler and FileHandler in its examples. There are very few methods in a handler for application developers to concern themselves with. The only handler methods that seem relevant for application developers who are using the built-in handler objects (that is, not creating custom handlers) are the following configuration methods:
Application code should not directly instantiate and use handlers. Instead, the Handler class is a base class that defines the interface that all Handlers should have and establishes some default behavior that child classes can use (or override). Formatters¶Formatter objects configure the final order, structure, and contents of the log message. Unlike the base logging.Handler class, application code may instantiate formatter classes, although you could likely subclass the formatter if your application needs special behavior. The constructor takes two optional arguments: a message format string and a date format string. If there is no message format string, the default is to use the raw message. If there is no date format string, the default date format is: %Y-%m-%d %H:%M:%S with the milliseconds tacked on at the end. The message format string uses %(<dictionary key>)s styled string substitution; the possible keys are documented in Formatter Objects. The following message format string will log the time in a human-readable format, the severity of the message, and the contents of the message, in that order: "%(asctime)s - %(levelname)s - %(message)s"
Configuring Logging¶Programmers can configure logging either by creating loggers, handlers, and formatters explicitly in a main module with the configuration methods listed above (using Python code), or by creating a logging config file. The following code is an example of configuring a very simple logger, a console handler, and a simple formatter in a Python module: import logging
# create logger
logger = logging.getLogger("simple_example")
logger.setLevel(logging.DEBUG)
# create console handler and set level to debug
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
# create formatter
formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
# add formatter to ch
ch.setFormatter(formatter)
# add ch to logger
logger.addHandler(ch)
# "application" code
logger.debug("debug message")
logger.info("info message")
logger.warn("warn message")
logger.error("error message")
logger.critical("critical message")
Running this module from the command line produces the following output: $ python simple_logging_module.py 2005-03-19 15:10:26,618 - simple_example - DEBUG - debug message 2005-03-19 15:10:26,620 - simple_example - INFO - info message 2005-03-19 15:10:26,695 - simple_example - WARNING - warn message 2005-03-19 15:10:26,697 - simple_example - ERROR - error message 2005-03-19 15:10:26,773 - simple_example - CRITICAL - critical message The following Python module creates a logger, handler, and formatter nearly identical to those in the example listed above, with the only difference being the names of the objects: import logging
import logging.config
logging.config.fileConfig("logging.conf")
# create logger
logger = logging.getLogger("simpleExample")
# "application" code
logger.debug("debug message")
logger.info("info message")
logger.warn("warn message")
logger.error("error message")
logger.critical("critical message")
Here is the logging.conf file: [loggers] keys=root,simpleExample [handlers] keys=consoleHandler [formatters] keys=simpleFormatter [logger_root] level=DEBUG handlers=consoleHandler [logger_simpleExample] level=DEBUG handlers=consoleHandler qualname=simpleExample propagate=0 [handler_consoleHandler] class=StreamHandler level=DEBUG formatter=simpleFormatter args=(sys.stdout,) [formatter_simpleFormatter] format=%(asctime)s - %(name)s - %(levelname)s - %(message)s datefmt= The output is nearly identical to that of the non-config-file-based example: $ python simple_logging_config.py 2005-03-19 15:38:55,977 - simpleExample - DEBUG - debug message 2005-03-19 15:38:55,979 - simpleExample - INFO - info message 2005-03-19 15:38:56,054 - simpleExample - WARNING - warn message 2005-03-19 15:38:56,055 - simpleExample - ERROR - error message 2005-03-19 15:38:56,130 - simpleExample - CRITICAL - critical message You can see that the config file approach has a few advantages over the Python code approach, mainly separation of configuration and code and the ability of noncoders to easily modify the logging properties. Configuring Logging for a Library¶When developing a library which uses logging, some consideration needs to be given to its configuration. If the using application does not use logging, and library code makes logging calls, then a one-off message “No handlers could be found for logger X.Y.Z” is printed to the console. This message is intended to catch mistakes in logging configuration, but will confuse an application developer who is not aware of logging by the library. In addition to documenting how a library uses logging, a good way to configure library logging so that it does not cause a spurious message is to add a handler which does nothing. This avoids the message being printed, since a handler will be found: it just doesn’t produce any output. If the library user configures logging for application use, presumably that configuration will add some handlers, and if levels are suitably configured then logging calls made in library code will send output to those handlers, as normal. A do-nothing handler can be simply defined as follows: import logging
class NullHandler(logging.Handler):
def emit(self, record):
pass
An instance of this handler should be added to the top-level logger of the logging namespace used by the library. If all logging by a library foo is done using loggers with names matching “foo.x.y”, then the code: import logging
h = NullHandler()
logging.getLogger("foo").addHandler(h)
should have the desired effect. If an organisation produces a number of libraries, then the logger name specified can be “orgname.foo” rather than just “foo”. Logging Levels¶The numeric values of logging levels are given in the following table. These are primarily of interest if you want to define your own levels, and need them to have specific values relative to the predefined levels. If you define a level with the same numeric value, it overwrites the predefined value; the predefined name is lost.
