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 <div class="section" id="logging-howto">
<h1>Logging HOWTO<a class="headerlink" href="#logging-howto" title="Permalink to this headline">¶</a></h1>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Author:</th><td class="field-body">Vinay Sajip &lt;vinay_sajip at red-dove dot com&gt;</td>
</tr>
</tbody>
</table>
<div class="section" id="basic-logging-tutorial">
<h2>Basic Logging Tutorial<a class="headerlink" href="#basic-logging-tutorial" title="Permalink to this headline">¶</a></h2>
Logging is a means of tracking events that happen when some software runs. The
software&#8217;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.
<div class="section" id="when-to-use-logging">
<h3>When to use logging<a class="headerlink" href="#when-to-use-logging" title="Permalink to this headline">¶</a></h3>
Logging provides a set of convenience functions for simple logging usage. These
are <a class="reference internal" href="../library/logging.html#logging.debug" title="logging.debug"><tt class="xref py py-func docutils literal">debug()</tt></a>, <a class="reference internal" href="../library/logging.html#logging.info" title="logging.info"><tt class="xref py py-func docutils literal">info()</tt></a>, <a class="reference internal" href="../library/logging.html#logging.warning" title="logging.warning"><tt class="xref py py-func docutils literal">warning()</tt></a>, <a class="reference internal" href="../library/logging.html#logging.error" title="logging.error"><tt class="xref py py-func docutils literal">error()</tt></a> and
<a class="reference internal" href="../library/logging.html#logging.critical" title="logging.critical"><tt class="xref py py-func docutils literal">critical()</tt></a>. 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.
<table border="1" class="docutils">
<colgroup>
<col width="49%" />
<col width="51%" />
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Task you want to perform</th>
<th class="head">The best tool for the task</th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td>Display console output for ordinary
usage of a command line script or
program</td>
<td><a class="reference internal" href="../library/functions.html#print" title="print"><tt class="xref py py-func docutils literal">print()</tt></a></td>
</tr>
<tr class="row-odd"><td>Report events that occur during
normal operation of a program (e.g.
for status monitoring or fault
investigation)</td>
<td><a class="reference internal" href="../library/logging.html#logging.info" title="logging.info"><tt class="xref py py-func docutils literal">logging.info()</tt></a> (or
<a class="reference internal" href="../library/logging.html#logging.debug" title="logging.debug"><tt class="xref py py-func docutils literal">logging.debug()</tt></a> for very
detailed output for diagnostic
purposes)</td>
</tr>
<tr class="row-even"><td>Issue a warning regarding a
particular runtime event</td>
<td><a class="reference internal" href="../library/warnings.html#warnings.warn" title="warnings.warn"><tt class="xref py py-func docutils literal">warnings.warn()</tt></a> in library
code if the issue is avoidable and
the client application should be
modified to eliminate the warning
<a class="reference internal" href="../library/logging.html#logging.warning" title="logging.warning"><tt class="xref py py-func docutils literal">logging.warning()</tt></a> if there is
nothing the client application can do
about the situation, but the event
should still be noted
</td>
</tr>
<tr class="row-odd"><td>Report an error regarding a
particular runtime event</td>
<td>Raise an exception</td>
</tr>
<tr class="row-even"><td>Report suppression of an error
without raising an exception (e.g.
error handler in a long-running
server process)</td>
<td><a class="reference internal" href="../library/logging.html#logging.error" title="logging.error"><tt class="xref py py-func docutils literal">logging.error()</tt></a>,
<a class="reference internal" href="../library/logging.html#logging.exception" title="logging.exception"><tt class="xref py py-func docutils literal">logging.exception()</tt></a> or
<a class="reference internal" href="../library/logging.html#logging.critical" title="logging.critical"><tt class="xref py py-func docutils literal">logging.critical()</tt></a> as
appropriate for the specific error
and application domain</td>
</tr>
</tbody>
</table>
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):
<table border="1" class="docutils">
<colgroup>
<col width="24%" />
<col width="76%" />
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Level</th>
<th class="head">When it&#8217;s used</th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td><tt class="docutils literal">DEBUG</tt></td>
<td>Detailed information, typically of interest
only when diagnosing problems.</td>
</tr>
<tr class="row-odd"><td><tt class="docutils literal">INFO</tt></td>
<td>Confirmation that things are working as
expected.</td>
</tr>
<tr class="row-even"><td><tt class="docutils literal">WARNING</tt></td>
<td>An indication that something unexpected
happened, or indicative of some problem in
the near future (e.g. &#8216;disk space low&#8217;).
The software is still working as expected.</td>
</tr>
<tr class="row-odd"><td><tt class="docutils literal">ERROR</tt></td>
<td>Due to a more serious problem, the software
has not been able to perform some function.</td>
</tr>
<tr class="row-even"><td><tt class="docutils literal">CRITICAL</tt></td>
<td>A serious error, indicating that the program
itself may be unable to continue running.</td>
</tr>
</tbody>
</table>
The default level is <tt class="docutils literal">WARNING</tt>, 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.
</div>
<div class="section" id="a-simple-example">
<h3>A simple example<a class="headerlink" href="#a-simple-example" title="Permalink to this headline">¶</a></h3>
A very simple example is:
<div class="highlight-python"><div class="highlight"><pre>import logging
logging.warning(&#39;Watch out!&#39;) # will print a message to the console
logging.info(&#39;I told you so&#39;) # will not print anything
</pre></div>
</div>
If you type these lines into a script and run it, you&#8217;ll see:
<div class="highlight-python"><pre>WARNING:root:Watch out!</pre>
</div>
printed out on the console. The <tt class="docutils literal">INFO</tt> message doesn&#8217;t appear because the
default level is <tt class="docutils literal">WARNING</tt>. The printed message includes the indication of
the level and the description of the event provided in the logging call, i.e.
&#8216;Watch out!&#8217;. Don&#8217;t worry about the &#8216;root&#8217; 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.
</div>
<div class="section" id="logging-to-a-file">
<h3>Logging to a file<a class="headerlink" href="#logging-to-a-file" title="Permalink to this headline">¶</a></h3>
A very common situation is that of recording logging events in a file, so let&#8217;s
look at that next:
<div class="highlight-python"><div class="highlight"><pre>import logging
logging.basicConfig(filename=&#39;example.log&#39;,level=logging.DEBUG)
logging.debug(&#39;This message should go to the log file&#39;)
logging.info(&#39;So should this&#39;)
logging.warning(&#39;And this, too&#39;)
</pre></div>
</div>
And now if we open the file and look at what we have, we should find the log
messages:
<div class="highlight-python"><pre>DEBUG:root:This message should go to the log file
INFO:root:So should this
WARNING:root:And this, too</pre>
</div>
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
<tt class="docutils literal">DEBUG</tt>, all of the messages were printed.
If you want to set the logging level from a command-line option such as:
<div class="highlight-python"><div class="highlight"><pre>--log=INFO
</pre></div>
</div>
and you have the value of the parameter passed for <tt class="docutils literal">--log</tt> in some variable
loglevel, you can use:
<div class="highlight-python"><div class="highlight"><pre>getattr(logging, loglevel.upper())
</pre></div>
</div>
to get the value which you&#8217;ll pass to <a class="reference internal" href="../library/logging.html#logging.basicConfig" title="logging.basicConfig"><tt class="xref py py-func docutils literal">basicConfig()</tt></a> via the level
argument. You may want to error check any user input value, perhaps as in the
following example:
<div class="highlight-python"><div class="highlight"><pre># 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(&#39;Invalid log level: %s&#39; % loglevel)
logging.basicConfig(level=numeric_level, ...)
</pre></div>
</div>
The call to <a class="reference internal" href="../library/logging.html#logging.basicConfig" title="logging.basicConfig"><tt class="xref py py-func docutils literal">basicConfig()</tt></a> should come before any calls to <a class="reference internal" href="../library/logging.html#logging.debug" title="logging.debug"><tt class="xref py py-func docutils literal">debug()</tt></a>,
<a class="reference internal" href="../library/logging.html#logging.info" title="logging.info"><tt class="xref py py-func docutils literal">info()</tt></a> etc. As it&#8217;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:
<div class="highlight-python"><div class="highlight"><pre>logging.basicConfig(filename=&#39;example.log&#39;, filemode=&#39;w&#39;, level=logging.DEBUG)
</pre></div>
</div>
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.
</div>
<div class="section" id="logging-from-multiple-modules">
<h3>Logging from multiple modules<a class="headerlink" href="#logging-from-multiple-modules" title="Permalink to this headline">¶</a></h3>
If your program consists of multiple modules, here&#8217;s an example of how you
could organize logging in it:
<div class="highlight-python"><div class="highlight"><pre># myapp.py
import logging
import mylib

