Metadata-Version: 1.1
Name: django-elasticache
Version: 1.0.3
Summary: Django cache backend for Amazon ElastiCache (memcached)
Home-page: http://github.com/gusdan/django-elasticache
Author: Danil Gusev
Author-email: danil.gusev@gmail.com
License: MIT
Description: Amazon ElastiCache backend for Django
        =====================================
        
        Simple Django cache backend for Amazon ElastiCache (memcached based). It uses
        `pylibmc <http://github.com/lericson/pylibmc>`_ and sets up a connection to each
        node in the cluster using
        `auto discovery <http://docs.aws.amazon.com/AmazonElastiCache/latest/UserGuide/AutoDiscovery.html>`_.
        
        
        Requirements
        ------------
        
        * pylibmc
        * Django 1.5+.
        
        It was written and tested on Python 2.7 and 3.4.
        
        Installation
        ------------
        
        Get it from `pypi <http://pypi.python.org/pypi/django-elasticache>`_::
        
            pip install django-elasticache
        
        or `github <http://github.com/gusdan/django-elasticache>`_::
        
            pip install -e git://github.com/gusdan/django-elasticache.git#egg=django-elasticache
        
        
        Usage
        -----
        
        Your cache backend should look something like this::
        
            CACHES = {
                'default': {
                    'BACKEND': 'django_elasticache.memcached.ElastiCache',
                    'LOCATION': 'cache-c.draaaf.cfg.use1.cache.amazonaws.com:11211',
                    'OPTIONS' {
                        'IGNORE_CLUSTER_ERRORS': [True,False],
                    },
                }
            }
        
        By the first call to cache it connects to cluster (using ``LOCATION`` param),
        gets list of all nodes and setup pylibmc client using full
        list of nodes. As result your cache will work with all nodes in cluster and
        automatically detect new nodes in cluster. List of nodes are stored in class-level
        cached, so any changes in cluster take affect only after restart of working process.
        But if you're using gunicorn or mod_wsgi you usually have max_request settings which
        restart process after some count of processed requests, so auto discovery will work
        fine.
        
        The ``IGNORE_CLUSTER_ERRORS`` option is useful when ``LOCATION`` doesn't have support
        for ``config get cluster``. When set to ``True``, and ``config get cluster`` fails,
        it returns a list of a single node with the same endpoint supplied to ``LOCATION``.
        
        Django-elasticache changes default pylibmc params to increase performance.
        
        Another solutions
        -----------------
        
        ElastiCache provides memcached interface so there are three solution of using it:
        
        1. Memcached configured with location = Configuration Endpoint
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        In this case your application
        will randomly connect to nodes in cluster and cache will be used with not optimal
        way. At some moment you will be connected to first node and set item. Minute later
        you will be connected to another node and will not able to get this item.
        
         ::
        
            CACHES = {
                'default': {
                    'BACKEND': 'django.core.cache.backends.memcached.PyLibMCCache',
                    'LOCATION': 'cache.gasdbp.cfg.use1.cache.amazonaws.com:11211',
                }
            }
        
        
        2. Memcached configured with all nodes
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        It will work fine, memcache client will
        separate items between all nodes and will balance loading on client side. You will
        have problems only after adding new nodes or delete old nodes. In this case you should
        add new nodes manually and don't forget update your app after all changes on AWS.
        
         ::
        
            CACHES = {
                'default': {
                    'BACKEND': 'django.core.cache.backends.memcached.PyLibMCCache',
                    'LOCATION': [
                        'cache.gqasdbp.0001.use1.cache.amazonaws.com:11211',
                        'cache.gqasdbp.0002.use1.cache.amazonaws.com:11211',
                    ]
                }
            }
        
        
        3. Use django-elasticache
        ~~~~~~~~~~~~~~~~~~~~~~~~~
        
        It will connect to cluster and retrieve ip addresses
        of all nodes and configure memcached to use all nodes.
        
         ::
        
            CACHES = {
                'default': {
                    'BACKEND': 'django_elasticache.memcached.ElastiCache',
                    'LOCATION': 'cache-c.draaaf.cfg.use1.cache.amazonaws.com:11211',
                }
            }
        
        
        Difference between setup with nodes list (django-elasticache) and
        connection to only one configuration Endpoint (using dns routing) you can see on
        this graph:
        
        .. image:: https://raw.github.com/gusdan/django-elasticache/master/docs/images/get%20operation%20in%20cluster.png
        
        Testing
        -------
        
        Run the tests like this::
        
            nosetests
        
Keywords: elasticache amazon cache pylibmc memcached aws
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Web Environment
Classifier: Environment :: Web Environment :: Mozilla
Classifier: Framework :: Django
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development :: Libraries :: Python Modules