Levels can also be associated with loggers, being set either by the developer or through loading a saved logging configuration. When a logging method is called on a logger, the logger compares its own level with the level associated with the method call. If the logger’s level is higher than the method call’s, no logging message is actually generated. This is the basic mechanism controlling the verbosity of logging output. Logging messages are encoded as instances of the LogRecord class. When a logger decides to actually log an event, a LogRecord instance is created from the logging message. Logging messages are subjected to a dispatch mechanism through the use of handlers, which are instances of subclasses of the Handler class. Handlers are responsible for ensuring that a logged message (in the form of a LogRecord) ends up in a particular location (or set of locations) which is useful for the target audience for that message (such as end users, support desk staff, system administrators, developers). Handlers are passed LogRecord instances intended for particular destinations. Each logger can have zero, one or more handlers associated with it (via the addHandler() method of Logger). In addition to any handlers directly associated with a logger, all handlers associated with all ancestors of the logger are called to dispatch the message. Just as for loggers, handlers can have levels associated with them. A handler’s level acts as a filter in the same way as a logger’s level does. If a handler decides to actually dispatch an event, the emit() method is used to send the message to its destination. Most user-defined subclasses of Handler will need to override this emit(). In addition to the base Handler class, many useful subclasses are provided:
The StreamHandler and FileHandler classes are defined in the core logging package. The other handlers are defined in a sub- module, logging.handlers. (There is also another sub-module, logging.config, for configuration functionality.) Logged messages are formatted for presentation through instances of the Formatter class. They are initialized with a format string suitable for use with the % operator and a dictionary. For formatting multiple messages in a batch, instances of BufferingFormatter can be used. In addition to the format string (which is applied to each message in the batch), there is provision for header and trailer format strings. When filtering based on logger level and/or handler level is not enough, instances of Filter can be added to both Logger and Handler instances (through their addFilter() method). Before deciding to process a message further, both loggers and handlers consult all their filters for permission. If any filter returns a false value, the message is not processed further. The basic Filter functionality allows filtering by specific logger name. If this feature is used, messages sent to the named logger and its children are allowed through the filter, and all others dropped. In addition to the classes described above, there are a number of module- level functions.
See also
Logger Objects¶Loggers have the following attributes and methods. Note that Loggers are never instantiated directly, but always through the module-level function logging.getLogger(name).
Basic example¶Changed in version 2.4: formerly basicConfig() did not take any keyword arguments. The logging package provides a lot of flexibility, and its configuration can appear daunting. This section demonstrates that simple use of the logging package is possible. The simplest example shows logging to the console: import logging
logging.debug('A debug message')
logging.info('Some information')
logging.warning('A shot across the bows')
If you run the above script, you’ll see this: WARNING:root:A shot across the bows Because no particular logger was specified, the system used the root logger. The debug and info messages didn’t appear because by default, the root logger is configured to only handle messages with a severity of WARNING or above. The message format is also a configuration default, as is the output destination of the messages - sys.stderr. The severity level, the message format and destination can be easily changed, as shown in the example below: import logging
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(levelname)s %(message)s',
filename='/tmp/myapp.log',
filemode='w')
logging.debug('A debug message')
logging.info('Some information')
logging.warning('A shot across the bows')
The basicConfig() method is used to change the configuration defaults, which results in output (written to /tmp/myapp.log) which should look something like the following: 2004-07-02 13:00:08,743 DEBUG A debug message 2004-07-02 13:00:08,743 INFO Some information 2004-07-02 13:00:08,743 WARNING A shot across the bows This time, all messages with a severity of DEBUG or above were handled, and the format of the messages was also changed, and output went to the specified file rather than the console. Formatting uses standard Python string formatting - see section String Formatting Operations. The format string takes the following common specifiers. For a complete list of specifiers, consult the Formatter documentation.
