15.7. logging — Logging facility for Python

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15.7. logging — 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.

15.7.1. 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.

15.7.1.1. 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 (in the example, we assume that you have the appropriate permissions to create a file called example.log in the current directory):

import logging
LOG_FILENAME = 'example.log'
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 = 'logging_rotatingfile_example.out'

# Set up a specific logger with our desired output level
my_logger = logging.getLogger('MyLogger')
my_logger.setLevel(logging.DEBUG)

# Add the log message handler to the logger
handler = logging.handlers.RotatingFileHandler(
              LOG_FILENAME, maxBytes=20, backupCount=5)

my_logger.addHandler(handler)

# Log some messages
for i in range(20):
    my_logger.debug('i = %d' % i)

# See what files are created
logfiles = glob.glob('%s*' % LOG_FILENAME)

for filename in logfiles:
    print filename

The result should be 6 separate files, each with part of the log history for the application:

logging_rotatingfile_example.out
logging_rotatingfile_example.out.1
logging_rotatingfile_example.out.2
logging_rotatingfile_example.out.3
logging_rotatingfile_example.out.4
logging_rotatingfile_example.out.5

The most current file is always logging_rotatingfile_example.out, and each time it reaches the size limit it is renamed with the suffix .1. Each of the existing backup files is renamed to increment the suffix (.1 becomes .2, etc.) and the .6 file is erased.

Obviously this example sets the log length much 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 NOTSET, DEBUG, INFO, WARNING, ERROR and CRITICAL.

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.

15.7.1.2. 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.

  • Logger.setLevel() specifies the lowest-severity log message a logger will handle, where debug is the lowest built-in severity level and critical is the highest built-in severity. For example, if the severity level is info, the logger will handle only info, warning, error, and critical messages and will ignore debug messages.
  • Logger.addFilter() and Logger.removeFilter() add and remove filter objects from the logger object. This tutorial does not address filters.

With the logger object configured, the following methods create log messages:

  • Logger.debug(), Logger.info(), Logger.warning(), Logger.error(), and Logger.critical() all create log records with a message and a level that corresponds to their respective method names. The message is actually a format string, which may contain the standard string substitution syntax of %s, %d, %f, and so on. The rest of their arguments is a list of objects that correspond with the substitution fields in the message. With regard to **kwargs, the logging methods care only about a keyword of exc_info and use it to determine whether to log exception information.
  • Logger.exception() creates a log message similar to Logger.error(). The difference is that Logger.exception() dumps a stack trace along with it. Call this method only from an exception handler.
  • Logger.log() takes a log level as an explicit argument. This is a little more verbose for logging messages than using the log level convenience methods listed above, but this is how to log at custom log levels.

getLogger() returns a reference to a logger instance with the specified if 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 descendants of foo. Child loggers propagate messages up to the handlers associated with their ancestor loggers. Because of this, it is unnecessary to define and configure handlers for all the loggers an application uses. It is sufficient to configure handlers for a top-level logger and create child loggers as needed.

15.7.1.3. 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:

  • The Handler.setLevel() method, just as in logger objects, specifies the lowest severity that will be dispatched to the appropriate destination. Why are there two setLevel() methods? The level set in the logger determines which severity of messages it will pass to its handlers. The level set in each handler determines which messages that handler will send on.
  • setFormatter() selects a Formatter object for this handler to use.
  • addFilter() and removeFilter() respectively configure and deconfigure filter objects on handlers.

Application code should not directly instantiate and use instances of Handler. 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).

15.7.1.4. 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"

15.7.1.5. Configuring Logging

Programmers can configure logging in three ways:

  1. Creating loggers, handlers, and formatters explicitly using Python code that calls the configuration methods listed above.
  2. Creating a logging config file and reading it using the fileConfig() function.
  3. Creating a dictionary of configuration information and passing it to the dictConfig() function.

The following example configures a very simple logger, a console handler, and a simple formatter using Python code:

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.

Note that the class names referenced in config files need to be either relative to the logging module, or absolute values which can be resolved using normal import mechanisms. Thus, you could use either handlers.WatchedFileHandler (relative to the logging module) or mypackage.mymodule.MyHandler (for a class defined in package mypackage and module mymodule, where mypackage is available on the Python import path).

Changed in version 2.7.

In Python 2.7, a new means of configuring logging has been introduced, using dictionaries to hold configuration information. This provides a superset of the functionality of the config-file-based approach outlined above, and is the recommended configuration method for new applications and deployments. Because a Python dictionary is used to hold configuration information, and since you can populate that dictionary using different means, you have more options for configuration. For example, you can use a configuration file in JSON format, or, if you have access to YAML processing functionality, a file in YAML format, to populate the configuration dictionary. Or, of course, you can construct the dictionary in Python code, receive it in pickled form over a socket, or use whatever approach makes sense for your application.

Here’s an example of the same configuration as above, in YAML format for the new dictionary-based approach:

version: 1
formatters:
  simple:
    format: format=%(asctime)s - %(name)s - %(levelname)s - %(message)s
handlers:
  console:
    class: logging.StreamHandler
    level: DEBUG
    formatter: simple
    stream: ext://sys.stdout
loggers:
  simpleExample:
    level: DEBUG
    handlers: [console]
    propagate: no
root:
    level: DEBUG
    handlers: [console]

For more information about logging using a dictionary, see Configuration functions.

15.7.1.6. 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”.

New in version 2.7.

The NullHandler class was not present in previous versions, but is now included, so that it need not be defined in library code.

15.7.2. 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.

Level Numeric value
CRITICAL 50
ERROR 40
WARNING 30
INFO 20
DEBUG 10
NOTSET 0

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 (unless the propagate flag for a logger is set to a false value, at which point the passing to ancestor handlers stops).

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().

15.7.3. Useful Handlers

In addition to the base Handler class, many useful subclasses are provided:

  1. StreamHandler instances send error messages to streams (file-like objects).
  2. FileHandler instances send error messages to disk files.
  3. BaseRotatingHandler is the base class for handlers that rotate log files at a certain point. It is not meant to be instantiated directly. Instead, use RotatingFileHandler or TimedRotatingFileHandler.
  4. RotatingFileHandler instances send error messages to disk files, with support for maximum log file sizes and log file rotation.
  5. TimedRotatingFileHandler instances send error messages to disk files, rotating the log file at certain timed intervals.
  6. SocketHandler instances send error messages to TCP/IP sockets.
  7. DatagramHandler instances send error messages to UDP sockets.
  8. SMTPHandler instances send error messages to a designated email address.
  9. SysLogHandler instances send error messages to a Unix syslog daemon, possibly on a remote machine.
  10. NTEventLogHandler instances send error messages to a Windows NT/2000/XP event log.
  11. MemoryHandler instances send error messages to a buffer in memory, which is flushed whenever specific criteria are met.
  12. HTTPHandler instances send error messages to an HTTP server using either GET or POST semantics.
  13. WatchedFileHandler instances watch the file they are logging to. If the file changes, it is closed and reopened using the file name. This handler is only useful on Unix-like systems; Windows does not support the underlying mechanism used.
  14. NullHandler instances do nothing with error messages. They are used by library developers who want to use logging, but want to avoid the “No handlers could be found for logger XXX” message which can be displayed if the library user has not configured logging. See Configuring Logging for a Library for more information.

