nagiosplugin Documentation

This documentation covers nagiosplugin 1.3.3.

Contents

The nagiosplugin library

About

nagiosplugin is a Python class library which helps writing Nagios (or Icinga) compatible plugins easily in Python. It cares for much of the boilerplate code and default logic commonly found in Nagios checks, including:

  • Nagios 3 Plugin API compliant parameters and output formatting
  • Full Nagios range syntax support
  • Automatic threshold checking
  • Multiple independend measures
  • Custom status line to communicate the main point quickly
  • Long output and performance data
  • Timeout handling
  • Persistent “cookies” to retain state information between check runs
  • Resume log file processing at the point where the last run left
  • No dependencies beyond the Python standard library (except for Python 2.6).

nagiosplugin runs on POSIX and Windows systems. It is compatible with Python 2.7, and Python 3.4 through 3.7.

Feedback and Suggestions

nagiosplugin is currently maintained by Matt Pounsett <matt@conundrum.com>. A public issue tracker can be found at <https://github.com/mpounsett/nagiosplugin/issues> for bugs, suggestions, and patches.

License

The nagiosplugin package is released under the Zope Public License 2.1 (ZPL), a BSD-style Open Source license.

Documentation

Comprehensive documentation is available online. The examples mentioned in the tutorials can also be found in the nagiosplugin/examples directory of the source distribution.

Acknowledgements

nagiosplugin was originally written and maintained by Christian Kauhaus <kc@flyingcircus.io>. Additional contributions from the community are acknowledged in the file CONTRIBUTORS.txt

First steps with nagiosplugin

This tutorial will guide you through all important steps of writing a check with the nagiosplugin class library. Read this to get started.

Key concepts

nagiosplugin has a fine-grained class model with clear separation of concerns. This allows plugin writers to focus on one particular tasks at a time while writing plugins. Nagios/Icinga plugins need to perform three steps: data acquisition, evaluation, and presentation. Each step has an associated class (Resource, Context, Summary) and information between tasks is passed with structured value objects (Metric, Result).

Classes overview

Here is a diagram with the most important classes and their relationships:

             +----------+                \
             | Resource |                 |
             +----------+                 |
       _____/      |     \_____           | Acquisition
      v            v           v          |
+---------+   +---------+   +---------+   |
| Metric  |...| Metric  |...| Metric  |  <
+---------+   +---------+   +---------+   |
     |             |             |        |
     v             v             v        |
+---------+   +---------+   +---------+   |
| Context |...| Context |...| Context |   | Evaluation
+---------+   +---------+   +---------+   |
     |             |             |        |
     v             v             v        |
+---------+   +---------+   +---------+   |
| Result  |...| Result  |...| Result  |  <
+---------+   +---------+   +---------+   |
           \___    |    ___/              |
               v   v   v                  | Presentation
              +---------+                 |
              | Summary |                 |
              +---------+                /
Resource

A model of the thing being monitored. It should usually have the same name as the whole plugin. Generates one or more metrics.

Example: system load

Metric

A single measured data point. A metric consists of a name, a value, a unit, and optional minimum and maximum bounds. Most metrics are scalar (the value can be represented as single number).

Example: load1=0.75

Context

Additional information to evaluate a metric. A context has usually a warning and critical range which allows to determine if a given metric is OK or not. Contexts also include information on how to present a metric in a human-readable way.

Example: warning=0.5, critical=1.0

Result

Product of a metric and a context. A result consists of a state (“ok”, “warning”, “critical”, “unknown”), some explanatory text, and references to the objects that it was generated from.

Example: WARNING - load1 is 0.75

Summary

Condenses all results in a single status line. The status line is the plugin’s most important output: it appears in mails, text messages, pager alerts etc.

Example: LOAD WARNING - load1 is 0.75 (greater than 0.5)

The following tutorials which will guide you through the most important features of nagiosplugin.

Hint

Study the source code in the nagiosplugin/examples directory for complete examples.

