Metadata-Version: 1.1
Name: python-panopticon
Version: 0.2.3
Summary: A collection of healthcheck and monitoring helpers.
Home-page: https://python-panopticon.readthedocs.org
Author: Mobify Research & Develpment Inc.
Author-email: ops@mobify.com
License: MIT
Description: python-panopticon
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        .. 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
                    }
                }
            }
        
        
        Setup Development
        -----------------
        
        The development setup is using `tox <https://tox.readthedocs.io/en/latest>`_
        for testing against various versions of Python. Running tox tests is quit
        simple for a given Python version that you have installed locally. For instance
        running tox with Python 2.7.10::
        
            $ pip install tox
            $ tox -e py27
        
        or for Python 3.5::
        
            $ tox -e py35
        
        
        If you prefer to install and run the tests inside a virtualenv, you can install
        all the test and release requirements inside a virtualenv using::
        
            $ pip install -e ".[test]"
            $ pip install -e ".[dev]"
        
        
        Creating a Release
        ------------------
        
        Creating a new release is simple. We use `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 do the following:
        
        * Change all version strings defined in ``bumpversion.cfg``.
        * Create a new commit.
        * Create a new tag with the given version number.
           
        **Note** A new release should only be created on the ``master`` branch after
        one or more changes have been merged and tested.
        
        After releasing a new version, the commit and tag have to be pushed to github::
        
            $ git push 
            $ git push --tags
        
        You can now release this version to PyPI using the Makefile. This requires
        the password for the ``mobify`` user to be exported in your shell. You can find
        it in LastPass::
        
            $ PYPI_PASSWORD=<the password> make release
        
        
        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