New in version 2.7.

The NullHandler class was not present in previous versions.

The NullHandler, 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.

15.7.4. Module-Level Functions

In addition to the classes described above, there are a number of module- level functions.

logging.getLogger([name])

Return a logger with the specified name or, if no name is specified, return a logger which is the root logger of the hierarchy. If specified, the name is typically a dot-separated hierarchical name like “a”, “a.b” or “a.b.c.d”. Choice of these names is entirely up to the developer who is using logging.

All calls to this function with a given name return the same logger instance. This means that logger instances never need to be passed between different parts of an application.

logging.getLoggerClass()

Return either the standard Logger class, or the last class passed to setLoggerClass(). This function may be called from within a new class definition, to ensure that installing a customised Logger class will not undo customisations already applied by other code. For example:

class MyLogger(logging.getLoggerClass()):
    # ... override behaviour here
logging.debug(msg[, *args[, **kwargs]])

Logs a message with level DEBUG on the root logger. The msg is the message format string, and the args are the arguments which are merged into msg using the string formatting operator. (Note that this means that you can use keywords in the format string, together with a single dictionary argument.)

There are two keyword arguments in kwargs which are inspected: exc_info which, if it does not evaluate as false, causes exception information to be added to the logging message. If an exception tuple (in the format returned by sys.exc_info()) is provided, it is used; otherwise, sys.exc_info() is called to get the exception information.

The other optional keyword argument is extra which can be used to pass a dictionary which is used to populate the __dict__ of the LogRecord created for the logging event with user-defined attributes. These custom attributes can then be used as you like. For example, they could be incorporated into logged messages. For example:

FORMAT = "%(asctime)-15s %(clientip)s %(user)-8s %(message)s"
logging.basicConfig(format=FORMAT)
d = {'clientip': '192.168.0.1', 'user': 'fbloggs'}
logging.warning("Protocol problem: %s", "connection reset", extra=d)

would print something like

2006-02-08 22:20:02,165 192.168.0.1 fbloggs  Protocol problem: connection reset

The keys in the dictionary passed in extra should not clash with the keys used by the logging system. (See the Formatter documentation for more information on which keys are used by the logging system.)

If you choose to use these attributes in logged messages, you need to exercise some care. In the above example, for instance, the Formatter has been set up with a format string which expects ‘clientip’ and ‘user’ in the attribute dictionary of the LogRecord. If these are missing, the message will not be logged because a string formatting exception will occur. So in this case, you always need to pass the extra dictionary with these keys.

While this might be annoying, this feature is intended for use in specialized circumstances, such as multi-threaded servers where the same code executes in many contexts, and interesting conditions which arise are dependent on this context (such as remote client IP address and authenticated user name, in the above example). In such circumstances, it is likely that specialized Formatters would be used with particular Handlers.

Changed in version 2.5: extra was added.

logging.info(msg[, *args[, **kwargs]])
Logs a message with level INFO on the root logger. The arguments are interpreted as for debug().
logging.warning(msg[, *args[, **kwargs]])
Logs a message with level WARNING on the root logger. The arguments are interpreted as for debug().
logging.error(msg[, *args[, **kwargs]])
Logs a message with level ERROR on the root logger. The arguments are interpreted as for debug().
logging.critical(msg[, *args[, **kwargs]])
Logs a message with level CRITICAL on the root logger. The arguments are interpreted as for debug().
logging.exception(msg[, *args])
Logs a message with level ERROR on the root logger. The arguments are interpreted as for debug(). Exception info is added to the logging message. This function should only be called from an exception handler.
logging.log(level, msg[, *args[, **kwargs]])
Logs a message with level level on the root logger. The other arguments are interpreted as for debug().
logging.disable(lvl)
Provides an overriding level lvl for all loggers which takes precedence over the logger’s own level. When the need arises to temporarily throttle logging output down across the whole application, this function can be useful. Its effect is to disable all logging calls of severity lvl and below, so that if you call it with a value of INFO, then all INFO and DEBUG events would be discarded, whereas those of severity WARNING and above would be processed according to the logger’s effective level.
logging.addLevelName(lvl, levelName)
Associates level lvl with text levelName in an internal dictionary, which is used to map numeric levels to a textual representation, for example when a Formatter formats a message. This function can also be used to define your own levels. The only constraints are that all levels used must be registered using this function, levels should be positive integers and they should increase in increasing order of severity.
logging.getLevelName(lvl)
Returns the textual representation of logging level lvl. If the level is one of the predefined levels CRITICAL, ERROR, WARNING, INFO or DEBUG then you get the corresponding string. If you have associated levels with names using addLevelName() then the name you have associated with lvl is returned. If a numeric value corresponding to one of the defined levels is passed in, the corresponding string representation is returned. Otherwise, the string “Level %s” % lvl is returned.
logging.makeLogRecord(attrdict)
Creates and returns a new LogRecord instance whose attributes are defined by attrdict. This function is useful for taking a pickled LogRecord attribute dictionary, sent over a socket, and reconstituting it as a LogRecord instance at the receiving end.
logging.basicConfig([**kwargs])

Does basic configuration for the logging system by creating a StreamHandler with a default Formatter and adding it to the root logger. The functions debug(), info(), warning(), error() and critical() will call basicConfig() automatically if no handlers are defined for the root logger.

This function does nothing if the root logger already has handlers configured for it.

Changed in version 2.4: Formerly, basicConfig() did not take any keyword arguments.

The following keyword arguments are supported.

Format Description
filename Specifies that a FileHandler be created, using the specified filename, rather than a StreamHandler.
filemode Specifies the mode to open the file, if filename is specified (if filemode is unspecified, it defaults to ‘a’).
format Use the specified format string for the handler.
datefmt Use the specified date/time format.
level Set the root logger level to the specified level.
stream Use the specified stream to initialize the StreamHandler. Note that this argument is incompatible with ‘filename’ - if both are present, ‘stream’ is ignored.
logging.shutdown()
Informs the logging system to perform an orderly shutdown by flushing and closing all handlers. This should be called at application exit and no further use of the logging system should be made after this call.
logging.setLoggerClass(klass)
Tells the logging system to use the class klass when instantiating a logger. The class should define __init__() such that only a name argument is required, and the __init__() should call Logger.__init__(). This function is typically called before any loggers are instantiated by applications which need to use custom logger behavior.

See also

PEP 282 - A Logging System
The proposal which described this feature for inclusion in the Python standard library.
Original Python logging package
This is the original source for the logging package. The version of the package available from this site is suitable for use with Python 1.5.2, 2.1.x and 2.2.x, which do not include the logging package in the standard library.

15.7.5. 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).

Logger.propagate
If this evaluates to false, logging messages are not passed by this logger or by its child loggers to the handlers of higher level (ancestor) loggers. The constructor sets this attribute to 1.
Logger.setLevel(lvl)

Sets the threshold for this logger to lvl. Logging messages which are less severe than lvl will be ignored. When a logger is created, the level is set to NOTSET (which causes all messages to be processed when the logger is the root logger, or delegation to the parent when the logger is a non-root logger). Note that the root logger is created with level WARNING.

The term “delegation to the parent” means that if a logger has a level of NOTSET, its chain of ancestor loggers is traversed until either an ancestor with a level other than NOTSET is found, or the root is reached.