Tutorials

Tutorial #1: ‘Hello world’ check

In the first tutorial, we will develop check_world. This check will determine if the world exists. The algorithm is simple: if the world would not exist, the check would not execute.

This minimalistic check consists of a Resource World which models the part of the world that is interesting for the purposes of our check. Resource classes must define a Resource.probe() method which returns a list of metrics. We just return a single Metric object that states that the world exists.

#!python

"""Hello world Nagios check."""

import nagiosplugin


class World(nagiosplugin.Resource):

    def probe(self):
        return [nagiosplugin.Metric('world', True, context='null')]


def main():
    check = nagiosplugin.Check(World())
    check.main()


if __name__ == '__main__':
    main()

We don’t have a context to evaluate the returned metric yet, so we resort to the built-in “null” context. The “null” context does nothing with its associated metrics.

We now create a Check object that is fed only with the resource object. We could put context and summary objects into the Check() constructor as well. This will be demonstrated in the next tutorial. There is also no command line processing nor timeout handling nor output control. We call the Check.main() method to evaluate resources, construct text output and exit with the appropriate status code.

Running the plugin creates very simple output:

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$ check_world.py
WORLD OK

The plugin’s exit status is 0, signalling success to the calling process.

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 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 Resource to generate metrics for the 1, 5, and 15 minute load averages.


class Load(nagiosplugin.Resource):
    """Domain model: system load.

    Determines the system load parameters and (optionally) cpu count.
    The `probe` method returns the three standard load average numbers.
    If `percpu` is true, the load average will be normalized.

    This check requires Linux-style /proc files to be present.
    """

    def __init__(self, percpu=False):
        self.percpu = percpu

    @staticmethod
    def cpus():
        _log.info('counting cpus with "nproc"')
        cpus = int(subprocess.check_output(['nproc']))
        _log.debug('found %i cpus in total', cpus)
        return cpus

    def probe(self):
        _log.info('reading load from /proc/loadavg')
        with open('/proc/loadavg') as loadavg:
            load = loadavg.readline().split()[0:3]
        _log.debug('raw load is %s', load)
        cpus = self.cpus() if self.percpu else 1
        load = [float(l) / cpus for l in load]
        for i, period in enumerate([1, 5, 15]):
            yield nagiosplugin.Metric('load%d' % period, load[i], min=0,
                                      context='load')

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 Load() constructor has a parameter two switch normalization on.

In the Load.probe() method the check reads the load averages from the /proc filesystem and extracts the interesting values. For each value, a 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 /proc is a little bit messy and deserves an extra method. Resource classes may encapsulate arbitrary complex measurement logic as long they define a 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 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, nagiosplugin provides a generalized context class for it. It is the 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 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 check_load is already functional:

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$ ./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), 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 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 Result default implementation, but we need to tweak the summary to display a load overview as stated in the first point:


class LoadSummary(nagiosplugin.Summary):
    """Status line conveying load information.

    We specialize the `ok` method to present all three figures in one
    handy tagline. In case of problems, the single-load texts from the
    contexts work well.
    """

    def __init__(self, percpu):
        self.percpu = percpu

    def ok(self, results):
        qualifier = 'per cpu ' if self.percpu else ''
        return 'loadavg %sis %s' % (qualifier, ', '.join(
            str(results[r].metric) for r in ['load1', 'load5', 'load15']))

The Summary class has three methods which can be specialized: ok() to return a status line when there are no problems, problem() to return a status line when the overall check status indicates problems, and verbose() to generate additional output. All three methods get a set of 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:


@nagiosplugin.guarded
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)
    argp.add_argument('-v', '--verbose', action='count', default=0,
                      help='increase output verbosity (use up to 3 times)')
    args = argp.parse_args()
    check = nagiosplugin.Check(
        Load(args.percpu),
        nagiosplugin.ScalarContext('load', args.warning, args.critical),
        LoadSummary(args.percpu))
    check.main(verbose=args.verbose)


if __name__ == '__main__':
    main()

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

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

The check’s main() method runs the check, prints the check’s output including summary, log messages and performance data to stdout and exits the plugin with the appropriate exit code.