def main():
 logging.basicConfig(filename=&#39;myapp.log&#39;, level=logging.INFO)
 logging.info(&#39;Started&#39;)
 mylib.do_something()
 logging.info(&#39;Finished&#39;)

if __name__ == &#39;__main__&#39;:
 main()
</pre></div>
</div>
<div class="highlight-python"><div class="highlight"><pre># mylib.py
import logging

def do_something():
 logging.info(&#39;Doing something&#39;)
</pre></div>
</div>
If you run myapp.py, you should see this in myapp.log:
<div class="highlight-python"><pre>INFO:root:Started
INFO:root:Doing something
INFO:root:Finished</pre>
</div>
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&#8217;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&#8217;ll need
to refer to the documentation beyond the tutorial level &#8211; see
<a class="reference internal" href="#logging-advanced-tutorial">Advanced Logging Tutorial</a>.
</div>
<div class="section" id="logging-variable-data">
<h3>Logging variable data<a class="headerlink" href="#logging-variable-data" title="Permalink to this headline">¶</a></h3>
To log variable data, use a format string for the event description message and
append the variable data as arguments. For example:
<div class="highlight-python"><div class="highlight"><pre>import logging
logging.warning(&#39;%s before you %s&#39;, &#39;Look&#39;, &#39;leap!&#39;)
</pre></div>
</div>
will display:
<div class="highlight-python"><pre>WARNING:root:Look before you leap!</pre>
</div>
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
<a class="reference internal" href="../library/stdtypes.html#str.format" title="str.format"><tt class="xref py py-meth docutils literal">str.format()</tt></a> and <a class="reference internal" href="../library/string.html#string.Template" title="string.Template"><tt class="xref py py-class docutils literal">string.Template</tt></a>. These newer formatting
options are supported, but exploring them is outside the scope of this
tutorial.
</div>
<div class="section" id="changing-the-format-of-displayed-messages">
<h3>Changing the format of displayed messages<a class="headerlink" href="#changing-the-format-of-displayed-messages" title="Permalink to this headline">¶</a></h3>
To change the format which is used to display messages, you need to
specify the format you want to use:
<div class="highlight-python"><div class="highlight"><pre>import logging
logging.basicConfig(format=&#39;%(levelname)s:%(message)s&#39;, level=logging.DEBUG)
logging.debug(&#39;This message should appear on the console&#39;)
logging.info(&#39;So should this&#39;)
logging.warning(&#39;And this, too&#39;)
</pre></div>
</div>
which would print:
<div class="highlight-python"><pre>DEBUG:This message should appear on the console
INFO:So should this
WARNING:And this, too</pre>
</div>
Notice that the &#8216;root&#8217; 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 <a class="reference internal" href="../library/logging.html#logrecord-attributes">LogRecord attributes</a>, 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.
</div>
<div class="section" id="displaying-the-date-time-in-messages">
<h3>Displaying the date/time in messages<a class="headerlink" href="#displaying-the-date-time-in-messages" title="Permalink to this headline">¶</a></h3>
To display the date and time of an event, you would place &#8216;%(asctime)s&#8217; in
your format string:
<div class="highlight-python"><div class="highlight"><pre>import logging
logging.basicConfig(format=&#39;%(asctime)s %(message)s&#39;)
logging.warning(&#39;is when this event was logged.&#39;)
</pre></div>
</div>
which should print something like this:
<div class="highlight-python"><pre>2010-12-12 11:41:42,612 is when this event was logged.</pre>
</div>
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 <tt class="docutils literal">basicConfig</tt>, as in this example:
<div class="highlight-python"><div class="highlight"><pre>import logging
logging.basicConfig(format=&#39;%(asctime)s %(message)s&#39;, datefmt=&#39;%m/%d/%Y %I:%M:%S %p&#39;)
logging.warning(&#39;is when this event was logged.&#39;)
</pre></div>
</div>
which would display something like this:
<div class="highlight-python"><pre>12/12/2010 11:46:36 AM is when this event was logged.</pre>
</div>
The format of the datefmt argument is the same as supported by
<a class="reference internal" href="../library/time.html#time.strftime" title="time.strftime"><tt class="xref py py-func docutils literal">time.strftime()</tt></a>.
</div>
<div class="section" id="next-steps">
<h3>Next Steps<a class="headerlink" href="#next-steps" title="Permalink to this headline">¶</a></h3>
That concludes the basic tutorial. It should be enough to get you up and
running with logging. There&#8217;s a lot more that the logging package offers, but
to get the best out of it, you&#8217;ll need to invest a little more of your time in
reading the following sections. If you&#8217;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&#8217;t
understand something, please post a question on the comp.lang.python Usenet
group (available at <a class="reference external" href="http://groups.google.com/group/comp.lang.python">http://groups.google.com/group/comp.lang.python</a>) 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 <a class="reference internal" href="logging-cookbook.html#logging-cookbook">Logging Cookbook</a>.
</div>
</div>
<div class="section" id="advanced-logging-tutorial">
<h2>Advanced Logging Tutorial<a class="headerlink" href="#advanced-logging-tutorial" title="Permalink to this headline">¶</a></h2>
The logging library takes a modular approach and offers several categories
of components: loggers, handlers, filters, and formatters.
<ul class="simple">
<li>Loggers expose the interface that application code directly uses.</li>
<li>Handlers send the log records (created by loggers) to the appropriate
destination.</li>
<li>Filters provide a finer grained facility for determining which log records
to output.</li>
<li>Formatters specify the layout of log records in the final output.</li>
</ul>
Log event information is passed between loggers, handlers, filters and
formatters in a <a class="reference internal" href="../library/logging.html#logging.LogRecord" title="logging.LogRecord"><tt class="xref py py-class docutils literal">LogRecord</tt></a> instance.
Logging is performed by calling methods on instances of the <a class="reference internal" href="../library/logging.html#logging.Logger" title="logging.Logger"><tt class="xref py py-class docutils literal">Logger</tt></a>
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 &#8216;scan&#8217; is the parent of loggers
&#8216;scan.text&#8217;, &#8216;scan.html&#8217; and &#8216;scan.pdf&#8217;. 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:
<div class="highlight-python"><div class="highlight"><pre>logger = logging.getLogger(__name__)
</pre></div>
</div>
This means that logger names track the package/module hierarchy, and it&#8217;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&#8217;s the
logger used by the functions <a class="reference internal" href="../library/logging.html#logging.debug" title="logging.debug"><tt class="xref py py-func docutils literal">debug()</tt></a>, <a class="reference internal" href="../library/logging.html#logging.info" title="logging.info"><tt class="xref py py-func docutils literal">info()</tt></a>, <a class="reference internal" href="../library/logging.html#logging.warning" title="logging.warning"><tt class="xref py py-func docutils literal">warning()</tt></a>,
<a class="reference internal" href="../library/logging.html#logging.error" title="logging.error"><tt class="xref py py-func docutils literal">error()</tt></a> and <a class="reference internal" href="../library/logging.html#logging.critical" title="logging.critical"><tt class="xref py py-func docutils literal">critical()</tt></a>, which just call the same-named method of
the root logger. The functions and the methods have the same signatures. The
root logger&#8217;s name is printed as &#8216;root&#8217; 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, 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 <a class="reference internal" href="../library/logging.html#logging.basicConfig" title="logging.basicConfig"><tt class="xref py py-func docutils literal">basicConfig()</tt></a> as in the
tutorial examples. If you call the functions <a class="reference internal" href="../library/logging.html#logging.debug" title="logging.debug"><tt class="xref py py-func docutils literal">debug()</tt></a>, <a class="reference internal" href="../library/logging.html#logging.info" title="logging.info"><tt class="xref py py-func docutils literal">info()</tt></a>,
<a class="reference internal" href="../library/logging.html#logging.warning" title="logging.warning"><tt class="xref py py-func docutils literal">warning()</tt></a>, <a class="reference internal" href="../library/logging.html#logging.error" title="logging.error"><tt class="xref py py-func docutils literal">error()</tt></a> and <a class="reference internal" href="../library/logging.html#logging.critical" title="logging.critical"><tt class="xref py py-func docutils literal">critical()</tt></a>, they will check to see
if no destination is set; and if one is not set, they will set a destination