If an ancestor is found with a level other than NOTSET, then that ancestor’s level is treated as the effective level of the logger where the ancestor search began, and is used to determine how a logging event is handled.

If the root is reached, and it has a level of NOTSET, then all messages will be processed. Otherwise, the root’s level will be used as the effective level.

Logger.isEnabledFor(lvl)
Indicates if a message of severity lvl would be processed by this logger. This method checks first the module-level level set by logging.disable(lvl) and then the logger’s effective level as determined by getEffectiveLevel().
Logger.getEffectiveLevel()
Indicates the effective level for this logger. If a value other than NOTSET has been set using setLevel(), it is returned. Otherwise, the hierarchy is traversed towards the root until a value other than NOTSET is found, and that value is returned.
Logger.getChild(suffix)

Returns a logger which is a descendant to this logger, as determined by the suffix. Thus, logging.getLogger('abc').getChild('def.ghi') would return the same logger as would be returned by logging.getLogger('abc.def.ghi'). This is a convenience method, useful when the parent logger is named using e.g. __name__ rather than a literal string.

New in version 2.7.

Logger.debug(msg[, *args[, **kwargs]])

Logs a message with level DEBUG on this logger. The msg is the message format string, and the args are the arguments which are merged into msg using the string formatting operator. (Note that this means that you can use keywords in the format string, together with a single dictionary argument.)

There are two keyword arguments in kwargs which are inspected: exc_info which, if it does not evaluate as false, causes exception information to be added to the logging message. If an exception tuple (in the format returned by sys.exc_info()) is provided, it is used; otherwise, sys.exc_info() is called to get the exception information.

The other optional keyword argument is extra which can be used to pass a dictionary which is used to populate the __dict__ of the LogRecord created for the logging event with user-defined attributes. These custom attributes can then be used as you like. For example, they could be incorporated into logged messages. For example:

FORMAT = "%(asctime)-15s %(clientip)s %(user)-8s %(message)s"
logging.basicConfig(format=FORMAT)
d = { 'clientip' : '192.168.0.1', 'user' : 'fbloggs' }
logger = logging.getLogger("tcpserver")
logger.warning("Protocol problem: %s", "connection reset", extra=d)

would print something like

2006-02-08 22:20:02,165 192.168.0.1 fbloggs  Protocol problem: connection reset

The keys in the dictionary passed in extra should not clash with the keys used by the logging system. (See the Formatter documentation for more information on which keys are used by the logging system.)

If you choose to use these attributes in logged messages, you need to exercise some care. In the above example, for instance, the Formatter has been set up with a format string which expects ‘clientip’ and ‘user’ in the attribute dictionary of the LogRecord. If these are missing, the message will not be logged because a string formatting exception will occur. So in this case, you always need to pass the extra dictionary with these keys.

While this might be annoying, this feature is intended for use in specialized circumstances, such as multi-threaded servers where the same code executes in many contexts, and interesting conditions which arise are dependent on this context (such as remote client IP address and authenticated user name, in the above example). In such circumstances, it is likely that specialized Formatters would be used with particular Handlers.

Changed in version 2.5: extra was added.

Logger.info(msg[, *args[, **kwargs]])
Logs a message with level INFO on this logger. The arguments are interpreted as for debug().
Logger.warning(msg[, *args[, **kwargs]])
Logs a message with level WARNING on this logger. The arguments are interpreted as for debug().
Logger.error(msg[, *args[, **kwargs]])
Logs a message with level ERROR on this logger. The arguments are interpreted as for debug().
Logger.critical(msg[, *args[, **kwargs]])
Logs a message with level CRITICAL on this logger. The arguments are interpreted as for debug().
Logger.log(lvl, msg[, *args[, **kwargs]])
Logs a message with integer level lvl on this logger. The other arguments are interpreted as for debug().
Logger.exception(msg[, *args])
Logs a message with level ERROR on this logger. The arguments are interpreted as for debug(). Exception info is added to the logging message. This method should only be called from an exception handler.
Logger.addFilter(filt)
Adds the specified filter filt to this logger.
Logger.removeFilter(filt)
Removes the specified filter filt from this logger.
Logger.filter(record)
Applies this logger’s filters to the record and returns a true value if the record is to be processed.
Logger.addHandler(hdlr)
Adds the specified handler hdlr to this logger.
Logger.removeHandler(hdlr)
Removes the specified handler hdlr from this logger.
Logger.findCaller()

Finds the caller’s source filename and line number. Returns the filename, line number and function name as a 3-element tuple.

Changed in version 2.4: The function name was added. In earlier versions, the filename and line number were returned as a 2-element tuple..

Logger.handle(record)
Handles a record by passing it to all handlers associated with this logger and its ancestors (until a false value of propagate is found). This method is used for unpickled records received from a socket, as well as those created locally. Logger-level filtering is applied using filter().
Logger.makeRecord(name, lvl, fn, lno, msg, args, exc_info[, func, extra])

This is a factory method which can be overridden in subclasses to create specialized LogRecord instances.

Changed in version 2.5: func and extra were added.

15.7.6. 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='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 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.

Format Description
%(name)s Name of the logger (logging channel).
%(levelname)s Text logging level for the message ('DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL').
%(asctime)s Human-readable time when the LogRecord was created. By default this is of the form “2003-07-08 16:49:45,896” (the numbers after the comma are millisecond portion of the time).
%(message)s The logged message.

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

15.7.7. 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.

15.7.8. Exceptions raised during logging

The logging package is designed to swallow exceptions which occur while logging in production. This is so that errors which occur while handling logging events - such as logging misconfiguration, network or other similar errors - do not cause the application using logging to terminate prematurely.

SystemExit and KeyboardInterrupt exceptions are never swallowed. Other exceptions which occur during the emit() method of a Handler subclass are passed to its handleError() method.

The default implementation of handleError() in Handler checks to see if a module-level variable, raiseExceptions, is set. If set, a traceback is printed to sys.stderr. If not set, the exception is swallowed.

Note: The default value of raiseExceptions is True. This is because during development, you typically want to be notified of any exceptions that occur. It’s advised that you set raiseExceptions to False for production usage.

15.7.9. 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.

15.7.10. Logging to a single file from multiple processes

Although logging is thread-safe, and logging to a single file from multiple threads in a single process is supported, logging to a single file from multiple processes is not supported, because there is no standard way to serialize access to a single file across multiple processes in Python. If you need to log to a single file from multiple processes, the best way of doing this is to have all the processes log to a SocketHandler, and have a separate process which implements a socket server which reads from the socket and logs to file. (If you prefer, you can dedicate one thread in one of the existing processes to perform this function.) The following section documents this approach in more detail and includes a working socket receiver which can be used as a starting point for you to adapt in your own applications.

If you are using a recent version of Python which includes the multiprocessing module, you can write your own handler which uses the Lock class from this module to serialize access to the file from your processes. The existing FileHandler and subclasses do not make use of multiprocessing at present, though they may do so in the future. Note that at present, the multiprocessing module does not provide working lock functionality on all platforms (see http://bugs.python.org/issue3770).

15.7.11. 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.

15.7.12. Using arbitrary objects as messages

In the preceding sections and examples, it has been assumed that the message passed when logging the event is a string. However, this is not the only possibility. You can pass an arbitrary object as a message, and its __str__() method will be called when the logging system needs to convert it to a string representation. In fact, if you want to, you can avoid computing a string representation altogether - for example, the SocketHandler emits an event by pickling it and sending it over the wire.