Note the guarded() decorator in front of the main function. It helps the code part outside 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).

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.

Topic Guides

Topic guides are meant as explanatory tests which expand on one specific area of the library. Expect more to come here.

Plugin Debugging

Debugging plugins can sometimes be complicated since there are so many classes, which are tied together in an implicit way. I have collected some frequent questions about debugging.

An uncaught exception makes the plugin return UNKNOWN. Where is the cause?

When your plugin raises an exception, you may get very little output. Example:

$ check_users.py
USERS UNKNOWN: RuntimeError: error

Set the verbose parameter of main() to some value greater than zero and you will get the full traceback:

$ check_users.py -v
USERS UNKNOWN: RuntimeError: error
Traceback (most recent call last):
  File "nagiosplugin/runtime.py", line 38, in wrapper
    return func(*args, **kwds)
  File "nagiosplugin/examples/check_users.py", line 104, in main
    check.main(args.verbose, args.timeout)
  File "nagiosplugin/check.py", line 110, in main
    runtime.execute(self, verbose, timeout)
  File "nagiosplugin/runtime.py", line 118, in execute
    with_timeout(self.timeout, self.run, check)
  File "nagiosplugin/platform/posix.py", line 19, in with_timeout
    func(*args, **kwargs)
  File "nagiosplugin/runtime.py", line 107, in run
    check()
  File "nagiosplugin/check.py", line 95, in __call__
    self._evaluate_resource(resource)
  File "nagiosplugin/check.py", line 73, in _evaluate_resource
    metrics = resource.probe()
  File "nagiosplugin/examples/check_users.py", line 57, in probe
    self.users = self.list_users()
  File "nagiosplugin/examples/check_users.py", line 34, in list_users
    raise RuntimeError('error')
RuntimeError: error
A Check constructor dies with “cannot add type <…>”

When you see the following exception raised from Check() (or Check.add()):

UNKNOWN: TypeError: ("cannot add type <class '__main__.Users'> to check", <__main__.Users object at 0x7f0c64f73f90>)

chances are high that you are trying to add an object that is not an instance from Resource, Context, Summary, or Results or its subclasses. A common error is to base a resource class on object instead of Resource.

I’m trying to use pdb but I get a timeout after 10s

When using an interactive debugger like pdb on plugins, you may experience that your debugging session is aborted with a timeout after 10 seconds. Just set the timeout parameter in main() to 0 to avoid this.

API docs

The nagiosplugin module consists of several submodules which are discussed in detail as follows. Refer to the “First steps with nagiosplugin” section for an introduction on how to use them for typical plugins.

Core API

The core API consists of all functions and classes which are called in a plugin’s main function. A typical main function is decorated with guarded() and creates a Check object. The check instance is fed with instances of Resource, Context, or Summary (respective custom subclasses). Finally, control is passed to the check’s main() method.

Note

All classes that plugin authors typically need are imported into the nagiosplugin name space. For example, use

import nagiosplugin
# ...
check = nagiosplugin.Check()

to get a Check instance.

nagiosplugin.check

Example: Skeleton main function

The following pseudo code outlines how Check is typically used in the main function of a plugin:

def main():
   check = nagiosplugin.Check(MyResource1(...), MyResource2(...),
                              MyContext1(...), MyContext2(...),
                              MySummary(...))
   check.main()
nagiosplugin.resource
nagiosplugin.context

Example ScalarContext usage

Configure a ScalarContext with warning and critical ranges found in ArgumentParser’s result object args and add it to a check:

c = Check(..., ScalarContext('metric', args.warning, args.critical), ...)
nagiosplugin.summary
nagiosplugin.runtime

Intermediate data API

The following classes allow to handle intermediate data that are used during the plugin’s execution in a structured way. Most of them are used by the nagiosplugin library itself to create objects which are passed into code written by plugin authors. Other classes (like Metric) are used by plugin authors to generate intermediate data during acquisition or evaluation steps.

