Basic Logging Tutorial
Logging is a means of tracking events that happen when some software runs. The
software’s developer adds logging calls to their code to indicate that certain
events have occurred. An event is described by a descriptive message which can
optionally contain variable data (i.e. data that is potentially different for
each occurrence of the event). Events also have an importance which the
developer ascribes to the event; the importance can also be called the level
or severity.
When to use logging
Logging provides a set of convenience functions for simple logging usage. These
are debug(), info(), warning(), error() and
critical(). To determine when to use logging, see the table below, which
states, for each of a set of common tasks, the best tool to use for it.
| Task you want to perform |
The best tool for the task |
| Display console output for ordinary
usage of a command line script or
program |
print() |
| Report events that occur during
normal operation of a program (e.g.
for status monitoring or fault
investigation) |
logging.info() (or
logging.debug() for very
detailed output for diagnostic
purposes) |
| Issue a warning regarding a
particular runtime event |
warnings.warn() in library
code if the issue is avoidable and
the client application should be
modified to eliminate the warning
logging.warning() if there is
nothing the client application can do
about the situation, but the event
should still be noted
|
| Report an error regarding a
particular runtime event |
Raise an exception |
| Report suppression of an error
without raising an exception (e.g.
error handler in a long-running
server process) |
logging.error(),
logging.exception() or
logging.critical() as
appropriate for the specific error
and application domain |
The logging functions are named after the level or severity of the events
they are used to track. The standard levels and their applicability are
described below (in increasing order of severity):
| Level |
When it’s used |
DEBUG |
Detailed information, typically of interest
only when diagnosing problems. |
INFO |
Confirmation that things are working as
expected. |
WARNING |
An indication that something unexpected
happened, or indicative of some problem in
the near future (e.g. ‘disk space low’).
The software is still working as expected. |
ERROR |
Due to a more serious problem, the software
has not been able to perform some function. |
CRITICAL |
A serious error, indicating that the program
itself may be unable to continue running. |
The default level is WARNING, which means that only events of this level
and above will be tracked, unless the logging package is configured to do
otherwise.
Events that are tracked can be handled in different ways. The simplest way of
handling tracked events is to print them to the console. Another common way
is to write them to a disk file.
A simple example
A very simple example is:
import logging
logging.warning('Watch out!') # will print a message to the console
logging.info('I told you so') # will not print anything
If you type these lines into a script and run it, you’ll see:
printed out on the console. The INFO message doesn’t appear because the
default level is WARNING. The printed message includes the indication of
the level and the description of the event provided in the logging call, i.e.
‘Watch out!’. Don’t worry about the ‘root’ part for now: it will be explained
later. The actual output can be formatted quite flexibly if you need that;
formatting options will also be explained later.
Logging to a file
A very common situation is that of recording logging events in a file, so let’s
look at that next. Be sure to try the following in a newly-started Python
interpreter, and don’t just continue from the session described above:
import logging
logging.basicConfig(filename='example.log',level=logging.DEBUG)
logging.debug('This message should go to the log file')
logging.info('So should this')
logging.warning('And this, too')
And now if we open the file and look at what we have, we should find the log
messages:
DEBUG:root:This message should go to the log file
INFO:root:So should this
WARNING:root:And this, too
This example also shows how you can set the logging level which acts as the
threshold for tracking. In this case, because we set the threshold to
DEBUG, all of the messages were printed.
If you want to set the logging level from a command-line option such as:
and you have the value of the parameter passed for --log in some variable
loglevel, you can use:
getattr(logging, loglevel.upper())
to get the value which you’ll pass to basicConfig() via the level
argument. You may want to error check any user input value, perhaps as in the
following example:
# assuming loglevel is bound to the string value obtained from the
# command line argument. Convert to upper case to allow the user to
# specify --log=DEBUG or --log=debug
numeric_level = getattr(logging, loglevel.upper(), None)
if not isinstance(numeric_level, int):
raise ValueError('Invalid log level: %s' % loglevel)
logging.basicConfig(level=numeric_level, ...)
The call to basicConfig() should come before any calls to debug(),
info() etc. As it’s intended as a one-off simple configuration facility,
only the first call will actually do anything: subsequent calls are effectively
no-ops.
If you run the above script several times, the messages from successive runs
are appended to the file example.log. If you want each run to start afresh,
not remembering the messages from earlier runs, you can specify the filemode
argument, by changing the call in the above example to:
logging.basicConfig(filename='example.log', filemode='w', level=logging.DEBUG)
The output will be the same as before, but the log file is no longer appended
to, so the messages from earlier runs are lost.
Logging from multiple modules
If your program consists of multiple modules, here’s an example of how you
could organize logging in it:
# myapp.py
import logging
import mylib
def main():
logging.basicConfig(filename='myapp.log', level=logging.INFO)
logging.info('Started')
mylib.do_something()
logging.info('Finished')
if __name__ == '__main__':
main()
# mylib.py
import logging
def do_something():
logging.info('Doing something')
If you run myapp.py, you should see this in myapp.log:
INFO:root:Started
INFO:root:Doing something
INFO:root:Finished
which is hopefully what you were expecting to see. You can generalize this to
multiple modules, using the pattern in mylib.py. Note that for this simple
usage pattern, you won’t know, by looking in the log file, where in your
application your messages came from, apart from looking at the event
description. If you want to track the location of your messages, you’ll need
to refer to the documentation beyond the tutorial level – see
Advanced Logging Tutorial.
