python-panopticon 0.1.1

Copyright (c) 2016 Mobify Research & Develpment Inc.

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Description: python-panopticon
#################


.. image:: https://travis-ci.org/mobify/python-panopticon.svg?branch=master
:target: https://travis-ci.org/mobify/python-panopticon

Panopticon is a collection of health check and monitoring helpers that we use
at `Mobify <https://mobify.com>`_ for our services.


Installation
------------

The easiest way to install it is from PyPI::

$ pip install python-panopticon


You can also install it straight from the repo::

$ pip install https://github.com/mobify/python-panopticon/archive/master.zip


Setup with Django
-----------------

panopticon comes with a Django integration app that simplifies the setup. Make
sure you have the ``python-panopticon`` package installed.

Add the ``panopticon.django`` app into you ``INSTALLED_APPS`` settings and
configure the API key for Datadog by specifying ``DATADOG_API_KEY`` in your
settings. You are all done!

If you want your healthcheck to be automatically exposed on ``/healthcheck/`` you
can simply add the following line to your main project ``urls.py``:

.. code:: python

#urls.py
urlpatterns = [
...

url(r'', include('panopticon.urls', namespace='panopticon')),
]

Using this view at this point requires ``django-rest-framework`` (DRF) to be
installed as a dependency. We'll probably changes it in the future but for now,
we are using DRF in our projects and it provides some additional features.

If you don't hook up ``panopticon.urls``, you can simply build your own view and
ignore this dependency.


Available Settings
------------------

* ``DATADOG_STATS_ENABLED`` : Enables or disables the Datadog wrapper in
panopticon. If you disable panopticon, it'll use a ``mock.Mock`` object as
the stats client. It is disabled by default.
* ``DATADOG_STATS_PREFIX`` : The prefix used for **all** Datadog metrics when
submitted to the Datadog API. The default is ``panopticon``.


Adding a custom healthcheck in Django
-------------------------------------

If you are using the Django app to integrate it with Django, adding new health
checks is easy. Every application in ``INSTALLED_APPS`` will be checked for a
``healthchecks.py`` module on startup. Loading each of these modules will
automatically register all health checks in that module. This is similar to how
``models.py`` and ``tasks.py`` (Celery) work.

Let's assume we have a ``monitoring`` Django app that should contain some simple
health checks. The first thing to do is creating a ``healthchecks.py`` file.
Within this file, we can now create a simple function that test the database
connection. All we have to do to hook it up is register it as a health check
and provide details about its success:

.. code:: python

from django.db import connection, DatabaseError

@HealthCheck.register_healthcheck
def database(data):
cursor = connection.cursor()

healthy = True
status = 'database is available.'

try:
cursor.execute('SELECT 1;')
except DatabaseError as exc:
status = 'error connecting to the database: {}'.format(str(exc))
finally:
cursor.close()

data[HealthCheck.HEALTHY] = healthy
data[HealthCheck.STATUS_MESSAGE] = status

return data

The name of the function, i.e. ``database`` in this case, will be used as the
component name for the health check result as defined in the response format
below.


The Response Format
-------------------

The health check format that we use makes sure that all health checks return an
agreed upon JSON response. This ensure that certain properties are always
present and can be relied upon for external processing, e.g. ``service_healthy``,
``timestamp``, ``components`` and ``healthy`` within each of the components.

.. code:: javascript
{
// This represents the overall health of the service
// If all of the components are healthy this should be true, false otherwise.
"service_healthy": true,

// The instant when the response was generated. This is useful to determine
// if the health check response is up to date or stale, for example because it
// was cached. This is in ISO8601 format.
"timestamp": "2014-09-03T23:09:38.702Z",

// We also expose the health status for each internal component
// of the service. Besides a “healthy” flag this can also include
// metadata like the number of queued jobs or average processing times.
// We expose this information in a list so that monitoring tools can parse
// and visualize this information easily.
"components": {
"database": {
"healthy": true,
"response_time": 0.00123,
"friendly_status": "The database is working awesomely great!"
},
"background_jobs": {
"healthy": true,
"response_time": 0.00123,
"queued_jobs": 423
}
}
}


Creating a Release
------------------

Creating a new release is simple using `bumpversion
<https://github.com/peritus/bumpversion>`_ which ensures that naming tags and
updating *all* version numbers in the Python code is ensured. To create a new
version specify the type of version bump (either major, minor or patch) and
bumpversion will do the rest. For a patch it looks like this::

$ bumpversion patch

This will create a new commit with the bumped version as well as a new tag.
Make sure that you push both the commit and the tag up.


License
-------

This code is licensed under the `MIT License`_.

.. _`MIT License`: https://github.com/mobify/python-panopticon/blob/master/LICENSE

Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: Implementation :: CPython