To change the date/time format, you can pass an additional keyword parameter, datefmt, as in the following: import logging
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(levelname)-8s %(message)s',
datefmt='%a, %d %b %Y %H:%M:%S',
filename='/temp/myapp.log',
filemode='w')
logging.debug('A debug message')
logging.info('Some information')
logging.warning('A shot across the bows')
which would result in output like Fri, 02 Jul 2004 13:06:18 DEBUG A debug message Fri, 02 Jul 2004 13:06:18 INFO Some information Fri, 02 Jul 2004 13:06:18 WARNING A shot across the bows The date format string follows the requirements of strftime() - see the documentation for the time module. If, instead of sending logging output to the console or a file, you’d rather use a file-like object which you have created separately, you can pass it to basicConfig() using the stream keyword argument. Note that if both stream and filename keyword arguments are passed, the stream argument is ignored. Of course, you can put variable information in your output. To do this, simply have the message be a format string and pass in additional arguments containing the variable information, as in the following example: import logging
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(levelname)-8s %(message)s',
datefmt='%a, %d %b %Y %H:%M:%S',
filename='/temp/myapp.log',
filemode='w')
logging.error('Pack my box with %d dozen %s', 5, 'liquor jugs')
which would result in Wed, 21 Jul 2004 15:35:16 ERROR Pack my box with 5 dozen liquor jugs Logging to multiple destinations¶Let’s say you want to log to console and file with different message formats and in differing circumstances. Say you want to log messages with levels of DEBUG and higher to file, and those messages at level INFO and higher to the console. Let’s also assume that the file should contain timestamps, but the console messages should not. Here’s how you can achieve this: import logging
# set up logging to file - see previous section for more details
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
datefmt='%m-%d %H:%M',
filename='/temp/myapp.log',
filemode='w')
# define a Handler which writes INFO messages or higher to the sys.stderr
console = logging.StreamHandler()
console.setLevel(logging.INFO)
# set a format which is simpler for console use
formatter = logging.Formatter('%(name)-12s: %(levelname)-8s %(message)s')
# tell the handler to use this format
console.setFormatter(formatter)
# add the handler to the root logger
logging.getLogger('').addHandler(console)
# Now, we can log to the root logger, or any other logger. First the root...
logging.info('Jackdaws love my big sphinx of quartz.')
# Now, define a couple of other loggers which might represent areas in your
# application:
logger1 = logging.getLogger('myapp.area1')
logger2 = logging.getLogger('myapp.area2')
logger1.debug('Quick zephyrs blow, vexing daft Jim.')
logger1.info('How quickly daft jumping zebras vex.')
logger2.warning('Jail zesty vixen who grabbed pay from quack.')
logger2.error('The five boxing wizards jump quickly.')
When you run this, on the console you will see root : INFO Jackdaws love my big sphinx of quartz. myapp.area1 : INFO How quickly daft jumping zebras vex. myapp.area2 : WARNING Jail zesty vixen who grabbed pay from quack. myapp.area2 : ERROR The five boxing wizards jump quickly. and in the file you will see something like 10-22 22:19 root INFO Jackdaws love my big sphinx of quartz. 10-22 22:19 myapp.area1 DEBUG Quick zephyrs blow, vexing daft Jim. 10-22 22:19 myapp.area1 INFO How quickly daft jumping zebras vex. 10-22 22:19 myapp.area2 WARNING Jail zesty vixen who grabbed pay from quack. 10-22 22:19 myapp.area2 ERROR The five boxing wizards jump quickly. As you can see, the DEBUG message only shows up in the file. The other messages are sent to both destinations. This example uses console and file handlers, but you can use any number and combination of handlers you choose. Adding contextual information to your logging output¶Sometimes you want logging output to contain contextual information in addition to the parameters passed to the logging call. For example, in a networked application, it may be desirable to log client-specific information in the log (e.g. remote client’s username, or IP address). Although you could use the extra parameter to achieve this, it’s not always convenient to pass the information in this way. While it might be tempting to create Logger instances on a per-connection basis, this is not a good idea because these instances are not garbage collected. While this is not a problem in practice, when the number of Logger instances is dependent on the level of granularity you want to use in logging an application, it could be hard to manage if the number of Logger instances becomes effectively unbounded. An easy way in which you can pass contextual information to be output along with logging event information is to use the LoggerAdapter class. This class is designed to look like a Logger, so that you can call debug(), info(), warning(), error(), exception(), critical() and log(). These methods have the same signatures as their counterparts in Logger, so you can use the two types of instances interchangeably. When you create an instance of LoggerAdapter, you pass it a Logger instance and a dict-like object which contains your contextual information. When you call one of the logging methods on an instance of LoggerAdapter, it delegates the call to the underlying instance of Logger passed to its constructor, and arranges to pass the contextual information in the delegated call. Here’s a snippet from the code of LoggerAdapter: def debug(self, msg, *args, **kwargs):
"""
Delegate a debug call to the underlying logger, after adding
contextual information from this adapter instance.