Logging variable data
To log variable data, use a format string for the event description message and
append the variable data as arguments. For example:
import logging
logging.warning('%s before you %s', 'Look', 'leap!')
will display:
WARNING:root:Look before you leap!
As you can see, merging of variable data into the event description message
uses the old, %-style of string formatting. This is for backwards
compatibility: the logging package pre-dates newer formatting options such as
str.format() and string.Template. These newer formatting
options are supported, but exploring them is outside the scope of this
tutorial: see Using particular formatting styles throughout your application for more information.
Displaying the date/time in messages
To display the date and time of an event, you would place ‘%(asctime)s’ in
your format string:
import logging
logging.basicConfig(format='%(asctime)s %(message)s')
logging.warning('is when this event was logged.')
which should print something like this:
2010-12-12 11:41:42,612 is when this event was logged.
The default format for date/time display (shown above) is like ISO8601 or
RFC 3339. If you need more control over the formatting of the date/time, provide
a datefmt argument to basicConfig, as in this example:
import logging
logging.basicConfig(format='%(asctime)s %(message)s', datefmt='%m/%d/%Y %I:%M:%S %p')
logging.warning('is when this event was logged.')
which would display something like this:
12/12/2010 11:46:36 AM is when this event was logged.
The format of the datefmt argument is the same as supported by
time.strftime().
Next Steps
That concludes the basic tutorial. It should be enough to get you up and
running with logging. There’s a lot more that the logging package offers, but
to get the best out of it, you’ll need to invest a little more of your time in
reading the following sections. If you’re ready for that, grab some of your
favourite beverage and carry on.
If your logging needs are simple, then use the above examples to incorporate
logging into your own scripts, and if you run into problems or don’t
understand something, please post a question on the comp.lang.python Usenet
group (available at https://groups.google.com/group/comp.lang.python) and you
should receive help before too long.
Still here? You can carry on reading the next few sections, which provide a
slightly more advanced/in-depth tutorial than the basic one above. After that,
you can take a look at the Logging Cookbook.
Advanced Logging Tutorial
The logging library takes a modular approach and offers several categories
of components: loggers, handlers, filters, and formatters.
- Loggers expose the interface that application code directly uses.
- Handlers send the log records (created by loggers) to the appropriate
destination.
- Filters provide a finer grained facility for determining which log records
to output.
- Formatters specify the layout of log records in the final output.
Log event information is passed between loggers, handlers, filters and
formatters in a LogRecord instance.
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.
A good convention to use when naming loggers is to use a module-level logger,
in each module which uses logging, named as follows:
logger = logging.getLogger(__name__)
This means that logger names track the package/module hierarchy, and it’s
intuitively obvious where events are logged just from the logger name.
The root of the hierarchy of loggers is called the root logger. That’s the
logger used by the functions debug(), info(), warning(),
error() and critical(), which just call the same-named method of
the root logger. The functions and the methods have the same signatures. The
root logger’s name is printed as ‘root’ in the logged output.
It is, of course, possible to log messages to different destinations. Support
is included in the package for writing log messages to files, HTTP GET/POST
locations, email via SMTP, generic sockets, queues, or OS-specific logging
mechanisms such as syslog or the Windows NT event log. Destinations are served
by handler classes. You can create your own log destination class if
you have special requirements not met by any of the built-in handler classes.
By default, no destination is set for any logging messages. You can specify
a destination (such as console or file) by using basicConfig() as in the
tutorial examples. If you call the functions debug(), info(),
warning(), error() and critical(), they will check to see
if no destination is set; and if one is not set, they will set a destination
of the console (sys.stderr) and a default format for the displayed
message before delegating to the root logger to do the actual message output.
The default format set by basicConfig() for messages is:
severity:logger name:message
You can change this by passing a format string to basicConfig() with the
format keyword argument. For all options regarding how a format string is
constructed, see Formatter Objects.
Logging Flow
The flow of log event information in loggers and handlers is illustrated in the
following diagram.
Loggers
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.
These are the most common configuration methods:
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.addHandler() and Logger.removeHandler() add and remove
handler objects from the logger object. Handlers are covered in more detail
in Handlers.
Logger.addFilter() and Logger.removeFilter() add and remove filter
objects from the logger object. Filters are covered in more detail in
Filter Objects.
You don’t need to always call these methods on every logger you create. See the
last two paragraphs in this section.
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
name 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.
Loggers have a concept of effective level. If a level is not explicitly set
on a logger, the level of its parent is used instead as its effective level.
If the parent has no explicit level set, its parent is examined, and so on -
all ancestors are searched until an explicitly set level is found. The root
logger always has an explicit level set (WARNING by default). When deciding
whether to process an event, the effective level of the logger is used to
determine whether the event is passed to the logger’s handlers.
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.