of the console (<tt class="docutils literal">sys.stderr</tt>) 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 <a class="reference internal" href="../library/logging.html#logging.basicConfig" title="logging.basicConfig"><tt class="xref py py-func docutils literal">basicConfig()</tt></a> for messages is:
<div class="highlight-python"><pre>severity:logger name:message</pre>
</div>
You can change this by passing a format string to <a class="reference internal" href="../library/logging.html#logging.basicConfig" title="logging.basicConfig"><tt class="xref py py-func docutils literal">basicConfig()</tt></a> with the
format keyword argument. For all options regarding how a format string is
constructed, see <a class="reference internal" href="../library/logging.html#formatter-objects">Formatter Objects</a>.
<div class="section" id="logging-flow">
<h3>Logging Flow<a class="headerlink" href="#logging-flow" title="Permalink to this headline">¶</a></h3>
The flow of log event information in loggers and handlers is illustrated in the
following diagram.
<img alt="../_images/logging_flow.png" src="../_images/logging_flow.png" />
</div>
<div class="section" id="loggers">
<h3>Loggers<a class="headerlink" href="#loggers" title="Permalink to this headline">¶</a></h3>
<a class="reference internal" href="../library/logging.html#logging.Logger" title="logging.Logger"><tt class="xref py py-class docutils literal">Logger</tt></a> 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:
<ul class="simple">
<li><a class="reference internal" href="../library/logging.html#logging.Logger.setLevel" title="logging.Logger.setLevel"><tt class="xref py py-meth docutils literal">Logger.setLevel()</tt></a> 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.</li>
<li><a class="reference internal" href="../library/logging.html#logging.Logger.addHandler" title="logging.Logger.addHandler"><tt class="xref py py-meth docutils literal">Logger.addHandler()</tt></a> and <a class="reference internal" href="../library/logging.html#logging.Logger.removeHandler" title="logging.Logger.removeHandler"><tt class="xref py py-meth docutils literal">Logger.removeHandler()</tt></a> add and remove
handler objects from the logger object. Handlers are covered in more detail
in <a class="reference internal" href="#handler-basic">Handlers</a>.</li>
<li><a class="reference internal" href="../library/logging.html#logging.Logger.addFilter" title="logging.Logger.addFilter"><tt class="xref py py-meth docutils literal">Logger.addFilter()</tt></a> and <a class="reference internal" href="../library/logging.html#logging.Logger.removeFilter" title="logging.Logger.removeFilter"><tt class="xref py py-meth docutils literal">Logger.removeFilter()</tt></a> add and remove filter
objects from the logger object. Filters are covered in more detail in
<a class="reference internal" href="../library/logging.html#filter">Filter Objects</a>.</li>
</ul>
You don&#8217;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:
<ul class="simple">
<li><a class="reference internal" href="../library/logging.html#logging.Logger.debug" title="logging.Logger.debug"><tt class="xref py py-meth docutils literal">Logger.debug()</tt></a>, <a class="reference internal" href="../library/logging.html#logging.Logger.info" title="logging.Logger.info"><tt class="xref py py-meth docutils literal">Logger.info()</tt></a>, <a class="reference internal" href="../library/logging.html#logging.Logger.warning" title="logging.Logger.warning"><tt class="xref py py-meth docutils literal">Logger.warning()</tt></a>,
<a class="reference internal" href="../library/logging.html#logging.Logger.error" title="logging.Logger.error"><tt class="xref py py-meth docutils literal">Logger.error()</tt></a>, and <a class="reference internal" href="../library/logging.html#logging.Logger.critical" title="logging.Logger.critical"><tt class="xref py py-meth docutils literal">Logger.critical()</tt></a> 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 <tt class="xref py py-const docutils literal">%s</tt>, <tt class="xref py py-const docutils literal">%d</tt>, <tt class="xref py py-const docutils literal">%f</tt>, 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 <tt class="xref py py-const docutils literal">**kwargs</tt>, the
logging methods care only about a keyword of <tt class="xref py py-const docutils literal">exc_info</tt> and use it to
determine whether to log exception information.</li>
<li><a class="reference internal" href="../library/logging.html#logging.Logger.exception" title="logging.Logger.exception"><tt class="xref py py-meth docutils literal">Logger.exception()</tt></a> creates a log message similar to
<a class="reference internal" href="../library/logging.html#logging.Logger.error" title="logging.Logger.error"><tt class="xref py py-meth docutils literal">Logger.error()</tt></a>. The difference is that <a class="reference internal" href="../library/logging.html#logging.Logger.exception" title="logging.Logger.exception"><tt class="xref py py-meth docutils literal">Logger.exception()</tt></a> dumps a
stack trace along with it. Call this method only from an exception handler.</li>
<li><a class="reference internal" href="../library/logging.html#logging.Logger.log" title="logging.Logger.log"><tt class="xref py py-meth docutils literal">Logger.log()</tt></a> 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.</li>
</ul>
<a class="reference internal" href="../library/logging.html#logging.getLogger" title="logging.getLogger"><tt class="xref py py-func docutils literal">getLogger()</tt></a> returns a reference to a logger instance with the specified
name if it is provided, or <tt class="docutils literal">root</tt> if not. The names are period-separated
hierarchical structures. Multiple calls to <a class="reference internal" href="../library/logging.html#logging.getLogger" title="logging.getLogger"><tt class="xref py py-func docutils literal">getLogger()</tt></a> 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 <tt class="docutils literal">foo</tt>, loggers with names of
<tt class="docutils literal">foo.bar</tt>, <tt class="docutils literal">foo.bar.baz</tt>, and <tt class="docutils literal">foo.bam</tt> are all descendants of <tt class="docutils literal">foo</tt>.
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 (<tt class="docutils literal">WARNING</tt> 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&#8217;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.)
</div>
<div class="section" id="handlers">
<h3>Handlers<a class="headerlink" href="#handlers" title="Permalink to this headline">¶</a></h3>
<tt class="xref py py-class docutils literal">Handler</tt> objects are responsible for dispatching the
appropriate log messages (based on the log messages&#8217; severity) to the handler&#8217;s
specified destination. Logger objects can add zero or more handler objects to
themselves with an <tt class="xref py py-func docutils literal">addHandler()</tt> 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
<a class="reference internal" href="#useful-handlers">Useful Handlers</a>); the tutorials use mainly <a class="reference internal" href="../library/logging.handlers.html#logging.StreamHandler" title="logging.StreamHandler"><tt class="xref py py-class docutils literal">StreamHandler</tt></a> and
<a class="reference internal" href="../library/logging.handlers.html#logging.FileHandler" title="logging.FileHandler"><tt class="xref py py-class docutils literal">FileHandler</tt></a> 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:
<ul class="simple">
<li>The <a class="reference internal" href="../library/logging.html#logging.Handler.setLevel" title="logging.Handler.setLevel"><tt class="xref py py-meth docutils literal">Handler.setLevel()</tt></a> method, just as in logger objects, specifies the
lowest severity that will be dispatched to the appropriate destination. Why
are there two <tt class="xref py py-func docutils literal">setLevel()</tt> 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.</li>
<li><tt class="xref py py-func docutils literal">setFormatter()</tt> selects a Formatter object for this handler to use.</li>
<li><tt class="xref py py-func docutils literal">addFilter()</tt> and <tt class="xref py py-func docutils literal">removeFilter()</tt> respectively configure and
deconfigure filter objects on handlers.</li>
</ul>
Application code should not directly instantiate and use instances of
<tt class="xref py py-class docutils literal">Handler</tt>. Instead, the <tt class="xref py py-class docutils literal">Handler</tt> 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).
</div>
<div class="section" id="formatters">
<h3>Formatters<a class="headerlink" href="#formatters" title="Permalink to this headline">¶</a></h3>
Formatter objects configure the final order, structure, and contents of the log
message. Unlike the base <tt class="xref py py-class docutils literal">logging.Handler</tt> 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 &#8211; a message format string and a date format string.
<dl class="method">
<dt id="logging.logging.Formatter.__init__">
<tt class="descclassname">logging.Formatter.</tt><tt class="descname">__init__</tt><big>(</big>fmt=None, datefmt=None<big>)</big><a class="headerlink" href="#logging.logging.Formatter.__init__" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