Note

All classes that plugin authors typically need are imported directly into the nagiosplugin name space. For example, use

import nagiosplugin
# ...
result = nagiosplugin.Result(nagiosplugin.Ok)

to get a Result instance.

nagiosplugin.metric
nagiosplugin.state

Note

ServiceState is not imported into the nagiosplugin top-level name space since there is usually no need to access it directly.

State subclasses

The state subclasses are singletons. Plugin authors should use the class name (without parentheses) to access the instance. For example:

state = nagiosplugin.Critical
nagiosplugin.performance
nagiosplugin.range
nagiosplugin.result

Auxiliary Classes

nagiosplugin’s auxiliary classes are not strictly required to write checks, but simplify common tasks and provide convenient access to functionality that is regularly needed by plugin authors.

Note

All classes that plugin authors typically need are imported directly into the nagiosplugin name space. For example, use

import nagiosplugin
# ...
with nagiosplugin.Cookie(path) as cookie:
   # ...

to get a cookie.

nagiosplugin.logtail

LogTail example

Calls process() for each new line in a log file:

cookie = nagiosplugin.Cookie(self.statefile)
with nagiosplugin.LogTail(self.logfile, cookie) as newlines:
   for line in newlines:
      process(line.decode())

Contributing to Nagiosplugin

Getting the source

The source can be obtained via git from https://github.com/mpounsett/nagiosplugin.git:

git clone https://github.com/mpounsett/nagiosplugin.git

This package supports installation in a virtualenv:

python3 -m venv .
pip install -e .

Making Changes

This project uses the Git-flow workflow, approximately as laid out by Vincent Driessen in 2010. New development should be done in feature branches, which are branched off of the develop branch. PRs should be sent to upstream:develop.

Consider whether your change updates or fixes any existing issues. Include the appropriate “fixes” or “updates” entry in your PR description so that the issue is updated. If your change does not reference an existing issue, consider creating an issue to link it to.

The project uses PEP8 as its style guide. All changes should be checked against PEP8 before committing, and commits MUST conform to PEP8 before sending a PR. PEP8 tests can be run with the tox -e flake8 command (see Tests below for details on setting up the tox environment). PRs that fail PEP8 compliance will be refused.

Note that, at present, much of the old codebase gets warnings related to the pylint and pydocstyle tests. Your changes must not introduce any new warnings from these tests.

If your change is a new feature, or otherwise alters the behaviour of nagiosplugin, update the relevant section of the documentation and include that in your PR.

Tests

nagiosplugin tests are run by tox, which is configured to expect all of the supported python versions to be present. The easiest way to accomplish this is by installing and using pyenv.

Once you have pyenv set up, make sure you have each of the supported versions of python specified by the envlist in tox.ini. This will likely look something like:

pyenv install 2.7.18
pyenv install 3.4.10
pyenv install 3.5.10
pyenv install 3.6.15
pyenv install 3.7.12
pyenv install 3.8.12
pyenv install 3.9.9
pyenv install 3.10.1
pyenv global 3.10.1 3.9.9 3.8.12 3.7.12 3.6.15 3.5.10 3.4.10 2.7.18 system

Install test dependencies:

pip install -r requirements_test.txt

After doing so, run the unit tests:

tox

To limit tests to a particular python environment:

tox -e py37

Run only PEP8 linting tests:

tox -e flake8

nagiosplugin also includes support for test coverage reports. Coverage reports are updated automatically by tox. Open htmlcov/index.html to see coverage reports.

You may run the supplied examples with the local interpreter:

python3 nagiosplugin/examples/check_load.py

Documentation

The documentation depends on Sphinx. Install the necessary dependencies, and then build the documentation:

pip install -r requirements_docs.txt
make docs

HTML documentation will be built and installed in doc/_build/html/. You can read the documentation by opening doc/_build/html/index.html.

Releasing

This information will be unnecessary for most contributors. It is only relevant to those actually making releases.

Versioning

nagiosplugin obeys the semantic version numbering specification published on SemVer, adapted slightly to be PEP 440 compliant.