"""
msg, kwargs = self.process(msg, kwargs)
self.logger.debug(msg, *args, **kwargs)
The process() method of LoggerAdapter is where the contextual information is added to the logging output. It’s passed the message and keyword arguments of the logging call, and it passes back (potentially) modified versions of these to use in the call to the underlying logger. The default implementation of this method leaves the message alone, but inserts an “extra” key in the keyword argument whose value is the dict-like object passed to the constructor. Of course, if you had passed an “extra” keyword argument in the call to the adapter, it will be silently overwritten. The advantage of using “extra” is that the values in the dict-like object are merged into the LogRecord instance’s __dict__, allowing you to use customized strings with your Formatter instances which know about the keys of the dict-like object. If you need a different method, e.g. if you want to prepend or append the contextual information to the message string, you just need to subclass LoggerAdapter and override process() to do what you need. Here’s an example script which uses this class, which also illustrates what dict-like behaviour is needed from an arbitrary “dict-like” object for use in the constructor: import logging
class ConnInfo:
"""
An example class which shows how an arbitrary class can be used as
the 'extra' context information repository passed to a LoggerAdapter.
"""
def __getitem__(self, name):
"""
To allow this instance to look like a dict.
"""
from random import choice
if name == "ip":
result = choice(["127.0.0.1", "192.168.0.1"])
elif name == "user":
result = choice(["jim", "fred", "sheila"])
else:
result = self.__dict__.get(name, "?")
return result
def __iter__(self):
"""
To allow iteration over keys, which will be merged into
the LogRecord dict before formatting and output.
"""
keys = ["ip", "user"]
keys.extend(self.__dict__.keys())
return keys.__iter__()
if __name__ == "__main__":
from random import choice
levels = (logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR, logging.CRITICAL)
a1 = logging.LoggerAdapter(logging.getLogger("a.b.c"),
{ "ip" : "123.231.231.123", "user" : "sheila" })
logging.basicConfig(level=logging.DEBUG,
format="%(asctime)-15s %(name)-5s %(levelname)-8s IP: %(ip)-15s User: %(user)-8s %(message)s")
a1.debug("A debug message")
a1.info("An info message with %s", "some parameters")
a2 = logging.LoggerAdapter(logging.getLogger("d.e.f"), ConnInfo())
for x in range(10):
lvl = choice(levels)
lvlname = logging.getLevelName(lvl)
a2.log(lvl, "A message at %s level with %d %s", lvlname, 2, "parameters")
When this script is run, the output should look something like this: 2008-01-18 14:49:54,023 a.b.c DEBUG IP: 123.231.231.123 User: sheila A debug message 2008-01-18 14:49:54,023 a.b.c INFO IP: 123.231.231.123 User: sheila An info message with some parameters 2008-01-18 14:49:54,023 d.e.f CRITICAL IP: 192.168.0.1 User: jim A message at CRITICAL level with 2 parameters 2008-01-18 14:49:54,033 d.e.f INFO IP: 192.168.0.1 User: jim A message at INFO level with 2 parameters 2008-01-18 14:49:54,033 d.e.f WARNING IP: 192.168.0.1 User: sheila A message at WARNING level with 2 parameters 2008-01-18 14:49:54,033 d.e.f ERROR IP: 127.0.0.1 User: fred A message at ERROR level with 2 parameters 2008-01-18 14:49:54,033 d.e.f ERROR IP: 127.0.0.1 User: sheila A message at ERROR level with 2 parameters 2008-01-18 14:49:54,033 d.e.f WARNING IP: 192.168.0.1 User: sheila A message at WARNING level with 2 parameters 2008-01-18 14:49:54,033 d.e.f WARNING IP: 192.168.0.1 User: jim A message at WARNING level with 2 parameters 2008-01-18 14:49:54,033 d.e.f INFO IP: 192.168.0.1 User: fred A message at INFO level with 2 parameters 2008-01-18 14:49:54,033 d.e.f WARNING IP: 192.168.0.1 User: sheila A message at WARNING level with 2 parameters 2008-01-18 14:49:54,033 d.e.f WARNING IP: 127.0.0.1 User: jim A message at WARNING level with 2 parameters New in version 2.6. The LoggerAdapter class was not present in previous versions. Sending and receiving logging events across a network¶Let’s say you want to send logging events across a network, and handle them at the receiving end. A simple way of doing this is attaching a SocketHandler instance to the root logger at the sending end: import logging, logging.handlers
rootLogger = logging.getLogger('')
rootLogger.setLevel(logging.DEBUG)
socketHandler = logging.handlers.SocketHandler('localhost',
logging.handlers.DEFAULT_TCP_LOGGING_PORT)
# don't bother with a formatter, since a socket handler sends the event as
# an unformatted pickle
rootLogger.addHandler(socketHandler)