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:
<div class="highlight-python"><pre>%Y-%m-%d %H:%M:%S</pre>
</div>
with the milliseconds tacked on at the end.
The message format string uses <tt class="docutils literal">%(&lt;dictionary key&gt;)s</tt> styled string
substitution; the possible keys are documented in <a class="reference internal" href="../library/logging.html#logrecord-attributes">LogRecord attributes</a>.
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:
<div class="highlight-python"><div class="highlight"><pre>&#39;%(asctime)s - %(levelname)s - %(message)s&#39;
</pre></div>
</div>
Formatters use a user-configurable function to convert the creation time of a
record to a tuple. By default, <a class="reference internal" href="../library/time.html#time.localtime" title="time.localtime"><tt class="xref py py-func docutils literal">time.localtime()</tt></a> is used; to change this
for a particular formatter instance, set the <tt class="docutils literal">converter</tt> attribute of the
instance to a function with the same signature as <a class="reference internal" href="../library/time.html#time.localtime" title="time.localtime"><tt class="xref py py-func docutils literal">time.localtime()</tt></a> or
<a class="reference internal" href="../library/time.html#time.gmtime" title="time.gmtime"><tt class="xref py py-func docutils literal">time.gmtime()</tt></a>. To change it for all formatters, for example if you want
all logging times to be shown in GMT, set the <tt class="docutils literal">converter</tt> attribute in the
Formatter class (to <tt class="docutils literal">time.gmtime</tt> for GMT display).
</div>
<div class="section" id="configuring-logging">
<h3>Configuring Logging<a class="headerlink" href="#configuring-logging" title="Permalink to this headline">¶</a></h3>
Programmers can configure logging in three ways:
<ol class="arabic simple">
<li>Creating loggers, handlers, and formatters explicitly using Python
code that calls the configuration methods listed above.</li>
<li>Creating a logging config file and reading it using the <a class="reference internal" href="../library/logging.config.html#logging.config.fileConfig" title="logging.config.fileConfig"><tt class="xref py py-func docutils literal">fileConfig()</tt></a>
function.</li>
<li>Creating a dictionary of configuration information and passing it
to the <a class="reference internal" href="../library/logging.config.html#logging.config.dictConfig" title="logging.config.dictConfig"><tt class="xref py py-func docutils literal">dictConfig()</tt></a> function.</li>
</ol>
For the reference documentation on the last two options, see
<a class="reference internal" href="../library/logging.config.html#logging-config-api">Configuration functions</a>. The following example configures a very simple
logger, a console handler, and a simple formatter using Python code:
<div class="highlight-python"><div class="highlight"><pre>import logging

