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:

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:

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 Context to a 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:

@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:

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:

@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 check.main() invocation depending on the number of “-v” flags. Let’s test this check:

$ 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 Check.main() is called). This is generally a good thing. To suppress tracebacks during initialization, call guarded() with an optional verbose parameter. Example:

@nagiosplugin.guarded(verbose=0)
def main():
   [...]

Note

The initial verbosity level takes effect only until 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:

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.