How to release

Instructions below are for a hypothetical 0.1.2 release. Make sure you use the correct version numbers for your release, and don’t copy and paste the below examples.

Begin by making sure you have the build prerequisites installed:

pip install -r requirements_build.txt

Create a release branch from develop:

git checkout develop
git checkout -b release/0.1.2

Check that all tests pass. Apply hotfixes as necessary to pass all tests before continuing.

Update the version number in nagiosplugin/version.py, and update the version release date in the HISTORY.txt file:

sed -i '' -e 's/\(__VERSION__ =\).*/\1 "0.1.2"/' nagiosplugin/version.py
sed -i '' -e 's/0.1.2 (unreleased)/0.1.2 (2019-11-07)/' HISTORY.txt

You may need to update the HISTORY.txt file with additional changes. You can get a list of commits since the last release by generating a reverse log, which you can edit down to just a list of relevant changes:

git log --reverse --no-merges 0.1.1... > new-changes.txt

Commit the updated history and version files, making sure both of the file changes are in the same commit. For a new version 0.1.2:

git stage HISTORY.txt nagiosplugin/version.py
git commit -m "Preparing release 0.1.2"

Merge the release into the main branch and tag the release:

git checkout main
git merge release/0.1.2
git tag 0.1.2
git push
git push --tags

Build the nagiosplugin distribution for PyPi:

python3 setup.py sdist bdist_wheel

Check the contents of the packages in dist/ to ensure they contain all of the expected files.

Test your package prior to uploading:

twine check dist/dist/nagiosplugin-0.1.2.tar.gz

Do a test upload with TestPyPi:

twine upload --repository-url https://test.pypi.org/legacy/ dist/*

Check on https://test.pypi.org/nagiosplugin that the package metadata looks correct. If everything is fine, upload the release:

twine upload dist/*

Merge the release back into develop and then delete the release branch:

git checkout develop
git merge release/0.1.2
git push
git branch -d release/0.1.2

Go to https://readthedocs.io/ and ensure the new stable and dev releases are available.

Glossary

acquisition
First step of check execution in the context of the nagiosplugin library. Data is retrieved from the system under surveillance using custom code. This is where the meat of a plugin is. Data acquisition is performed by one or more domain model objects which are usually Resource subclasses.
domain model
One or more classes that abstract the properties of the system under surveillance that are relevant for the check. The domain model code should not be interspersed with secondary aspects like data representation or interfacing with outside monitoring infrastructure.
evaluation
Second step of check execution in the context of the nagiosplugin library. Data generated in the acquisition step is evaluated according to criteria specified in Context objects.
Nagios plugin API
Documents that define how a Nagios/Icinga compatible plugin must be called and how it should respond. There is a main document and an appendix for Nagios 3 extensions.
perfdata
See performance data.
performance data
Part of the plugin output which is passed to external programs by Nagios.
presentation
Third step of check execution in the context of the nagiosplugin library. Outcomes from the evaluation step are condensed into a compact summary which is suited to inform the admin about relevant system state. Data presentation is the responsibility of Summary objects which also generate the performance data output section.
range
String notation defined in the Nagios plugin API to express a set of acceptable values. Values outside a range trigger a warning or critical condition.
unit of measure
Property of a metric which is returned in Performance Data and is used for example as axis label in performance graphs. Nagios plugins should only use base units like s, B, etc. instead of scaled units like days, MiB etc.
uom
See Unit of Measure.

