check_load.rst

.. currentmodule:: nagiosplugin

Tutorial #2: check_load

In this tutorial, we will discuss important basic features that are present in nearly every check. These include command line processing, metric evaluation with scalar contexts, status line formatting and logging.

The :program:`check_load` plugin resembles the one found in the standard Nagios plugins collection. It allows to check the system load average against thresholds.

Data acquisition

First, we will subclass :class:`~nagiosplugin.resource.Resource` to generate metrics for the 1, 5, and 15 minute load averages.

.. literalinclude:: /../nagiosplugin/examples/check_load.py
   :start-after: # data acquisition
   :end-before: # data presentation

:program:`check_load` has two modes of operation: the load averages may either be takes as read from the kernel or normalized by cpu. Accordingly, the :meth:`Load()` constructor has a parameter two switch normalization on.

In the :meth:`Load.probe` method the check reads the load averages from the :file:`/proc` filesystem and extracts the interesting values. For each value, a :class:`~nagiosplugin.metric.Metric` object is returned. Each metric has a generated name ("load1", "load5", "load15") and a value. We don't declare a unit of measure since load averages come without unit. All metrics will share the same context "load" which means that the thresholds for all three values will be the same.

Note

Deriving the number of CPUs from :file:`/proc` is a little bit messy and deserves an extra method. Resource classes may encapsulate arbitrary complex measurement logic as long they define a :meth:`Resource.probe` method that returns a list of metrics. In the code example shown above, we sprinkle some logging statements which show effects when the check is called with an increased logging level (discussed below).

Evaluation

The :program:`check_load` plugin should accept warning and critical ranges and determine if any load value is outside these ranges. Since this kind of logic is pretty standard for most of all Nagios/Icinga plugins, :mod:`nagiosplugin` provides a generalized context class for it. It is the :class:`~nagiosplugin.context.ScalarContext` class which accepts a warning and a critical range as well as a template to present metric values in a human-readable way.

When :class:`~nagiosplugin.context.ScalarContext` is sufficient, it may be configured during instantiation right in the main function. A first version of the main function looks like this:

def main():
    argp = argparse.ArgumentParser(description=__doc__)
    argp.add_argument('-w', '--warning', metavar='RANGE', default='',
                      help='return warning if load is outside RANGE')
    argp.add_argument('-c', '--critical', metavar='RANGE', default='',
                      help='return critical if load is outside RANGE')
    argp.add_argument('-r', '--percpu', action='store_true', default=False)
    args = argp.parse_args()
    check = nagiosplugin.Check(
        Load(args.percpu),
        nagiosplugin.ScalarContext('load', args.warning, args.critical))
    check.main()

Note that the context name "load" is referenced by all three metrics returned by the Load.probe method.

This version of :program:`check_load` is already functional:

$ ./check_load.py
LOAD OK - load1 is 0.11
| load15=0.21;;;0 load1=0.11;;;0 load5=0.18;;;0

$ ./check_load.py -c 0.1:0.2
LOAD CRITICAL - load15 is 0.22 (outside 0.1:0.2)
| load15=0.22;;0.1:0.2;0 load1=0.11;;0.1:0.2;0 load5=0.2;;0.1:0.2;0
# exit status 2

$ ./check_load.py -c 0.1:0.2 -r
LOAD OK - load1 is 0.105
| load15=0.11;;0.1:0.2;0 load1=0.105;;0.1:0.2;0 load5=0.1;;0.1:0.2;0

In the first invocation (lines 1--3), :program:`check_load` reports only the first load value which looks bit arbitrary. In the second invocation (lines 5--8), we set a critical threshold. The range specification is parsed automatically according to the :term:`Nagios plugin API` and the first metric that lies outside is reported. In the third invocation (lines 10--12), we request normalization and all values fit in the range this time.

Result presentation

Although we now have a running check, the output is not as informative as it could be. The first line of output (status line) is very important since the information presented therein should give the admin a clue what is going on. We want the first line to display:

• a load overview when there is nothing wrong
• which load value violates a threshold, if applicable
• which threshold is being violated, if applicable.

The last two points are already covered by the :class:`~nagiosplugin.result.Result` default implementation, but we need to tweak the summary to display a load overview as stated in the first point:

.. literalinclude:: /../nagiosplugin/examples/check_load.py
   :start-after: # data presentation
   :end-before: # runtime environment and data evaluation

The :class:`~nagiosplugin.summary.Summary` class has three methods which can be specialized: :meth:`~nagiosplugin.summary.Summary.ok` to return a status line when there are no problems, :meth:`~nagiosplugin.summary.Summary.problem` to return a status line when the overall check status indicates problems, and :meth:`~nagiosplugin.summary.Summary.verbose` to generate additional output. All three methods get a set of :class:`~nagiosplugin.result.Result` objects passed in. In our code, the ok method queries uses the original metrics referenced by the result objects to build an overview like "loadavg is 0.19, 0.16, 0.14".

Check setup

The last step in this tutorial is to put the pieces together:

.. literalinclude:: /../nagiosplugin/examples/check_load.py
   :start-after: # runtime environment and data evaluation

In the :py:func:`main` function we parse the command line parameters using the standard :class:`argparse.ArgumentParser` class. Watch the :class:`~nagiosplugin.check.Check` object creation: its constructor can be fed with a variable number of :class:`~nagiosplugin.resource.Resource`, :class:`~nagiosplugin.context.Context`, and :class:`~nagiosplugin.summary.Summary` objects. In this tutorial, instances of our specialized Load and LoadSummary classes go in.

We did not specialize a :class:`~nagiosplugin.context.Context` class to evaluate the load metrics. Instead, we use the supplied :class:`~nagiosplugin.context.ScalarContext` which compares a scalar value against two ranges according to the range syntax defined by the Nagios plugin API. The default :class:`~nagiosplugin.context.ScalarContext` implementation covers the majority of evaluation needs. Checks using non-scalar metrics or requiring special logic should subclass :class:`~nagiosplugin.context.Context` to fit their needs.

The check's :meth:`~nagiosplugin.check.Check.main` method runs the check, prints the check's output including summary, log messages and :term:`performance data` to stdout and exits the plugin with the appropriate exit code.

Note the :func:`~nagiosplugin.runtime.guarded` decorator in front of the main function. It helps the code part outside :class:`~nagiosplugin.check.Check` to behave: in case of uncaught exceptions, it ensures that the exit code is 3 (unknown) and that the exception string is properly formatted. Additionally, logging is set up at an early stage so that even messages logged from constructors are captured and printed at the right place in the output (between status line and performance data).