# Now, we can log to the root logger, or any other logger. First the root...
logging.info('Jackdaws love my big sphinx of quartz.')
# Now, define a couple of other loggers which might represent areas in your
# application:
logger1 = logging.getLogger('myapp.area1')
logger2 = logging.getLogger('myapp.area2')
logger1.debug('Quick zephyrs blow, vexing daft Jim.')
logger1.info('How quickly daft jumping zebras vex.')
logger2.warning('Jail zesty vixen who grabbed pay from quack.')
logger2.error('The five boxing wizards jump quickly.')
At the receiving end, you can set up a receiver using the SocketServer module. Here is a basic working example: import cPickle
import logging
import logging.handlers
import SocketServer
import struct
class LogRecordStreamHandler(SocketServer.StreamRequestHandler):
"""Handler for a streaming logging request.
This basically logs the record using whatever logging policy is
configured locally.
"""
def handle(self):
"""
Handle multiple requests - each expected to be a 4-byte length,
followed by the LogRecord in pickle format. Logs the record
according to whatever policy is configured locally.
"""
while 1:
chunk = self.connection.recv(4)
if len(chunk) < 4:
break
slen = struct.unpack(">L", chunk)[0]
chunk = self.connection.recv(slen)
while len(chunk) < slen:
chunk = chunk + self.connection.recv(slen - len(chunk))
obj = self.unPickle(chunk)
record = logging.makeLogRecord(obj)
self.handleLogRecord(record)
def unPickle(self, data):
return cPickle.loads(data)
def handleLogRecord(self, record):
# if a name is specified, we use the named logger rather than the one
# implied by the record.
if self.server.logname is not None:
name = self.server.logname
else:
name = record.name
logger = logging.getLogger(name)
# N.B. EVERY record gets logged. This is because Logger.handle
# is normally called AFTER logger-level filtering. If you want
# to do filtering, do it at the client end to save wasting
# cycles and network bandwidth!
logger.handle(record)
class LogRecordSocketReceiver(SocketServer.ThreadingTCPServer):
"""simple TCP socket-based logging receiver suitable for testing.
"""
allow_reuse_address = 1
def __init__(self, host='localhost',
port=logging.handlers.DEFAULT_TCP_LOGGING_PORT,
handler=LogRecordStreamHandler):
SocketServer.ThreadingTCPServer.__init__(self, (host, port), handler)
self.abort = 0
self.timeout = 1
self.logname = None
def serve_until_stopped(self):
import select
abort = 0
while not abort:
rd, wr, ex = select.select([self.socket.fileno()],
[], [],
self.timeout)
if rd:
self.handle_request()
abort = self.abort
def main():
logging.basicConfig(
format="%(relativeCreated)5d %(name)-15s %(levelname)-8s %(message)s")
tcpserver = LogRecordSocketReceiver()
print "About to start TCP server..."
tcpserver.serve_until_stopped()
if __name__ == "__main__":
main()
First run the server, and then the client. On the client side, nothing is printed on the console; on the server side, you should see something like: About to start TCP server... 59 root INFO Jackdaws love my big sphinx of quartz. 59 myapp.area1 DEBUG Quick zephyrs blow, vexing daft Jim. 69 myapp.area1 INFO How quickly daft jumping zebras vex. 69 myapp.area2 WARNING Jail zesty vixen who grabbed pay from quack. 69 myapp.area2 ERROR The five boxing wizards jump quickly. Handler Objects¶Handlers have the following attributes and methods. Note that Handler is never instantiated directly; this class acts as a base for more useful subclasses. However, the __init__() method in subclasses needs to call Handler.__init__().
StreamHandler¶The StreamHandler class, located in the core logging package, sends logging output to streams such as sys.stdout, sys.stderr or any file-like object (or, more precisely, any object which supports write() and flush() methods).
FileHandler¶The FileHandler class, located in the core logging package, sends logging output to a disk file. It inherits the output functionality from StreamHandler.