15.7.13. Optimization

Formatting of message arguments is deferred until it cannot be avoided. However, computing the arguments passed to the logging method can also be expensive, and you may want to avoid doing it if the logger will just throw away your event. To decide what to do, you can call the isEnabledFor() method which takes a level argument and returns true if the event would be created by the Logger for that level of call. You can write code like this:

if logger.isEnabledFor(logging.DEBUG):
    logger.debug("Message with %s, %s", expensive_func1(),
                                        expensive_func2())

so that if the logger’s threshold is set above DEBUG, the calls to expensive_func1() and expensive_func2() are never made.

There are other optimizations which can be made for specific applications which need more precise control over what logging information is collected. Here’s a list of things you can do to avoid processing during logging which you don’t need:

What you don’t want to collect How to avoid collecting it
Information about where calls were made from. Set logging._srcfile to None.
Threading information. Set logging.logThreads to 0.
Process information. Set logging.logProcesses to 0.

Also note that the core logging module only includes the basic handlers. If you don’t import logging.handlers and logging.config, they won’t take up any memory.

15.7.14. 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__().

Handler.__init__(level=NOTSET)
Initializes the Handler instance by setting its level, setting the list of filters to the empty list and creating a lock (using createLock()) for serializing access to an I/O mechanism.
Handler.createLock()
Initializes a thread lock which can be used to serialize access to underlying I/O functionality which may not be threadsafe.
Handler.acquire()
Acquires the thread lock created with createLock().
Handler.release()
Releases the thread lock acquired with acquire().
Handler.setLevel(lvl)
Sets the threshold for this handler to lvl. Logging messages which are less severe than lvl will be ignored. When a handler is created, the level is set to NOTSET (which causes all messages to be processed).
Handler.setFormatter(form)
Sets the Formatter for this handler to form.
Handler.addFilter(filt)
Adds the specified filter filt to this handler.
Handler.removeFilter(filt)
Removes the specified filter filt from this handler.
Handler.filter(record)
Applies this handler’s filters to the record and returns a true value if the record is to be processed.
Handler.flush()
Ensure all logging output has been flushed. This version does nothing and is intended to be implemented by subclasses.
Handler.close()
Tidy up any resources used by the handler. This version does no output but removes the handler from an internal list of handlers which is closed when shutdown() is called. Subclasses should ensure that this gets called from overridden close() methods.
Handler.handle(record)
Conditionally emits the specified logging record, depending on filters which may have been added to the handler. Wraps the actual emission of the record with acquisition/release of the I/O thread lock.
Handler.handleError(record)
This method should be called from handlers when an exception is encountered during an emit() call. By default it does nothing, which means that exceptions get silently ignored. This is what is mostly wanted for a logging system - most users will not care about errors in the logging system, they are more interested in application errors. You could, however, replace this with a custom handler if you wish. The specified record is the one which was being processed when the exception occurred.
Handler.format(record)
Do formatting for a record - if a formatter is set, use it. Otherwise, use the default formatter for the module.
Handler.emit(record)
Do whatever it takes to actually log the specified logging record. This version is intended to be implemented by subclasses and so raises a NotImplementedError.

15.7.14.1. 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).

class logging.StreamHandler([stream])

Returns a new instance of the StreamHandler class. If stream is specified, the instance will use it for logging output; otherwise, sys.stderr will be used.

emit(record)
If a formatter is specified, it is used to format the record. The record is then written to the stream with a trailing newline. If exception information is present, it is formatted using traceback.print_exception() and appended to the stream.
flush()
Flushes the stream by calling its flush() method. Note that the close() method is inherited from Handler and so does no output, so an explicit flush() call may be needed at times.

15.7.14.2. FileHandler

The FileHandler class, located in the core logging package, sends logging output to a disk file. It inherits the output functionality from StreamHandler.

class logging.FileHandler(filename[, mode[, encoding[, delay]]])

Returns a new instance of the FileHandler class. The specified file is opened and used as the stream for logging. If mode is not specified, 'a' is used. If encoding is not None, it is used to open the file with that encoding. If delay is true, then file opening is deferred until the first call to emit(). By default, the file grows indefinitely.

Changed in version 2.6: delay was added.

close()
Closes the file.
emit(record)
Outputs the record to the file.

15.7.14.3. NullHandler

New in version 2.7.

The NullHandler class, located in the core logging package, does not do any formatting or output. It is essentially a “no-op” handler for use by library developers.

class logging.NullHandler

Returns a new instance of the NullHandler class.

emit(record)
This method does nothing.

See Configuring Logging for a Library for more information on how to use NullHandler.

15.7.14.4. 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.

class logging.handlers.WatchedFileHandler(filename[, mode[, encoding[, delay]]])

Returns a new instance of the WatchedFileHandler class. The specified file is opened and used as the stream for logging. If mode is not specified, 'a' is used. If encoding is not None, it is used to open the file with that encoding. If delay is true, then file opening is deferred until the first call to emit(). By default, the file grows indefinitely.

Changed in version 2.6: delay was added.

emit(record)
Outputs the record to the file, but first checks to see if the file has changed. If it has, the existing stream is flushed and closed and the file opened again, before outputting the record to the file.

15.7.14.5. RotatingFileHandler

The RotatingFileHandler class, located in the logging.handlers module, supports rotation of disk log files.

class logging.handlers.RotatingFileHandler(filename[, mode[, maxBytes[, backupCount[, encoding[, delay]]]]])

Returns a new instance of the RotatingFileHandler class. The specified file is opened and used as the stream for logging. If mode is not specified, 'a' is used. If encoding is not None, it is used to open the file with that encoding. If delay is true, then file opening is deferred until the first call to emit(). By default, the file grows indefinitely.

You can use the maxBytes and backupCount values to allow the file to rollover at a predetermined size. When the size is about to be exceeded, the file is closed and a new file is silently opened for output. Rollover occurs whenever the current log file is nearly maxBytes in length; if maxBytes is zero, rollover never occurs. If backupCount is non-zero, the system will save old log files by appending the extensions “.1”, “.2” etc., to the filename. For example, with a backupCount of 5 and a base file name of app.log, you would get app.log, app.log.1, app.log.2, up to app.log.5. The file being written to is always app.log. When this file is filled, it is closed and renamed to app.log.1, and if files app.log.1, app.log.2, etc. exist, then they are renamed to app.log.2, app.log.3 etc. respectively.

Changed in version 2.6: delay was added.

doRollover()
Does a rollover, as described above.
emit(record)
Outputs the record to the file, catering for rollover as described previously.

15.7.14.6. TimedRotatingFileHandler

The TimedRotatingFileHandler class, located in the logging.handlers module, supports rotation of disk log files at certain timed intervals.

class logging.handlers.TimedRotatingFileHandler(filename[, when[, interval[, backupCount[, encoding[, delay[, utc]]]]]])

Returns a new instance of the TimedRotatingFileHandler class. The specified file is opened and used as the stream for logging. On rotating it also sets the filename suffix. Rotating happens based on the product of when and interval.

You can use the when to specify the type of interval. The list of possible values is below. Note that they are not case sensitive.

