Siebel EIM-Open UI: Pick Applet not rendering properly from Toolbar button

Siebel Articles Python Perl 5 Documentation Manuel PHP7 français

Java Das PHP7 Handbuch PHP7 Manual English Linux and Unix News

Python 3.6.5 Documentation > Logging HOWTO

Logging HOWTO
*************

Author:
Vinay Sajip <vinay_sajip at red-dove dot com>

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.

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):

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:

WARNING:root:Watch out!

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:

--log=INFO

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.

Changing the format of displayed messages
-----------------------------------------

To change the format which is used to display messages, you need to
specify the format you want to use:

import logging
logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.DEBUG)
logging.debug('This message should appear on the console')
logging.info('So should this')
logging.warning('And this, too')

which would print:

DEBUG:This message should appear on the console
INFO:So should this
WARNING:And this, too

Notice that the ‘root’ which appeared in earlier examples has
disappeared. For a full set of things that can appear in format
strings, you can refer to the documentation for LogRecord attributes,
but for simple usage, you just need the *levelname* (severity),
*message* (event description, including variable data) and perhaps to
display when the event occurred. This is described in the next
section.

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 ISO8601. 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.

[image]

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).

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 three optional arguments – a message
format string, a date format string and a style indicator.

logging.Formatter.__init__(fmt=None, datefmt=None, style='%')

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 "style" is one of *%*,
‘{‘ or ‘$’. If one of these is not specified, then ‘%’ will be used.

If the "style" is ‘%’, the message format string uses "%(<dictionary
key>)s" styled string substitution; the possible keys are documented
in LogRecord attributes. If the style is ‘{‘, the message format
string is assumed to be compatible with "str.format()" (using keyword
arguments), while if the style is ‘$’ then the message format string
should conform to what is expected by "string.Template.substitute()".

Changed in version 3.2: Added the "style" parameter.

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'

Formatters use a user-configurable function to convert the creation
time of a record to a tuple. By default, "time.localtime()" is used;
to change this for a particular formatter instance, set the
"converter" attribute of the instance to a function with the same
signature as "time.localtime()" or "time.gmtime()". To change it for
all formatters, for example if you want all logging times to be shown
in GMT, set the "converter" attribute in the Formatter class (to
"time.gmtime" for GMT display).

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.

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:

1. "StreamHandler" instances send messages to streams (file-like
objects).

2. "FileHandler" instances send 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 messages to disk files,
with support for maximum log file sizes and log file rotation.

5. "TimedRotatingFileHandler" instances send messages to disk
files, rotating the log file at certain timed intervals.

6. "SocketHandler" instances send messages to TCP/IP sockets. Since
3.4, Unix domain sockets are also supported.

7. "DatagramHandler" instances send messages to UDP sockets. Since
3.4, Unix domain sockets are also supported.

8. "SMTPHandler" instances send messages to a designated email
address.

9. "SysLogHandler" instances send messages to a Unix syslog daemon,
possibly on a remote machine.

10. "NTEventLogHandler" instances send messages to a Windows
NT/2000/XP event log.

11. "MemoryHandler" instances send messages to a buffer in memory,
which is flushed whenever specific criteria are met.

12. "HTTPHandler" instances send 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. "QueueHandler" instances send messages to a queue, such as
those implemented in the "queue" or "multiprocessing" modules.

15. "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 3.1: The "NullHandler" class.

New in version 3.2: The "QueueHandler" class.

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.

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.

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.

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.

Note: In some cases, "isEnabledFor()" can itself be more expensive
than you’d like (e.g. for deeply nested loggers where an explicit
level is only set high up in the logger hierarchy). In such cases
(or if you want to avoid calling a method in tight loops), you can
cache the result of a call to "isEnabledFor()" in a local or
instance variable, and use that instead of calling the method each
time. Such a cached value would only need to be recomputed when the
logging configuration changes dynamically while the application is
running (which is not all that common).

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". This |
| | avoids calling "sys._getframe()", which |
| | may help to speed up your code in |
| | environments like PyPy (which can’t |
| | speed up code that uses |
| | "sys._getframe()"), if and when PyPy |
| | supports Python 3.x. |
+-------------------------------------------------+------------------------------------------+
| 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.