WatchedFileHandler¶New in version 2.6. The WatchedFileHandler class, located in the logging.handlers module, is a FileHandler which watches the file it is logging to. If the file changes, it is closed and reopened using the file name. A file change can happen because of usage of programs such as newsyslog and logrotate which perform log file rotation. This handler, intended for use under Unix/Linux, watches the file to see if it has changed since the last emit. (A file is deemed to have changed if its device or inode have changed.) If the file has changed, the old file stream is closed, and the file opened to get a new stream. This handler is not appropriate for use under Windows, because under Windows open log files cannot be moved or renamed - logging opens the files with exclusive locks - and so there is no need for such a handler. Furthermore, ST_INO is not supported under Windows; stat() always returns zero for this value.
RotatingFileHandler¶The RotatingFileHandler class, located in the logging.handlers module, supports rotation of disk log files.
TimedRotatingFileHandler¶The TimedRotatingFileHandler class, located in the logging.handlers module, supports rotation of disk log files at certain timed intervals.
SocketHandler¶The SocketHandler class, located in the logging.handlers module, sends logging output to a network socket. The base class uses a TCP socket.
DatagramHandler¶The DatagramHandler class, located in the logging.handlers module, inherits from SocketHandler to support sending logging messages over UDP sockets.
SysLogHandler¶The SysLogHandler class, located in the logging.handlers module, supports sending logging messages to a remote or local Unix syslog.
NTEventLogHandler¶The NTEventLogHandler class, located in the logging.handlers module, supports sending logging messages to a local Windows NT, Windows 2000 or Windows XP event log. Before you can use it, you need Mark Hammond’s Win32 extensions for Python installed.
SMTPHandler¶The SMTPHandler class, located in the logging.handlers module, supports sending logging messages to an email address via SMTP.
MemoryHandler¶The MemoryHandler class, located in the logging.handlers module, supports buffering of logging records in memory, periodically flushing them to a target handler. Flushing occurs whenever the buffer is full, or when an event of a certain severity or greater is seen. MemoryHandler is a subclass of the more general BufferingHandler, which is an abstract class. This buffers logging records in memory. Whenever each record is added to the buffer, a check is made by calling shouldFlush() to see if the buffer should be flushed. If it should, then flush() is expected to do the needful.
HTTPHandler¶The HTTPHandler class, located in the logging.handlers module, supports sending logging messages to a Web server, using either GET or POST semantics.
Formatter Objects¶Formatters have the following attributes and methods. They are responsible for converting a LogRecord to (usually) a string which can be interpreted by either a human or an external system. The base Formatter allows a formatting string to be specified. If none is supplied, the default value of '%(message)s' is used. A Formatter can be initialized with a format string which makes use of knowledge of the LogRecord attributes - such as the default value mentioned above making use of the fact that the user’s message and arguments are pre-formatted into a LogRecord‘s message attribute. This format string contains standard python %-style mapping keys. See section String Formatting Operations for more information on string formatting. Currently, the useful mapping keys in a LogRecord are:
Changed in version 2.5: funcName was added.
Filter Objects¶Filters can be used by Handlers and Loggers for more sophisticated filtering than is provided by levels. The base filter class only allows events which are below a certain point in the logger hierarchy. For example, a filter initialized with “A.B” will allow events logged by loggers “A.B”, “A.B.C”, “A.B.C.D”, “A.B.D” etc. but not “A.BB”, “B.A.B” etc. If initialized with the empty string, all events are passed.
LogRecord Objects¶LogRecord instances are created every time something is logged. They contain all the information pertinent to the event being logged. The main information passed in is in msg and args, which are combined using msg % args to create the message field of the record. The record also includes information such as when the record was created, the source line where the logging call was made, and any exception information to be logged.
LoggerAdapter Objects¶New in version 2.6. LoggerAdapter instances are used to conveniently pass contextual information into logging calls. For a usage example , see the section on adding contextual information to your logging output.
In addition to the above, LoggerAdapter supports all the logging methods of Logger, i.e. debug(), info(), warning(), error(), exception(), critical() and log(). These methods have the same signatures as their counterparts in Logger, so you can use the two types of instances interchangeably. Thread Safety¶The logging module is intended to be thread-safe without any special work needing to be done by its clients. It achieves this though using threading locks; there is one lock to serialize access to the module’s shared data, and each handler also creates a lock to serialize access to its underlying I/O. Configuration¶Configuration functions¶The following functions configure the logging module. They are located in the logging.config module. Their use is optional — you can configure the logging module using these functions or by making calls to the main API (defined in logging itself) and defining handlers which are declared either in logging or logging.handlers.