# create logger
logger = logging.getLogger(&#39;simple_example&#39;)
logger.setLevel(logging.DEBUG)

# create console handler and set level to debug
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)

# create formatter
formatter = logging.Formatter(&#39;%(asctime)s - %(name)s - %(levelname)s - %(message)s&#39;)

# add formatter to ch
ch.setFormatter(formatter)

# add ch to logger
logger.addHandler(ch)

# &#39;application&#39; code
logger.debug(&#39;debug message&#39;)
logger.info(&#39;info message&#39;)
logger.warn(&#39;warn message&#39;)
logger.error(&#39;error message&#39;)
logger.critical(&#39;critical message&#39;)
</pre></div>
</div>
Running this module from the command line produces the following output:
<div class="highlight-python"><pre>$ 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</pre>
</div>
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:
<div class="highlight-python"><div class="highlight"><pre>import logging
import logging.config

logging.config.fileConfig(&#39;logging.conf&#39;)

[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=</pre>
</div>
The output is nearly identical to that of the non-config-file-based example:
<div class="highlight-python"><pre>$ 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</pre>
</div>
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.
<div class="admonition warning">
Warning
The <a class="reference internal" href="../library/logging.config.html#logging.config.fileConfig" title="logging.config.fileConfig"><tt class="xref py py-func docutils literal">fileConfig()</tt></a> function takes a default parameter,
<tt class="docutils literal">disable_existing_loggers</tt>, which defaults to <tt class="docutils literal">True</tt> for reasons of
backward compatibility. This may or may not be what you want, since it
will cause any loggers existing before the <a class="reference internal" href="../library/logging.config.html#logging.config.fileConfig" title="logging.config.fileConfig"><tt class="xref py py-func docutils literal">fileConfig()</tt></a> 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 <tt class="docutils literal">False</tt> for this parameter if you wish.
The dictionary passed to <a class="reference internal" href="../library/logging.config.html#logging.config.dictConfig" title="logging.config.dictConfig"><tt class="xref py py-func docutils literal">dictConfig()</tt></a> can also specify a Boolean
value with key <tt class="docutils literal">disable_existing_loggers</tt>, which if not specified
explicitly in the dictionary also defaults to being interpreted as
<tt class="docutils literal">True</tt>. 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 <tt class="docutils literal">False</tt>.
</div>
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
<a class="reference internal" href="../library/logging.handlers.html#logging.handlers.WatchedFileHandler" title="logging.handlers.WatchedFileHandler"><tt class="xref py py-class docutils literal">WatchedFileHandler</tt></a> (relative to the logging module) or
<tt class="docutils literal">mypackage.mymodule.MyHandler</tt> (for a class defined in package <tt class="docutils literal">mypackage</tt>
and module <tt class="docutils literal">mymodule</tt>, where <tt class="docutils literal">mypackage</tt> is available on the Python import
path).
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&#8217;s an example of the same configuration as above, in YAML format for
the new dictionary-based approach:
<div class="highlight-python"><pre>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]</pre>
</div>
For more information about logging using a dictionary, see
<a class="reference internal" href="../library/logging.config.html#logging-config-api">Configuration functions</a>.
</div>
<div class="section" id="what-happens-if-no-configuration-is-provided">
<h3>What happens if no configuration is provided<a class="headerlink" href="#what-happens-if-no-configuration-is-provided" title="Permalink to this headline">¶</a></h3>
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 Python 2.x, the behaviour is as follows:
<ul class="simple">
<li>If logging.raiseExceptions is False (production mode), the event is
silently dropped.</li>
<li>If logging.raiseExceptions is True (development mode), a message
&#8216;No handlers could be found for logger X.Y.Z&#8217; is printed once.</li>
</ul>
</div>
<div class="section" id="configuring-logging-for-a-library">
<h3>Configuring Logging for a Library<a class="headerlink" href="#configuring-logging-for-a-library" title="Permalink to this headline">¶</a></h3>
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 configure logging, and library code makes
logging calls, then (as described in the previous section) an error message
will be printed to <tt class="docutils literal">sys.stderr</tt>.
If for some reason you don&#8217;t want this message 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&#8217;s events: it just doesn&#8217;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:
<a class="reference internal" href="../library/logging.handlers.html#logging.NullHandler" title="logging.NullHandler"><tt class="xref py py-class docutils literal">NullHandler</tt></a> (since Python 2.7). 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 an error message being output to
<tt class="docutils literal">sys.stderr</tt> in the absence of logging configuration). If all logging by a
library foo is done using loggers with names matching &#8216;foo.x&#8217;, &#8216;foo.x.y&#8217;,
etc. then the code:
<div class="highlight-python"><div class="highlight"><pre>import logging
logging.getLogger(&#39;foo&#39;).addHandler(logging.NullHandler())
</pre></div>
</div>
should have the desired effect. If an organisation produces a number of
libraries, then the logger name specified can be &#8216;orgname.foo&#8217; rather than
just &#8216;foo&#8217;.
<div class="admonition note">
Note
It is strongly advised that you do not add any handlers other
than <a class="reference internal" href="../library/logging.handlers.html#logging.NullHandler" title="logging.NullHandler"><tt class="xref py py-class docutils literal">NullHandler</tt></a> to your library&#8217;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 &#8216;under the hood&#8217;, you might well interfere
with their ability to carry out unit tests and deliver logs which suit their
requirements.
</div>
</div>
</div>
<div class="section" id="logging-levels">
<h2>Logging Levels<a class="headerlink" href="#logging-levels" title="Permalink to this headline">¶</a></h2>
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.
<table border="1" class="docutils">
<colgroup>
<col width="48%" />
<col width="52%" />
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Level</th>
<th class="head">Numeric value</th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td><tt class="docutils literal">CRITICAL</tt></td>
<td>50</td>
</tr>
<tr class="row-odd"><td><tt class="docutils literal">ERROR</tt></td>
<td>40</td>
</tr>
<tr class="row-even"><td><tt class="docutils literal">WARNING</tt></td>
<td>30</td>
</tr>
<tr class="row-odd"><td><tt class="docutils literal">INFO</tt></td>
<td>20</td>
</tr>
<tr class="row-even"><td><tt class="docutils literal">DEBUG</tt></td>
<td>10</td>
</tr>
<tr class="row-odd"><td><tt class="docutils literal">NOTSET</tt></td>
<td>0</td>
</tr>
</tbody>
</table>