Value Type of interval
'S' Seconds
'M' Minutes
'H' Hours
'D' Days
'W' Week day (0=Monday)
'midnight' Roll over at midnight

The system will save old log files by appending extensions to the filename. The extensions are date-and-time based, using the strftime format %Y-%m-%d_%H-%M-%S or a leading portion thereof, depending on the rollover interval.

When computing the next rollover time for the first time (when the handler is created), the last modification time of an existing log file, or else the current time, is used to compute when the next rotation will occur.

If the utc argument is true, times in UTC will be used; otherwise local time is used.

If backupCount is nonzero, at most backupCount files will be kept, and if more would be created when rollover occurs, the oldest one is deleted. The deletion logic uses the interval to determine which files to delete, so changing the interval may leave old files lying around.

If delay is true, then file opening is deferred until the first call to emit().

Changed in version 2.6: delay was added.

doRollover()
Does a rollover, as described above.
emit(record)
Outputs the record to the file, catering for rollover as described above.

15.7.14.7. SocketHandler

The SocketHandler class, located in the logging.handlers module, sends logging output to a network socket. The base class uses a TCP socket.

class logging.handlers.SocketHandler(host, port)

Returns a new instance of the SocketHandler class intended to communicate with a remote machine whose address is given by host and port.

close()
Closes the socket.
emit()
Pickles the record’s attribute dictionary and writes it to the socket in binary format. If there is an error with the socket, silently drops the packet. If the connection was previously lost, re-establishes the connection. To unpickle the record at the receiving end into a LogRecord, use the makeLogRecord() function.
handleError()
Handles an error which has occurred during emit(). The most likely cause is a lost connection. Closes the socket so that we can retry on the next event.
makeSocket()
This is a factory method which allows subclasses to define the precise type of socket they want. The default implementation creates a TCP socket (socket.SOCK_STREAM).
makePickle(record)

Pickles the record’s attribute dictionary in binary format with a length prefix, and returns it ready for transmission across the socket.

Note that pickles aren’t completely secure. If you are concerned about security, you may want to override this method to implement a more secure mechanism. For example, you can sign pickles using HMAC and then verify them on the receiving end, or alternatively you can disable unpickling of global objects on the receiving end.

send(packet)
Send a pickled string packet to the socket. This function allows for partial sends which can happen when the network is busy.

15.7.14.8. DatagramHandler

The DatagramHandler class, located in the logging.handlers module, inherits from SocketHandler to support sending logging messages over UDP sockets.

class logging.handlers.DatagramHandler(host, port)

Returns a new instance of the DatagramHandler class intended to communicate with a remote machine whose address is given by host and port.

emit()
Pickles the record’s attribute dictionary and writes it to the socket in binary format. If there is an error with the socket, silently drops the packet. To unpickle the record at the receiving end into a LogRecord, use the makeLogRecord() function.
makeSocket()
The factory method of SocketHandler is here overridden to create a UDP socket (socket.SOCK_DGRAM).
send(s)
Send a pickled string to a socket.

15.7.14.9. SysLogHandler

The SysLogHandler class, located in the logging.handlers module, supports sending logging messages to a remote or local Unix syslog.

class logging.handlers.SysLogHandler([address[, facility[, socktype]]])

Returns a new instance of the SysLogHandler class intended to communicate with a remote Unix machine whose address is given by address in the form of a (host, port) tuple. If address is not specified, ('localhost', 514) is used. The address is used to open a socket. An alternative to providing a (host, port) tuple is providing an address as a string, for example “/dev/log”. In this case, a Unix domain socket is used to send the message to the syslog. If facility is not specified, LOG_USER is used. The type of socket opened depends on the socktype argument, which defaults to socket.SOCK_DGRAM and thus opens a UDP socket. To open a TCP socket (for use with the newer syslog daemons such as rsyslog), specify a value of socket.SOCK_STREAM.

Changed in version 2.7: socktype was added.

close()
Closes the socket to the remote host.
emit(record)
The record is formatted, and then sent to the syslog server. If exception information is present, it is not sent to the server.
encodePriority(facility, priority)

Encodes the facility and priority into an integer. You can pass in strings or integers - if strings are passed, internal mapping dictionaries are used to convert them to integers.

The symbolic LOG_ values are defined in SysLogHandler and mirror the values defined in the sys/syslog.h header file.

Priorities

Name (string) Symbolic value
alert LOG_ALERT
crit or critical LOG_CRIT
debug LOG_DEBUG
emerg or panic LOG_EMERG
err or error LOG_ERR
info LOG_INFO
notice LOG_NOTICE
warn or warning LOG_WARNING

Facilities

Name (string) Symbolic value
auth LOG_AUTH
authpriv LOG_AUTHPRIV
cron LOG_CRON
daemon LOG_DAEMON
ftp LOG_FTP
kern LOG_KERN
lpr LOG_LPR
mail LOG_MAIL
news LOG_NEWS
syslog LOG_SYSLOG
user LOG_USER
uucp LOG_UUCP
local0 LOG_LOCAL0
local1 LOG_LOCAL1
local2 LOG_LOCAL2
local3 LOG_LOCAL3
local4 LOG_LOCAL4
local5 LOG_LOCAL5
local6 LOG_LOCAL6
local7 LOG_LOCAL7
mapPriority(levelname)
Maps a logging level name to a syslog priority name. You may need to override this if you are using custom levels, or if the default algorithm is not suitable for your needs. The default algorithm maps DEBUG, INFO, WARNING, ERROR and CRITICAL to the equivalent syslog names, and all other level names to “warning”.

15.7.14.10. 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.

class logging.handlers.NTEventLogHandler(appname[, dllname[, logtype]])

Returns a new instance of the NTEventLogHandler class. The appname is used to define the application name as it appears in the event log. An appropriate registry entry is created using this name. The dllname should give the fully qualified pathname of a .dll or .exe which contains message definitions to hold in the log (if not specified, 'win32service.pyd' is used - this is installed with the Win32 extensions and contains some basic placeholder message definitions. Note that use of these placeholders will make your event logs big, as the entire message source is held in the log. If you want slimmer logs, you have to pass in the name of your own .dll or .exe which contains the message definitions you want to use in the event log). The logtype is one of 'Application', 'System' or 'Security', and defaults to 'Application'.

close()
At this point, you can remove the application name from the registry as a source of event log entries. However, if you do this, you will not be able to see the events as you intended in the Event Log Viewer - it needs to be able to access the registry to get the .dll name. The current version does not do this.
emit(record)
Determines the message ID, event category and event type, and then logs the message in the NT event log.
getEventCategory(record)
Returns the event category for the record. Override this if you want to specify your own categories. This version returns 0.
getEventType(record)
Returns the event type for the record. Override this if you want to specify your own types. This version does a mapping using the handler’s typemap attribute, which is set up in __init__() to a dictionary which contains mappings for DEBUG, INFO, WARNING, ERROR and CRITICAL. If you are using your own levels, you will either need to override this method or place a suitable dictionary in the handler’s typemap attribute.
getMessageID(record)
Returns the message ID for the record. If you are using your own messages, you could do this by having the msg passed to the logger being an ID rather than a format string. Then, in here, you could use a dictionary lookup to get the message ID. This version returns 1, which is the base message ID in win32service.pyd.