Configuration file format¶The configuration file format understood by fileConfig() is based on ConfigParser functionality. The file must contain sections called [loggers], [handlers] and [formatters] which identify by name the entities of each type which are defined in the file. For each such entity, there is a separate section which identified how that entity is configured. Thus, for a logger named log01 in the [loggers] section, the relevant configuration details are held in a section [logger_log01]. Similarly, a handler called hand01 in the [handlers] section will have its configuration held in a section called [handler_hand01], while a formatter called form01 in the [formatters] section will have its configuration specified in a section called [formatter_form01]. The root logger configuration must be specified in a section called [logger_root]. Examples of these sections in the file are given below. [loggers]
keys=root,log02,log03,log04,log05,log06,log07
[handlers]
keys=hand01,hand02,hand03,hand04,hand05,hand06,hand07,hand08,hand09
[formatters]
keys=form01,form02,form03,form04,form05,form06,form07,form08,form09
The root logger must specify a level and a list of handlers. An example of a root logger section is given below. [logger_root]
level=NOTSET
handlers=hand01
The level entry can be one of DEBUG, INFO, WARNING, ERROR, CRITICAL or NOTSET. For the root logger only, NOTSET means that all messages will be logged. Level values are eval()uated in the context of the logging package’s namespace. The handlers entry is a comma-separated list of handler names, which must appear in the [handlers] section. These names must appear in the [handlers] section and have corresponding sections in the configuration file. For loggers other than the root logger, some additional information is required. This is illustrated by the following example. [logger_parser]
level=DEBUG
handlers=hand01
propagate=1
qualname=compiler.parser
The level and handlers entries are interpreted as for the root logger, except that if a non-root logger’s level is specified as NOTSET, the system consults loggers higher up the hierarchy to determine the effective level of the logger. The propagate entry is set to 1 to indicate that messages must propagate to handlers higher up the logger hierarchy from this logger, or 0 to indicate that messages are not propagated to handlers up the hierarchy. The qualname entry is the hierarchical channel name of the logger, that is to say the name used by the application to get the logger. Sections which specify handler configuration are exemplified by the following. [handler_hand01] class=StreamHandler level=NOTSET formatter=form01 args=(sys.stdout,) The class entry indicates the handler’s class (as determined by eval() in the logging package’s namespace). The level is interpreted as for loggers, and NOTSET is taken to mean “log everything”. Changed in version 2.6: Added support for resolving the handler’s class as a dotted module and class name. The formatter entry indicates the key name of the formatter for this handler. If blank, a default formatter (logging._defaultFormatter) is used. If a name is specified, it must appear in the [formatters] section and have a corresponding section in the configuration file. The args entry, when eval()uated in the context of the logging package’s namespace, is the list of arguments to the constructor for the handler class. Refer to the constructors for the relevant handlers, or to the examples below, to see how typical entries are constructed. [handler_hand02]
class=FileHandler
level=DEBUG
formatter=form02
args=('python.log', 'w')
[handler_hand03]
class=handlers.SocketHandler
level=INFO
formatter=form03
args=('localhost', handlers.DEFAULT_TCP_LOGGING_PORT)
[handler_hand04]
class=handlers.DatagramHandler
level=WARN
formatter=form04
args=('localhost', handlers.DEFAULT_UDP_LOGGING_PORT)
[handler_hand05]
class=handlers.SysLogHandler
level=ERROR
formatter=form05
args=(('localhost', handlers.SYSLOG_UDP_PORT), handlers.SysLogHandler.LOG_USER)
[handler_hand06]
class=handlers.NTEventLogHandler
level=CRITICAL
formatter=form06
args=('Python Application', '', 'Application')
[handler_hand07]
class=handlers.SMTPHandler
level=WARN
formatter=form07
args=('localhost', 'from@abc', ['user1@abc', 'user2@xyz'], 'Logger Subject')
[handler_hand08]
class=handlers.MemoryHandler
level=NOTSET
formatter=form08
target=
args=(10, ERROR)
[handler_hand09]
class=handlers.HTTPHandler
level=NOTSET
formatter=form09
args=('localhost:9022', '/log', 'GET')
Sections which specify formatter configuration are typified by the following. [formatter_form01] format=F1 %(asctime)s %(levelname)s %(message)s datefmt= class=logging.Formatter The format entry is the overall format string, and the datefmt entry is the strftime()-compatible date/time format string. If empty, the package substitutes ISO8601 format date/times, which is almost equivalent to specifying the date format string "%Y-%m-%d %H:%M:%S". The ISO8601 format also specifies milliseconds, which are appended to the result of using the above format string, with a comma separator. An example time in ISO8601 format is 2003-01-23 00:29:50,411. The class entry is optional. It indicates the name of the formatter’s class (as a dotted module and class name.) This option is useful for instantiating a Formatter subclass. Subclasses of Formatter can present exception tracebacks in an expanded or condensed format. Configuration server example¶Here is an example of a module using the logging configuration server: import logging
