@@ -4,7 +4,7 @@ Amazon ElastiCache backend for Django
44Simple Django cache backend for Amazon ElastiCache (memcached based). It uses
55`pylibmc <http://github.com/lericson/pylibmc >`_ and setup connection to each
66node in cluster using
7- `Auto Discovery <http://docs.aws.amazon.com/AmazonElastiCache/latest/UserGuide/AutoDiscovery.html >`_
7+ `auto discovery <http://docs.aws.amazon.com/AmazonElastiCache/latest/UserGuide/AutoDiscovery.html >`_
88function.
99
1010
@@ -35,25 +35,26 @@ Your cache backend should look something like this::
3535
3636 CACHES = {
3737 'default': {
38- 'BACKEND': 'django_elasticache.memcached.ElasctiCache ',
38+ 'BACKEND': 'django_elasticache.memcached.ElastiCache ',
3939 'LOCATION': 'cache-c.drtgf.cfg.use1.cache.amazonaws.com:11211',
4040 }
4141 }
4242
43- By the first call to cache it connect to cluster (using LOCATION),
44- get list of all nodes and setup pylibmc client using full
45- list of nodes. As result your cache will work with all nodes and
43+ By the first call to cache it connects to cluster (using LOCATION param ),
44+ gets list of all nodes and setup pylibmc client using full
45+ list of nodes. As result your cache will work with all nodes in cluster and
4646automatically detect new nodes in cluster. List of nodes are stored in class-level
47- cached, so any changes in cluster take affect only after restart working process.
47+ cached, so any changes in cluster take affect only after restart of working process.
4848But if you're using gunicorn or mod_wsgi you usually have max_request settings which
49- restart process after some count of processed requests.
49+ restart process after some count of processed requests, so auto discovery will work
50+ fine.
5051
51- Django-elascticache changes default pylibmc params to increase performance.
52+ Django-elasticache changes default pylibmc params to increase performance.
5253
5354
5455Testing
5556-------
5657
5758Run the tests like this::
5859
59- nosetest
60+ nosetest
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