15.7.14.11. SMTPHandler

The SMTPHandler class, located in the logging.handlers module, supports sending logging messages to an email address via SMTP.

class logging.handlers.SMTPHandler(mailhost, fromaddr, toaddrs, subject[, credentials])

Returns a new instance of the SMTPHandler class. The instance is initialized with the from and to addresses and subject line of the email. The toaddrs should be a list of strings. To specify a non-standard SMTP port, use the (host, port) tuple format for the mailhost argument. If you use a string, the standard SMTP port is used. If your SMTP server requires authentication, you can specify a (username, password) tuple for the credentials argument.

Changed in version 2.6: credentials was added.

emit(record)
Formats the record and sends it to the specified addressees.
getSubject(record)
If you want to specify a subject line which is record-dependent, override this method.

15.7.14.12. 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.

class logging.handlers.BufferingHandler(capacity)

Initializes the handler with a buffer of the specified capacity.

emit(record)
Appends the record to the buffer. If shouldFlush() returns true, calls flush() to process the buffer.
flush()
You can override this to implement custom flushing behavior. This version just zaps the buffer to empty.
shouldFlush(record)
Returns true if the buffer is up to capacity. This method can be overridden to implement custom flushing strategies.
class logging.handlers.MemoryHandler(capacity[, flushLevel[, target]])

Returns a new instance of the MemoryHandler class. The instance is initialized with a buffer size of capacity. If flushLevel is not specified, ERROR is used. If no target is specified, the target will need to be set using setTarget() before this handler does anything useful.

close()
Calls flush(), sets the target to None and clears the buffer.
flush()
For a MemoryHandler, flushing means just sending the buffered records to the target, if there is one. Override if you want different behavior.
setTarget(target)
Sets the target handler for this handler.
shouldFlush(record)
Checks for buffer full or a record at the flushLevel or higher.

15.7.14.13. HTTPHandler

The HTTPHandler class, located in the logging.handlers module, supports sending logging messages to a Web server, using either GET or POST semantics.

class logging.handlers.HTTPHandler(host, url[, method])

Returns a new instance of the HTTPHandler class. The instance is initialized with a host address, url and HTTP method. The host can be of the form host:port, should you need to use a specific port number. If no method is specified, GET is used.

emit(record)
Sends the record to the Web server as an URL-encoded dictionary.

15.7.15. 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:

Format Description
%(name)s Name of the logger (logging channel).
%(levelno)s Numeric logging level for the message (DEBUG, INFO, WARNING, ERROR, CRITICAL).
%(levelname)s Text logging level for the message ('DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL').
%(pathname)s Full pathname of the source file where the logging call was issued (if available).
%(filename)s Filename portion of pathname.
%(module)s Module (name portion of filename).
%(funcName)s Name of function containing the logging call.
%(lineno)d Source line number where the logging call was issued (if available).
%(created)f Time when the LogRecord was created (as returned by time.time()).
%(relativeCreated)d Time in milliseconds when the LogRecord was created, relative to the time the logging module was loaded.
%(asctime)s Human-readable time when the LogRecord was created. By default this is of the form “2003-07-08 16:49:45,896” (the numbers after the comma are millisecond portion of the time).
%(msecs)d Millisecond portion of the time when the LogRecord was created.
%(thread)d Thread ID (if available).
%(threadName)s Thread name (if available).
%(process)d Process ID (if available).
%(message)s The logged message, computed as msg % args.

Changed in version 2.5: funcName was added.

class logging.Formatter([fmt[, datefmt]])

Returns a new instance of the Formatter class. The instance is initialized with a format string for the message as a whole, as well as a format string for the date/time portion of a message. If no fmt is specified, '%(message)s' is used. If no datefmt is specified, the ISO8601 date format is used.

format(record)
The record’s attribute dictionary is used as the operand to a string formatting operation. Returns the resulting string. Before formatting the dictionary, a couple of preparatory steps are carried out. The message attribute of the record is computed using msg % args. If the formatting string contains '(asctime)', formatTime() is called to format the event time. If there is exception information, it is formatted using formatException() and appended to the message. Note that the formatted exception information is cached in attribute exc_text. This is useful because the exception information can be pickled and sent across the wire, but you should be careful if you have more than one Formatter subclass which customizes the formatting of exception information. In this case, you will have to clear the cached value after a formatter has done its formatting, so that the next formatter to handle the event doesn’t use the cached value but recalculates it afresh.
formatTime(record[, datefmt])
This method should be called from format() by a formatter which wants to make use of a formatted time. This method can be overridden in formatters to provide for any specific requirement, but the basic behavior is as follows: if datefmt (a string) is specified, it is used with time.strftime() to format the creation time of the record. Otherwise, the ISO8601 format is used. The resulting string is returned.
formatException(exc_info)
Formats the specified exception information (a standard exception tuple as returned by sys.exc_info()) as a string. This default implementation just uses traceback.print_exception(). The resulting string is returned.

15.7.16. 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.

class logging.Filter([name])

Returns an instance of the Filter class. If name is specified, it names a logger which, together with its children, will have its events allowed through the filter. If no name is specified, allows every event.

filter(record)
Is the specified record to be logged? Returns zero for no, nonzero for yes. If deemed appropriate, the record may be modified in-place by this method.

15.7.17. 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.

class logging.LogRecord(name, lvl, pathname, lineno, msg, args, exc_info[, func])

Returns an instance of LogRecord initialized with interesting information. The name is the logger name; lvl is the numeric level; pathname is the absolute pathname of the source file in which the logging call was made; lineno is the line number in that file where the logging call is found; msg is the user-supplied message (a format string); args is the tuple which, together with msg, makes up the user message; and exc_info is the exception tuple obtained by calling sys.exc_info() (or None, if no exception information is available). The func is the name of the function from which the logging call was made. If not specified, it defaults to None.

Changed in version 2.5: func was added.

getMessage()
Returns the message for this LogRecord instance after merging any user-supplied arguments with the message.

15.7.18. 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.

class logging.LoggerAdapter(logger, extra)

Returns an instance of LoggerAdapter initialized with an underlying Logger instance and a dict-like object.

process(msg, kwargs)
Modifies the message and/or keyword arguments passed to a logging call in order to insert contextual information. This implementation takes the object passed as extra to the constructor and adds it to kwargs using key ‘extra’. The return value is a (msg, kwargs) tuple which has the (possibly modified) versions of the arguments passed in.

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.

Changed in version 2.7.

The isEnabledFor() method was added to LoggerAdapter. This method delegates to the underlying logger.

15.7.19. 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.

If you are implementing asynchronous signal handlers using the signal module, you may not be able to use logging from within such handlers. This is because lock implementations in the threading module are not always re-entrant, and so cannot be invoked from such signal handlers.

15.7.20. Integration with the warnings module

The captureWarnings() function can be used to integrate logging with the warnings module.

logging.captureWarnings(capture)

This function is used to turn the capture of warnings by logging on and off.

If capture is True, warnings issued by the warnings module will be redirected to the logging system. Specifically, a warning will be formatted using warnings.formatwarning() and the resulting string logged to a logger named “py.warnings” with a severity of WARNING.

If capture is False, the redirection of warnings to the logging system will stop, and warnings will be redirected to their original destinations (i.e. those in effect before captureWarnings(True) was called).

