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.. _tut3:
.. currentmodule:: nagiosplugin
Tutorial #3: check_users
========================
In the third tutorial, we will learn how to process multiple metrics.
Additionally, we will see how to use logging and verbosity levels.
Multiple metrics
----------------
A plugin can perform several measurements at once. This is often necessary to
perform more complex state evaluations or improve latency. Consider a check that
determines both the number of total logged in users and the number of unique
logged in users.
A Resource implementation could look like this:
.. code-block:: python
class Users(nagiosplugin.Resource):
def __init__(self):
self.users = []
self.unique_users = set()
def list_users(self):
"""Return logged in users as list of user names."""
[...]
return users
def probe(self):
"""Return both total and unique user count."""
self.users = self.list_users()
self.unique_users = set(self.users)
return [nagiosplugin.Metric('total', len(self.users), min=0,
context='users'),
nagiosplugin.Metric('unique', len(self.unique_users), min=0,
context='users')]
The `probe()` method returns a list containing two metric objects.
Alternatively, the `probe()` method can act as generator and yield
metrics:
.. code-block:: python
def probe(self):
"""Return both total and unique user count."""
self.users = self.list_users()
self.unique_users = set(self.users)
yield nagiosplugin.Metric('total', len(self.users), min=0,
context='users')
yield nagiosplugin.Metric('unique', len(self.unique_users), min=0,
context='users')]
This may be more comfortable than constructing a list of metrics first and
returning them all at once.
To assign a :class:`~nagiosplugin.context.Context` to a
:class:`~nagiosplugin.metric.Metric`, pass the context's name in the metric's
**context** parameter. Both metrics use the same context "users". This way, the
main function must define only one context that applies the same thresholds to
both metrics:
.. code-block:: python
@nagiosplugin.guarded
def main():
argp = argparse.ArgumentParser()
[...]
args = argp.parse_args()
check = nagiosplugin.Check(
Users(),
nagiosplugin.ScalarContext('users', args.warning, args.critical,
fmt_metric='{value} users logged in'))
check.main()
Multiple contexts
-----------------
The above example defines only one context for all metrics. This may not be
practical. Each metric should get its own context now. By default, a metric is
matched by a context of the same name. So we just leave out the **context**
parameters:
.. code-block:: python
def probe(self):
[...]
return [nagiosplugin.Metric('total', len(self.users), min=0),
nagiosplugin.Metric('unique', len(self.unique_users), min=0)]
We then define two contexts (one for each metric) in the `main()` function:
.. code-block:: python
@nagiosplugin.guarded
def main():
[...]
args = argp.parse_args()
check = nagiosplugin.Check(
Users(),
nagiosplugin.ScalarContext('total', args.warning, args.critical,
fmt_metric='{value} users logged in'),
nagiosplugin.ScalarContext(
'unique', args.warning_unique, args.critical_unique,
fmt_metric='{value} unique users logged in'))
check.main(args.verbose, args.timeout)
Alternatively, we can require every context that fits in metric definitions.
Logging and verbosity levels
----------------------------
**nagiosplugin** integrates with the `logging`_ module from Python's standard
library. If the main function is decorated with `guarded` (which is heavily
recommended), the logging module gets automatically configured before the
execution of the `main()` function starts. Messages logged to the *nagiosplugin*
logger (or any sublogger) are processed with nagiosplugin's integrated logging.
Consider the following example check::
import argparse
import nagiosplugin
import logging
_log = logging.getLogger('nagiosplugin')
class Logging(nagiosplugin.Resource):
def probe(self):
_log.warning('warning message')
_log.info('info message')
_log.debug('debug message')
return [nagiosplugin.Metric('zero', 0, context='default')]
@nagiosplugin.guarded
def main():
argp = argparse.ArgumentParser()
argp.add_argument('-v', '--verbose', action='count', default=0)
args = argp.parse_args()
check = nagiosplugin.Check(Logging())
check.main(args.verbose)
if __name__ == '__main__':
main()
The verbosity level is set in the :meth:`check.main()` invocation depending on
the number of "-v" flags. Let's test this check:
.. code-block:: bash
$ check_verbose.py
LOGGING OK - zero is 0 | zero=0
warning message (check_verbose.py:11)
$ check_verbose.py -v
LOGGING OK - zero is 0
warning message (check_verbose.py:11)
| zero=0
$ check_verbose.py -vv
LOGGING OK - zero is 0
warning message (check_verbose.py:11)
info message (check_verbose.py:12)
| zero=0
$ check_verbose.py -vvv
LOGGING OK - zero is 0
warning message (check_verbose.py:11)
info message (check_verbose.py:12)
debug message (check_verbose.py:13)
| zero=0
When called with *verbose=0,* both the summary and the performance data are
printed on one line and the warning message is displayed. Messages logged with
*warning* or *error* level are always printed.
Setting *verbose* to 1 does not change the logging level but enable multi-line
output. Additionally, full tracebacks would be printed in the case of an
uncaught exception.
Verbosity levels of 2 and 3 enable logging with *info* or *debug* levels.
This behaviour conforms to the "Verbose output" suggestions found in the
`Nagios plug-in development guidelines`_.
The initial verbosity level is 1 (multi-line output). This means that tracebacks
are printed for uncaught exceptions raised in the initialization phase (before
:meth:`Check.main` is called). This is generally a good thing. To suppress
tracebacks during initialization, call :func:`~nagiosplugin.runtime.guarded`
with an optional `verbose` parameter. Example:
.. code-block:: python
@nagiosplugin.guarded(verbose=0)
def main():
[...]
.. note::
The initial verbosity level takes effect only until :meth:`Check.main`
is called with a different verbosity level.
It is advisable to sprinkle logging statements in the plugin code, especially
into the resource model classes. A logging example for a users check could look
like this:
.. code-block:: python
class Users(nagiosplugin.Resource):
[...]
def list_users(self):
"""Return list of logged in users."""
_log.info('querying users with "%s" command', self.who_cmd)
users = []
try:
for line in subprocess.check_output([self.who_cmd]).splitlines():
_log.debug('who output: %s', line.strip())
users.append(line.split()[0].decode())
except OSError:
raise nagiosplugin.CheckError(
'cannot determine number of users ({} failed)'.format(
self.who_cmd))
_log.debug('found users: %r', users)
return users
Interesting items to log are: the command which is invoked to query the
information from the system, or the raw result to verify that parsing works
correctly.
.. _logging: http://docs.python.org/3/library/logging.html
.. _Nagios plug-in development guidelines: http://nagiosplug.sourceforge.net/developer-guidelines.html#AEN39
.. vim: set spell spelllang=en:
|