import logging.config
import time
import os
# read initial config file
logging.config.fileConfig("logging.conf")
# create and start listener on port 9999
t = logging.config.listen(9999)
t.start()
logger = logging.getLogger("simpleExample")
try:
# loop through logging calls to see the difference
# new configurations make, until Ctrl+C is pressed
while True:
logger.debug("debug message")
logger.info("info message")
logger.warn("warn message")
logger.error("error message")
logger.critical("critical message")
time.sleep(5)
except KeyboardInterrupt:
# cleanup
logging.config.stopListening()
t.join()
And here is a script that takes a filename and sends that file to the server, properly preceded with the binary-encoded length, as the new logging configuration: #!/usr/bin/env python
import socket, sys, struct
data_to_send = open(sys.argv[1], "r").read()
HOST = 'localhost'
PORT = 9999
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
print "connecting..."
s.connect((HOST, PORT))
print "sending config..."
s.send(struct.pack(">L", len(data_to_send)))
s.send(data_to_send)
s.close()
print "complete"
More examples¶Multiple handlers and formatters¶Loggers are plain Python objects. The addHandler() method has no minimum or maximum quota for the number of handlers you may add. Sometimes it will be beneficial for an application to log all messages of all severities to a text file while simultaneously logging errors or above to the console. To set this up, simply configure the appropriate handlers. The logging calls in the application code will remain unchanged. Here is a slight modification to the previous simple module-based configuration example: import logging
logger = logging.getLogger("simple_example")
logger.setLevel(logging.DEBUG)
# create file handler which logs even debug messages
fh = logging.FileHandler("spam.log")
fh.setLevel(logging.DEBUG)
# create console handler with a higher log level
ch = logging.StreamHandler()
ch.setLevel(logging.ERROR)
# create formatter and add it to the handlers
formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
ch.setFormatter(formatter)
fh.setFormatter(formatter)
# add the handlers to logger
logger.addHandler(ch)
logger.addHandler(fh)
# "application" code
logger.debug("debug message")
logger.info("info message")
logger.warn("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. Using logging in multiple modules¶It was mentioned above that multiple calls to logging.getLogger('someLogger') return a reference to the same logger object. This is true not only within the same module, but also across modules as long as it is in the same Python interpreter process. It is true for references to the same object; additionally, application code can define and configure a parent logger in one module and create (but not configure) a child logger in a separate module, and all logger calls to the child will pass up to the parent. Here is a main module: import logging
import auxiliary_module
# create logger with "spam_application"
logger = logging.getLogger("spam_application")
logger.setLevel(logging.DEBUG)
# create file handler which logs even debug messages
fh = logging.FileHandler("spam.log")
fh.setLevel(logging.DEBUG)
# create console handler with a higher log level
ch = logging.StreamHandler()
ch.setLevel(logging.ERROR)
# create formatter and add it to the handlers
formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
fh.setFormatter(formatter)
ch.setFormatter(formatter)
# add the handlers to the logger
logger.addHandler(fh)
logger.addHandler(ch)
logger.info("creating an instance of auxiliary_module.Auxiliary")
a = auxiliary_module.Auxiliary()
logger.info("created an instance of auxiliary_module.Auxiliary")
logger.info("calling auxiliary_module.Auxiliary.do_something")
a.do_something()
logger.info("finished auxiliary_module.Auxiliary.do_something")
logger.info("calling auxiliary_module.some_function()")
auxiliary_module.some_function()
logger.info("done with auxiliary_module.some_function()")
Here is the auxiliary module: import logging
# create logger
module_logger = logging.getLogger("spam_application.auxiliary")
class Auxiliary:
def __init__(self):
self.logger = logging.getLogger("spam_application.auxiliary.Auxiliary")
self.logger.info("creating an instance of Auxiliary")
def do_something(self):
self.logger.info("doing something")
a = 1 + 1
self.logger.info("done doing something")
def some_function():
module_logger.info("received a call to \"some_function\"")
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() Navigation |