15.7.21. Configuration

15.7.21.1. 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.

logging.dictConfig(config)

Takes the logging configuration from a dictionary. The contents of this dictionary are described in Configuration dictionary schema below.

If an error is encountered during configuration, this function will raise a ValueError, TypeError, AttributeError or ImportError with a suitably descriptive message. The following is a (possibly incomplete) list of conditions which will raise an error:

  • A level which is not a string or which is a string not corresponding to an actual logging level.
  • A propagate value which is not a boolean.
  • An id which does not have a corresponding destination.
  • A non-existent handler id found during an incremental call.
  • An invalid logger name.
  • Inability to resolve to an internal or external object.

Parsing is performed by the DictConfigurator class, whose constructor is passed the dictionary used for configuration, and has a configure() method. The logging.config module has a callable attribute dictConfigClass which is initially set to DictConfigurator. You can replace the value of dictConfigClass with a suitable implementation of your own.

dictConfig() calls dictConfigClass passing the specified dictionary, and then calls the configure() method on the returned object to put the configuration into effect:

def dictConfig(config):
    dictConfigClass(config).configure()

For example, a subclass of DictConfigurator could call DictConfigurator.__init__() in its own __init__(), then set up custom prefixes which would be usable in the subsequent configure() call. dictConfigClass would be bound to this new subclass, and then dictConfig() could be called exactly as in the default, uncustomized state.

logging.fileConfig(fname[, defaults])
Reads the logging configuration from a ConfigParser-format file named fname. This function can be called several times from an application, allowing an end user to select from various pre-canned configurations (if the developer provides a mechanism to present the choices and load the chosen configuration). Defaults to be passed to the ConfigParser can be specified in the defaults argument.
logging.listen([port])

Starts up a socket server on the specified port, and listens for new configurations. If no port is specified, the module’s default DEFAULT_LOGGING_CONFIG_PORT is used. Logging configurations will be sent as a file suitable for processing by fileConfig(). Returns a Thread instance on which you can call start() to start the server, and which you can join() when appropriate. To stop the server, call stopListening().

To send a configuration to the socket, read in the configuration file and send it to the socket as a string of bytes preceded by a four-byte length string packed in binary using struct.pack('>L', n).

logging.stopListening()
Stops the listening server which was created with a call to listen(). This is typically called before calling join() on the return value from listen().

15.7.21.2. Configuration dictionary schema

Describing a logging configuration requires listing the various objects to create and the connections between them; for example, you may create a handler named “console” and then say that the logger named “startup” will send its messages to the “console” handler. These objects aren’t limited to those provided by the logging module because you might write your own formatter or handler class. The parameters to these classes may also need to include external objects such as sys.stderr. The syntax for describing these objects and connections is defined in Object connections below.

15.7.21.2.1. Dictionary Schema Details

The dictionary passed to dictConfig() must contain the following keys:

  • version - to be set to an integer value representing the schema version. The only valid value at present is 1, but having this key allows the schema to evolve while still preserving backwards compatibility.

All other keys are optional, but if present they will be interpreted as described below. In all cases below where a ‘configuring dict’ is mentioned, it will be checked for the special '()' key to see if a custom instantiation is required. If so, the mechanism described in User-defined objects below is used to create an instance; otherwise, the context is used to determine what to instantiate.

  • formatters - the corresponding value will be a dict in which each key is a formatter id and each value is a dict describing how to configure the corresponding Formatter instance.

    The configuring dict is searched for keys format and datefmt (with defaults of None) and these are used to construct a logging.Formatter instance.

  • filters - the corresponding value will be a dict in which each key is a filter id and each value is a dict describing how to configure the corresponding Filter instance.

    The configuring dict is searched for the key name (defaulting to the empty string) and this is used to construct a logging.Filter instance.

  • handlers - the corresponding value will be a dict in which each key is a handler id and each value is a dict describing how to configure the corresponding Handler instance.

    The configuring dict is searched for the following keys:

    • class (mandatory). This is the fully qualified name of the handler class.
    • level (optional). The level of the handler.
    • formatter (optional). The id of the formatter for this handler.
    • filters (optional). A list of ids of the filters for this handler.

    All other keys are passed through as keyword arguments to the handler’s constructor. For example, given the snippet:

    handlers:
      console:
        class : logging.StreamHandler
        formatter: brief
        level   : INFO
        filters: [allow_foo]
        stream  : ext://sys.stdout
      file:
        class : logging.handlers.RotatingFileHandler
        formatter: precise
        filename: logconfig.log
        maxBytes: 1024
        backupCount: 3

    the handler with id console is instantiated as a logging.StreamHandler, using sys.stdout as the underlying stream. The handler with id file is instantiated as a logging.handlers.RotatingFileHandler with the keyword arguments filename='logconfig.log', maxBytes=1024, backupCount=3.

  • loggers - the corresponding value will be a dict in which each key is a logger name and each value is a dict describing how to configure the corresponding Logger instance.

    The configuring dict is searched for the following keys:

    • level (optional). The level of the logger.
    • propagate (optional). The propagation setting of the logger.
    • filters (optional). A list of ids of the filters for this logger.
    • handlers (optional). A list of ids of the handlers for this logger.

    The specified loggers will be configured according to the level, propagation, filters and handlers specified.

  • root - this will be the configuration for the root logger. Processing of the configuration will be as for any logger, except that the propagate setting will not be applicable.

  • incremental - whether the configuration is to be interpreted as incremental to the existing configuration. This value defaults to False, which means that the specified configuration replaces the existing configuration with the same semantics as used by the existing fileConfig() API.

    If the specified value is True, the configuration is processed as described in the section on Incremental Configuration.

  • disable_existing_loggers - whether any existing loggers are to be disabled. This setting mirrors the parameter of the same name in fileConfig(). If absent, this parameter defaults to True. This value is ignored if incremental is True.

15.7.21.2.2. Incremental Configuration

It is difficult to provide complete flexibility for incremental configuration. For example, because objects such as filters and formatters are anonymous, once a configuration is set up, it is not possible to refer to such anonymous objects when augmenting a configuration.

Furthermore, there is not a compelling case for arbitrarily altering the object graph of loggers, handlers, filters, formatters at run-time, once a configuration is set up; the verbosity of loggers and handlers can be controlled just by setting levels (and, in the case of loggers, propagation flags). Changing the object graph arbitrarily in a safe way is problematic in a multi-threaded environment; while not impossible, the benefits are not worth the complexity it adds to the implementation.

Thus, when the incremental key of a configuration dict is present and is True, the system will completely ignore any formatters and filters entries, and process only the level settings in the handlers entries, and the level and propagate settings in the loggers and root entries.

Using a value in the configuration dict lets configurations to be sent over the wire as pickled dicts to a socket listener. Thus, the logging verbosity of a long-running application can be altered over time with no need to stop and restart the application.

15.7.21.2.3. Object connections

The schema describes a set of logging objects - loggers, handlers, formatters, filters - which are connected to each other in an object graph. Thus, the schema needs to represent connections between the objects. For example, say that, once configured, a particular logger has attached to it a particular handler. For the purposes of this discussion, we can say that the logger represents the source, and the handler the destination, of a connection between the two. Of course in the configured objects this is represented by the logger holding a reference to the handler. In the configuration dict, this is done by giving each destination object an id which identifies it unambiguously, and then using the id in the source object’s configuration to indicate that a connection exists between the source and the destination object with that id.