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&#8217;s level is higher than the method call&#8217;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 <a class="reference internal" href="../library/logging.html#logging.LogRecord" title="logging.LogRecord"><tt class="xref py py-class docutils literal">LogRecord</tt></a>
class. When a logger decides to actually log an event, a
<a class="reference internal" href="../library/logging.html#logging.LogRecord" title="logging.LogRecord"><tt class="xref py py-class docutils literal">LogRecord</tt></a> 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 <tt class="xref py py-class docutils literal">Handler</tt>
class. Handlers are responsible for ensuring that a logged message (in the form
of a <a class="reference internal" href="../library/logging.html#logging.LogRecord" title="logging.LogRecord"><tt class="xref py py-class docutils literal">LogRecord</tt></a>) 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
<a class="reference internal" href="../library/logging.html#logging.LogRecord" title="logging.LogRecord"><tt class="xref py py-class docutils literal">LogRecord</tt></a> instances intended for particular destinations. Each logger
can have zero, one or more handlers associated with it (via the
<a class="reference internal" href="../library/logging.html#logging.Logger.addHandler" title="logging.Logger.addHandler"><tt class="xref py py-meth docutils literal">addHandler()</tt></a> method of <a class="reference internal" href="../library/logging.html#logging.Logger" title="logging.Logger"><tt class="xref py py-class docutils literal">Logger</tt></a>). 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&#8217;s
level acts as a filter in the same way as a logger&#8217;s level does. If a handler
decides to actually dispatch an event, the <a class="reference internal" href="../library/logging.html#logging.Handler.emit" title="logging.Handler.emit"><tt class="xref py py-meth docutils literal">emit()</tt></a> method is used
to send the message to its destination. Most user-defined subclasses of
<tt class="xref py py-class docutils literal">Handler</tt> will need to override this <a class="reference internal" href="../library/logging.html#logging.Handler.emit" title="logging.Handler.emit"><tt class="xref py py-meth docutils literal">emit()</tt></a>.
<div class="section" id="custom-levels">
<h3>Custom Levels<a class="headerlink" href="#custom-levels" title="Permalink to this headline">¶</a></h3>
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&#8217;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.
</div>
</div>
<div class="section" id="useful-handlers">
<h2>Useful Handlers<a class="headerlink" href="#useful-handlers" title="Permalink to this headline">¶</a></h2>
In addition to the base <tt class="xref py py-class docutils literal">Handler</tt> class, many useful subclasses are
provided:
<ol class="arabic simple">
<li><a class="reference internal" href="../library/logging.handlers.html#logging.StreamHandler" title="logging.StreamHandler"><tt class="xref py py-class docutils literal">StreamHandler</tt></a> instances send messages to streams (file-like
objects).</li>
<li><a class="reference internal" href="../library/logging.handlers.html#logging.FileHandler" title="logging.FileHandler"><tt class="xref py py-class docutils literal">FileHandler</tt></a> instances send messages to disk files.</li>
<li><tt class="xref py py-class docutils literal">BaseRotatingHandler</tt> is the base class for handlers that
rotate log files at a certain point. It is not meant to be instantiated
directly. Instead, use <a class="reference internal" href="../library/logging.handlers.html#logging.handlers.RotatingFileHandler" title="logging.handlers.RotatingFileHandler"><tt class="xref py py-class docutils literal">RotatingFileHandler</tt></a> or
<a class="reference internal" href="../library/logging.handlers.html#logging.handlers.TimedRotatingFileHandler" title="logging.handlers.TimedRotatingFileHandler"><tt class="xref py py-class docutils literal">TimedRotatingFileHandler</tt></a>.</li>
<li><a class="reference internal" href="../library/logging.handlers.html#logging.handlers.RotatingFileHandler" title="logging.handlers.RotatingFileHandler"><tt class="xref py py-class docutils literal">RotatingFileHandler</tt></a> instances send messages to disk
files, with support for maximum log file sizes and log file rotation.</li>
<li><a class="reference internal" href="../library/logging.handlers.html#logging.handlers.TimedRotatingFileHandler" title="logging.handlers.TimedRotatingFileHandler"><tt class="xref py py-class docutils literal">TimedRotatingFileHandler</tt></a> instances send messages to
disk files, rotating the log file at certain timed intervals.</li>
<li><a class="reference internal" href="../library/logging.handlers.html#logging.handlers.SocketHandler" title="logging.handlers.SocketHandler"><tt class="xref py py-class docutils literal">SocketHandler</tt></a> instances send messages to TCP/IP
sockets.</li>
<li><a class="reference internal" href="../library/logging.handlers.html#logging.handlers.DatagramHandler" title="logging.handlers.DatagramHandler"><tt class="xref py py-class docutils literal">DatagramHandler</tt></a> instances send messages to UDP
sockets.</li>
<li><a class="reference internal" href="../library/logging.handlers.html#logging.handlers.SMTPHandler" title="logging.handlers.SMTPHandler"><tt class="xref py py-class docutils literal">SMTPHandler</tt></a> instances send messages to a designated
email address.</li>
<li><a class="reference internal" href="../library/logging.handlers.html#logging.handlers.SysLogHandler" title="logging.handlers.SysLogHandler"><tt class="xref py py-class docutils literal">SysLogHandler</tt></a> instances send messages to a Unix
syslog daemon, possibly on a remote machine.</li>
<li><a class="reference internal" href="../library/logging.handlers.html#logging.handlers.NTEventLogHandler" title="logging.handlers.NTEventLogHandler"><tt class="xref py py-class docutils literal">NTEventLogHandler</tt></a> instances send messages to a
Windows NT/2000/XP event log.</li>
<li><a class="reference internal" href="../library/logging.handlers.html#logging.handlers.MemoryHandler" title="logging.handlers.MemoryHandler"><tt class="xref py py-class docutils literal">MemoryHandler</tt></a> instances send messages to a buffer
in memory, which is flushed whenever specific criteria are met.</li>
<li><a class="reference internal" href="../library/logging.handlers.html#logging.handlers.HTTPHandler" title="logging.handlers.HTTPHandler"><tt class="xref py py-class docutils literal">HTTPHandler</tt></a> instances send messages to an HTTP
server using either <tt class="docutils literal">GET</tt> or <tt class="docutils literal">POST</tt> semantics.</li>
<li><a class="reference internal" href="../library/logging.handlers.html#logging.handlers.WatchedFileHandler" title="logging.handlers.WatchedFileHandler"><tt class="xref py py-class docutils literal">WatchedFileHandler</tt></a> 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.</li>
<li><a class="reference internal" href="../library/logging.handlers.html#logging.NullHandler" title="logging.NullHandler"><tt class="xref py py-class docutils literal">NullHandler</tt></a> instances do nothing with error messages. They are used
by library developers who want to use logging, but want to avoid the &#8216;No
handlers could be found for logger XXX&#8217; message which can be displayed if
the library user has not configured logging. See <a class="reference internal" href="#library-config">Configuring Logging for a Library</a> for
more information.</li>
</ol>