Release History

1.3.3 (2022-02-07)

  • new: adding support for newer python releases (3.8, 3.9, 3.10)
  • fix: various typos in docstrings (#39, )
  • fix: change use of deprecated upload-dir to upload_dir in setup.cfg (#34)
  • fix: Runtime class now sets None for stdout by default (#24)
  • fix: corrected argument order in several calls to super() (#18)
  • cleanup: many bits of code tidying to eliminate linter warnings

1.3.2 (2019-11-09)

  • Include doc and tests directories in source distribution to support Gentoo package building tests (#22)
  • Update official python support to 2.7, 3.4+ in README

1.3.1 (2019-11-08)

  • Fixed a packaging bug

1.3.0 (2019-11-08)

  • New maintainer/contributor information and project home
  • Updated tests and package metadata for recent Python 3 versions
  • Newer tooling for tests/documentation

1.2.4 (2016-03-12)

  • Add optional keyword parameter verbose to Runtime.guarded(). This parameter allows to set verbose level in the early execution phase (#13).
  • Allow Context.evaluate() return either a Result or ServiceState object. In case the latter is returned, it gets automatically wrapped in a Result object (#6).

1.2.3 (2015-10-30)

  • Fix bug that caused a UnicodeDecodeError when using non-ASCII characters in fmt_metric (#12).
  • Print perfdata always on a single line (even in multi-line mode) to improve compatibility with various monitoring systems (#11).

1.2.2 (2014-05-27)

  • Mention that nagiosplugin also runs with Python 3.4 (no code changes necessary).
  • Make name prefix in status output optional by allowing to assign None to Check.name.
  • Accept bare metric as return value from Resource.probe().
  • Fix bug where Context.describe() was not used to obtain metric description (#13162).

1.2.1 (2014-03-19)

  • Fix build failures with LANG=C (#13140).
  • Remove length limitation of perfdata labels (#13214).
  • Fix formatting of large integers as Metric values (#13287).
  • Range: allow simple numerals as argument to Range() (#12658).
  • Cookie: allow for empty state file specification (#12788).

1.2 (2013-11-08)

  • New Summary.empty method is called if there are no results present (#11593).
  • Improve range violation wording (#11597).
  • Ensure that nagiosplugin install correctly with current setuptools (#12660).
  • Behave and do not attach anything to the root logger.
  • Add debugging topic guide. Explain how to disable the timeout when using pdb (#11592).

1.1 (2013-06-19)

  • Identical to 1.1b1.

1.1b1 (2013-05-28)

  • Made compatible with Python 2.6 (#12297).
  • Tutorial #3: check_users (#11539).
  • Minor documentation improvements.

1.0.0 (2013-02-05)

  • LogTail returns lines as byte strings in Python 3 to avoid codec issues (#11564).
  • LogTail gives a line-based iterator instead of a file object (#11564).
  • Basic API docs for the most important classes (#11612).
  • Made compatible with Python 2.7 (#11533).
  • Made compatible with Python 3.3.

1.0.0b1 (2012-10-29)

  • Improve error reporting for missing contexts.
  • Exit with code 3 if no metrics have been generated.
  • Improve default Summary.verbose() to list all threshold violations.
  • Move main source repository to https://bitbucket.org/gocept/nagiosplugin/ (#11561).

1.0.0a2 (2012-10-26)

  • API docs for the most important classes (#7939).
  • Added two tutorials (#9425).
  • Fix packaging issues.

1.0.0a1 (2012-10-25)

  • Completely reworked API. The new API is not compatible with the old 0.4 API so you must update your plugins.
  • Python 3 support.
  • The Cookie class is now basically a persistent dict and accepts key/value pairs. Cookie are stored as JSON files by default so they can be inspected by the system administrator (#9400).
  • New LogTail class which provides convenient access to constantly growing log files which are eventually rotated.

0.4.5 (2012-06-18)

  • Windows port. nagiosplugin code now runs under pywin32 (#10899).
  • Include examples in egg release (#9901).

0.4.4 (2011-07-18)

Bugfix release to fix issues reported by users.

  • Improve Mac OS X compatibility (#8755).
  • Include examples in distribution (#8555).

0.4.3 (2010-12-17)

  • Change __str__ representation of large numbers to avoid scientific notation.

0.4.2 (2010-10-11)

  • Packaging issues.

0.4.1 (2010-09-21)

  • Fix distribution to install correctly.
  • Documentation: tutorial and topic guides.

0.4 (2010-08-17)

  • Initial public release.

Indices and tables

To download the package, see the PyPI page.