So, for example, consider the following YAML snippet:

formatters:
  brief:
    # configuration for formatter with id 'brief' goes here
  precise:
    # configuration for formatter with id 'precise' goes here
handlers:
  h1: #This is an id
   # configuration of handler with id 'h1' goes here
   formatter: brief
  h2: #This is another id
   # configuration of handler with id 'h2' goes here
   formatter: precise
loggers:
  foo.bar.baz:
    # other configuration for logger 'foo.bar.baz'
    handlers: [h1, h2]

(Note: YAML used here because it’s a little more readable than the equivalent Python source form for the dictionary.)

The ids for loggers are the logger names which would be used programmatically to obtain a reference to those loggers, e.g. foo.bar.baz. The ids for Formatters and Filters can be any string value (such as brief, precise above) and they are transient, in that they are only meaningful for processing the configuration dictionary and used to determine connections between objects, and are not persisted anywhere when the configuration call is complete.

The above snippet indicates that logger named foo.bar.baz should have two handlers attached to it, which are described by the handler ids h1 and h2. The formatter for h1 is that described by id brief, and the formatter for h2 is that described by id precise.

15.7.21.2.4. User-defined objects

The schema supports user-defined objects for handlers, filters and formatters. (Loggers do not need to have different types for different instances, so there is no support in this configuration schema for user-defined logger classes.)

Objects to be configured are described by dictionaries which detail their configuration. In some places, the logging system will be able to infer from the context how an object is to be instantiated, but when a user-defined object is to be instantiated, the system will not know how to do this. In order to provide complete flexibility for user-defined object instantiation, the user needs to provide a ‘factory’ - a callable which is called with a configuration dictionary and which returns the instantiated object. This is signalled by an absolute import path to the factory being made available under the special key '()'. Here’s a concrete example:

formatters:
  brief:
    format: '%(message)s'
  default:
    format: '%(asctime)s %(levelname)-8s %(name)-15s %(message)s'
    datefmt: '%Y-%m-%d %H:%M:%S'
  custom:
      (): my.package.customFormatterFactory
      bar: baz
      spam: 99.9
      answer: 42

The above YAML snippet defines three formatters. The first, with id brief, is a standard logging.Formatter instance with the specified format string. The second, with id default, has a longer format and also defines the time format explicitly, and will result in a logging.Formatter initialized with those two format strings. Shown in Python source form, the brief and default formatters have configuration sub-dictionaries:

{
  'format' : '%(message)s'
}

and:

{
  'format' : '%(asctime)s %(levelname)-8s %(name)-15s %(message)s',
  'datefmt' : '%Y-%m-%d %H:%M:%S'
}

respectively, and as these dictionaries do not contain the special key '()', the instantiation is inferred from the context: as a result, standard logging.Formatter instances are created. The configuration sub-dictionary for the third formatter, with id custom, is:

{
  '()' : 'my.package.customFormatterFactory',
  'bar' : 'baz',
  'spam' : 99.9,
  'answer' : 42
}

and this contains the special key '()', which means that user-defined instantiation is wanted. In this case, the specified factory callable will be used. If it is an actual callable it will be used directly - otherwise, if you specify a string (as in the example) the actual callable will be located using normal import mechanisms. The callable will be called with the remaining items in the configuration sub-dictionary as keyword arguments. In the above example, the formatter with id custom will be assumed to be returned by the call:

my.package.customFormatterFactory(bar='baz', spam=99.9, answer=42)

The key '()' has been used as the special key because it is not a valid keyword parameter name, and so will not clash with the names of the keyword arguments used in the call. The '()' also serves as a mnemonic that the corresponding value is a callable.

15.7.21.2.5. Access to external objects

There are times where a configuration needs to refer to objects external to the configuration, for example sys.stderr. If the configuration dict is constructed using Python code, this is straightforward, but a problem arises when the configuration is provided via a text file (e.g. JSON, YAML). In a text file, there is no standard way to distinguish sys.stderr from the literal string 'sys.stderr'. To facilitate this distinction, the configuration system looks for certain special prefixes in string values and treat them specially. For example, if the literal string 'ext://sys.stderr' is provided as a value in the configuration, then the ext:// will be stripped off and the remainder of the value processed using normal import mechanisms.

The handling of such prefixes is done in a way analogous to protocol handling: there is a generic mechanism to look for prefixes which match the regular expression ^(?P<prefix>[a-z]+)://(?P<suffix>.*)$ whereby, if the prefix is recognised, the suffix is processed in a prefix-dependent manner and the result of the processing replaces the string value. If the prefix is not recognised, then the string value will be left as-is.

15.7.21.2.6. Access to internal objects

As well as external objects, there is sometimes also a need to refer to objects in the configuration. This will be done implicitly by the configuration system for things that it knows about. For example, the string value 'DEBUG' for a level in a logger or handler will automatically be converted to the value logging.DEBUG, and the handlers, filters and formatter entries will take an object id and resolve to the appropriate destination object.

However, a more generic mechanism is needed for user-defined objects which are not known to the logging module. For example, consider logging.handlers.MemoryHandler, which takes a target argument which is another handler to delegate to. Since the system already knows about this class, then in the configuration, the given target just needs to be the object id of the relevant target handler, and the system will resolve to the handler from the id. If, however, a user defines a my.package.MyHandler which has an alternate handler, the configuration system would not know that the alternate referred to a handler. To cater for this, a generic resolution system allows the user to specify:

handlers:
  file:
    # configuration of file handler goes here

  custom:
    (): my.package.MyHandler
    alternate: cfg://handlers.file

The literal string 'cfg://handlers.file' will be resolved in an analogous way to strings with the ext:// prefix, but looking in the configuration itself rather than the import namespace. The mechanism allows access by dot or by index, in a similar way to that provided by str.format. Thus, given the following snippet:

handlers:
  email:
    class: logging.handlers.SMTPHandler
    mailhost: localhost
    fromaddr: [email protected]
    toaddrs:
      - [email protected]
      - [email protected]
    subject: Houston, we have a problem.

in the configuration, the string 'cfg://handlers' would resolve to the dict with key handlers, the string 'cfg://handlers.email would resolve to the dict with key email in the handlers dict, and so on. The string 'cfg://handlers.email.toaddrs[1] would resolve to 'dev_team.domain.tld' and the string 'cfg://handlers.email.toaddrs[0]' would resolve to the value '[email protected]'. The subject value could be accessed using either 'cfg://handlers.email.subject' or, equivalently, 'cfg://handlers.email[subject]'. The latter form only needs to be used if the key contains spaces or non-alphanumeric characters. If an index value consists only of decimal digits, access will be attempted using the corresponding integer value, falling back to the string value if needed.

Given a string cfg://handlers.myhandler.mykey.123, this will resolve to config_dict['handlers']['myhandler']['mykey']['123']. If the string is specified as cfg://handlers.myhandler.mykey[123], the system will attempt to retrieve the value from config_dict['handlers']['myhandler']['mykey'][123], and fall back to config_dict['handlers']['myhandler']['mykey']['123'] if that fails.

15.7.21.3. 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 identifies 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', '[email protected]', ['[email protected]', '[email protected]'], '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.

15.7.21.4. 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"

15.7.22. More examples

15.7.22.1. 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.

15.7.22.2. 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()