New in version 2.7: The <a class="reference internal" href="../library/logging.handlers.html#logging.NullHandler" title="logging.NullHandler"><tt class="xref py py-class docutils literal">NullHandler</tt></a> class.
The <a class="reference internal" href="../library/logging.handlers.html#logging.NullHandler" title="logging.NullHandler"><tt class="xref py py-class docutils literal">NullHandler</tt></a>, <a class="reference internal" href="../library/logging.handlers.html#logging.StreamHandler" title="logging.StreamHandler"><tt class="xref py py-class docutils literal">StreamHandler</tt></a> and <a class="reference internal" href="../library/logging.handlers.html#logging.FileHandler" title="logging.FileHandler"><tt class="xref py py-class docutils literal">FileHandler</tt></a>
classes are defined in the core logging package. The other handlers are
defined in a sub- module, <a class="reference internal" href="../library/logging.handlers.html#module-logging.handlers" title="logging.handlers: Handlers for the logging module."><tt class="xref py py-mod docutils literal">logging.handlers</tt></a>. (There is also another
sub-module, <a class="reference internal" href="../library/logging.config.html#module-logging.config" title="logging.config: Configuration of the logging module."><tt class="xref py py-mod docutils literal">logging.config</tt></a>, for configuration functionality.)
Logged messages are formatted for presentation through instances of the
<a class="reference internal" href="../library/logging.html#logging.Formatter" title="logging.Formatter"><tt class="xref py py-class docutils literal">Formatter</tt></a> 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
<tt class="xref py py-class docutils literal">BufferingFormatter</tt> 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 <a class="reference internal" href="../library/logging.html#logging.Filter" title="logging.Filter"><tt class="xref py py-class docutils literal">Filter</tt></a> can be added to both <a class="reference internal" href="../library/logging.html#logging.Logger" title="logging.Logger"><tt class="xref py py-class docutils literal">Logger</tt></a> and
<tt class="xref py py-class docutils literal">Handler</tt> instances (through their <tt class="xref py py-meth docutils literal">addFilter()</tt> 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 <a class="reference internal" href="../library/logging.html#logging.Filter" title="logging.Filter"><tt class="xref py py-class docutils literal">Filter</tt></a> 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.
</div>
<div class="section" id="exceptions-raised-during-logging">
<h2>Exceptions raised during logging<a class="headerlink" href="#exceptions-raised-during-logging" title="Permalink to this headline">¶</a></h2>
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.
<tt class="xref py py-class docutils literal">SystemExit</tt> and <tt class="xref py py-class docutils literal">KeyboardInterrupt</tt> exceptions are never
swallowed. Other exceptions which occur during the <tt class="xref py py-meth docutils literal">emit()</tt> method of a
<tt class="xref py py-class docutils literal">Handler</tt> subclass are passed to its <tt class="xref py py-meth docutils literal">handleError()</tt> method.
The default implementation of <tt class="xref py py-meth docutils literal">handleError()</tt> in <tt class="xref py py-class docutils literal">Handler</tt> checks
to see if a module-level variable, <tt class="xref py py-data docutils literal">raiseExceptions</tt>, is set. If set, a
traceback is printed to <a class="reference internal" href="../library/sys.html#sys.stderr" title="sys.stderr"><tt class="xref py py-data docutils literal">sys.stderr</tt></a>. If not set, the exception is swallowed.
<div class="admonition note">
Note
The default value of <tt class="xref py py-data docutils literal">raiseExceptions</tt> is <tt class="docutils literal">True</tt>. This is
because during development, you typically want to be notified of any
exceptions that occur. It&#8217;s advised that you set <tt class="xref py py-data docutils literal">raiseExceptions</tt> to
<tt class="docutils literal">False</tt> for production usage.
</div>
</div>
<div class="section" id="using-arbitrary-objects-as-messages">
<h2>Using arbitrary objects as messages<a class="headerlink" href="#using-arbitrary-objects-as-messages" title="Permalink to this headline">¶</a></h2>
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
<a class="reference internal" href="../reference/datamodel.html#object.__str__" title="object.__str__"><tt class="xref py py-meth docutils literal">__str__()</tt></a> 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
<tt class="xref py py-class docutils literal">SocketHandler</tt> emits an event by pickling it and sending it over the
wire.
</div>
<div class="section" id="optimization">
<h2>Optimization<a class="headerlink" href="#optimization" title="Permalink to this headline">¶</a></h2>
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 <tt class="xref py py-meth docutils literal">isEnabledFor()</tt>
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:
<div class="highlight-python"><div class="highlight"><pre>if logger.isEnabledFor(logging.DEBUG):
 logger.debug(&#39;Message with %s, %s&#39;, expensive_func1(),
 expensive_func2())
</pre></div>
</div>
so that if the logger&#8217;s threshold is set above <tt class="docutils literal">DEBUG</tt>, the calls to
<tt class="xref py py-func docutils literal">expensive_func1()</tt> and <tt class="xref py py-func docutils literal">expensive_func2()</tt> 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&#8217;s a
list of things you can do to avoid processing during logging which you don&#8217;t
need:
<table border="1" class="docutils">
<colgroup>
<col width="54%" />
<col width="46%" />
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">What you don&#8217;t want to collect</th>
<th class="head">How to avoid collecting it</th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td>Information about where calls were made from.</td>
<td>Set <tt class="docutils literal">logging._srcfile</tt> to <tt class="docutils literal">None</tt>.</td>
</tr>
<tr class="row-odd"><td>Threading information.</td>
<td>Set <tt class="docutils literal">logging.logThreads</tt> to <tt class="docutils literal">0</tt>.</td>
</tr>
<tr class="row-even"><td>Process information.</td>
<td>Set <tt class="docutils literal">logging.logProcesses</tt> to <tt class="docutils literal">0</tt>.</td>
</tr>
</tbody>
</table>
Also note that the core logging module only includes the basic handlers. If
you don&#8217;t import <a class="reference internal" href="../library/logging.handlers.html#module-logging.handlers" title="logging.handlers: Handlers for the logging module."><tt class="xref py py-mod docutils literal">logging.handlers</tt></a> and <a class="reference internal" href="../library/logging.config.html#module-logging.config" title="logging.config: Configuration of the logging module."><tt class="xref py py-mod docutils literal">logging.config</tt></a>, they won&#8217;t
take up any memory.
<div class="admonition-see-also admonition seealso">
See also
<dl class="docutils">
<dt>Module <a class="reference internal" href="../library/logging.html#module-logging" title="logging: Flexible event logging system for applications."><tt class="xref py py-mod docutils literal">logging</tt></a></dt>
<dd>API reference for the logging module.</dd>
<dt>Module <a class="reference internal" href="../library/logging.config.html#module-logging.config" title="logging.config: Configuration of the logging module."><tt class="xref py py-mod docutils literal">logging.config</tt></a></dt>
<dd>Configuration API for the logging module.</dd>
<dt>Module <a class="reference internal" href="../library/logging.handlers.html#module-logging.handlers" title="logging.handlers: Handlers for the logging module."><tt class="xref py py-mod docutils literal">logging.handlers</tt></a></dt>
<dd>Useful handlers included with the logging module.</dd>
</dl>
<a class="reference internal" href="logging-cookbook.html#logging-cookbook">A logging cookbook</a>
</div>
</div>
</div>

