Engines

Rupee supports caching using process memory, Redis, and Memcached.

For Redis support:

pip install redis

For Memcached support, you can use pylibmc:

pip install pylibmc

or python-memcache:

pip install python-memcache

Cache Access

You can create cache instances like this:

memory = rupee.engine.Memory()
memcached = rupee.engine.Memcached(['localhost:11211'])
redis = rupee.engine.Redis('localhost:6379')

All instances conform to the same API, which offer the get/set/delete operations you’d expect:

cache = rupee.engine.Memcached(['localhost:11211'])
cache.set('foo', 'bar', ttl=3600)
cache.set_multi({'baz': 1, 'qux': 2})
cache.get('baz') == 1
cache.get_multi(['foo', 'qux']) == {'foo': 'bar', 'qux': 2}
cache.delete('qux')
cache.delete_all(['foo', 'baz'])
cache.delete_all_data()

Cached Decorators

You can decorate functions to be cache their results:

cache = rupee.engine.Redis('localhost:6379')

@rupee.cached(cache, ttl=3600)
def foo(bar, baz):
    return _some_expensive_thing(bar, baz)

To clear the cache entry for a function call:

foo.dirty(1, 2)

For functions that perform bulk operations, you can use the multi-cache decorator:

@rupee.multi_cached(cache):
def get(items):
    return {item: _some_expensive_thing(item) for item in items}

Functions decorated with multi_cached must take a single list as an argument and return a dictionary keyed on the items in that list. Then, results for each item will be cached separately, and only the needed items will be passed to the function. To illustrate:

get([1, 2, 3]) # calls _some_expensive_thing on 1, 2, and 3
get([1, 2, 3]) # _some_expensive_thing is never called
get([2, 3, 4]) # calls _some_expensive_thing only on 4