(You can, however, turn off propagation by setting the propagate
attribute of a logger to False.)
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 (see
Useful Handlers); the tutorials use mainly 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
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).
Configuring Logging
Programmers can configure logging in three ways:
- Creating loggers, handlers, and formatters explicitly using Python
code that calls the configuration methods listed above.
- Creating a logging config file and reading it using the
fileConfig()
function.
- Creating a dictionary of configuration information and passing it
to the
dictConfig() function.
For the reference documentation on the last two options, see
Configuration functions. 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.
Warning
The fileConfig() function takes a default parameter,
disable_existing_loggers, which defaults to True for reasons of
backward compatibility. This may or may not be what you want, since it
will cause any loggers existing before the fileConfig() call to
be disabled unless they (or an ancestor) are explicitly named in the
configuration. Please refer to the reference documentation for more
information, and specify False for this parameter if you wish.
The dictionary passed to dictConfig() can also specify a Boolean
value with key disable_existing_loggers, which if not specified
explicitly in the dictionary also defaults to being interpreted as
True. This leads to the logger-disabling behaviour described above,
which may not be what you want - in which case, provide the key
explicitly with a value of False.
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
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).
In Python 3.2, 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: '%(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.
What happens if no configuration is provided
If no logging configuration is provided, it is possible to have a situation
where a logging event needs to be output, but no handlers can be found to
output the event. The behaviour of the logging package in these
circumstances is dependent on the Python version.
For versions of Python prior to 3.2, the behaviour is as follows:
- If logging.raiseExceptions is
False (production mode), the event is
silently dropped.
- If logging.raiseExceptions is
True (development mode), a message
‘No handlers could be found for logger X.Y.Z’ is printed once.
In Python 3.2 and later, the behaviour is as follows:
- The event is output using a ‘handler of last resort’, stored in
logging.lastResort. This internal handler is not associated with any
logger, and acts like a StreamHandler which writes the
event description message to the current value of sys.stderr (therefore
respecting any redirections which may be in effect). No formatting is
done on the message - just the bare event description message is printed.
The handler’s level is set to WARNING, so all events at this and
greater severities will be output.
To obtain the pre-3.2 behaviour, logging.lastResort can be set to None.
Configuring Logging for a Library
When developing a library which uses logging, you should take care to
document how the library uses logging - for example, the names of loggers
used. Some consideration also needs to be given to its logging configuration.
If the using application does not use logging, and library code makes logging
calls, then (as described in the previous section) events of severity
WARNING and greater will be printed to sys.stderr. This is regarded as
the best default behaviour.
If for some reason you don’t want these messages printed in the absence of
any logging configuration, you can attach a do-nothing handler to the top-level
logger for your library. This avoids the message being printed, since a handler
will be always be found for the library’s events: 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 is included in the logging package:
NullHandler (since Python 3.1). An instance of this handler
could be added to the top-level logger of the logging namespace used by the
library (if you want to prevent your library’s logged events being output to
sys.stderr in the absence of logging configuration). If all logging by a
library foo is done using loggers with names matching ‘foo.x’, ‘foo.x.y’,
etc. then the code:
import logging
logging.getLogger('foo').addHandler(logging.NullHandler())
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’.
Note
It is strongly advised that you do not add any handlers other
than NullHandler to your library’s loggers. This is
because the configuration of handlers is the prerogative of the application
developer who uses your library. The application developer knows their
target audience and what handlers are most appropriate for their
application: if you add handlers ‘under the hood’, you might well interfere
with their ability to carry out unit tests and deliver logs which suit their
requirements.
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().
Custom Levels
Defining your own levels is possible, but should not be necessary, as the
existing levels have been chosen on the basis of practical experience.
However, if you are convinced that you need custom levels, great care should
be exercised when doing this, and it is possibly a very bad idea to define
custom levels if you are developing a library. That’s because if multiple
library authors all define their own custom levels, there is a chance that
the logging output from such multiple libraries used together will be
difficult for the using developer to control and/or interpret, because a
given numeric value might mean different things for different libraries.
Useful Handlers
In addition to the base Handler class, many useful subclasses are
provided:
StreamHandler instances send messages to streams (file-like
objects).
FileHandler instances send messages to disk files.
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.
RotatingFileHandler instances send messages to disk
files, with support for maximum log file sizes and log file rotation.
TimedRotatingFileHandler instances send messages to
disk files, rotating the log file at certain timed intervals.
SocketHandler instances send messages to TCP/IP
sockets. Since 3.4, Unix domain sockets are also supported.
DatagramHandler instances send messages to UDP
sockets. Since 3.4, Unix domain sockets are also supported.
SMTPHandler instances send messages to a designated
email address.
SysLogHandler instances send messages to a Unix
syslog daemon, possibly on a remote machine.
NTEventLogHandler instances send messages to a
Windows NT/2000/XP event log.
MemoryHandler instances send messages to a buffer
in memory, which is flushed whenever specific criteria are met.
HTTPHandler instances send messages to an HTTP
server using either GET or POST semantics.
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.
QueueHandler instances send messages to a queue, such as
those implemented in the queue or multiprocessing modules.
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.
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.