</div>
 </div>
 </div>
 <div class="sphinxsidebar">
 <div class="sphinxsidebarwrapper">
 <h3><a href="../contents.html">Table Of Contents</a></h3>
 <ul>
<li><a class="reference internal" href="#">Logging HOWTO</a><ul>
<li><a class="reference internal" href="#basic-logging-tutorial">Basic Logging Tutorial</a><ul>
<li><a class="reference internal" href="#when-to-use-logging">When to use logging</a></li>
<li><a class="reference internal" href="#a-simple-example">A simple example</a></li>
<li><a class="reference internal" href="#logging-to-a-file">Logging to a file</a></li>
<li><a class="reference internal" href="#logging-from-multiple-modules">Logging from multiple modules</a></li>
<li><a class="reference internal" href="#logging-variable-data">Logging variable data</a></li>
<li><a class="reference internal" href="#changing-the-format-of-displayed-messages">Changing the format of displayed messages</a></li>
<li><a class="reference internal" href="#displaying-the-date-time-in-messages">Displaying the date/time in messages</a></li>
<li><a class="reference internal" href="#next-steps">Next Steps</a></li>
</ul>
</li>
<li><a class="reference internal" href="#advanced-logging-tutorial">Advanced Logging Tutorial</a><ul>
<li><a class="reference internal" href="#logging-flow">Logging Flow</a></li>
<li><a class="reference internal" href="#loggers">Loggers</a></li>
<li><a class="reference internal" href="#handlers">Handlers</a></li>
<li><a class="reference internal" href="#formatters">Formatters</a></li>
<li><a class="reference internal" href="#configuring-logging">Configuring Logging</a></li>
<li><a class="reference internal" href="#what-happens-if-no-configuration-is-provided">What happens if no configuration is provided</a></li>
<li><a class="reference internal" href="#configuring-logging-for-a-library">Configuring Logging for a Library</a></li>
</ul>
</li>
<li><a class="reference internal" href="#logging-levels">Logging Levels</a><ul>
<li><a class="reference internal" href="#custom-levels">Custom Levels</a></li>
</ul>
</li>
<li><a class="reference internal" href="#useful-handlers">Useful Handlers</a></li>
<li><a class="reference internal" href="#exceptions-raised-during-logging">Exceptions raised during logging</a></li>
<li><a class="reference internal" href="#using-arbitrary-objects-as-messages">Using arbitrary objects as messages</a></li>
<li><a class="reference internal" href="#optimization">Optimization</a></li>
</ul>
</li>
</ul>

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