{
    "info": {
        "author": "Tommy MacWilliam",
        "author_email": "tmacwilliam@cs.harvard.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3 :: Only"
        ],
        "description": "Rupee: Python [STRIKEOUT:Cashing] Caching\n=========================================\n\nRupee is a simple, but fully-featured caching library for Python 3.\n\nEngines\n-------\n\nRupee supports caching using process memory, Redis, and Memcached.\n\nFor Redis support:\n\n::\n\n    pip install redis\n\nFor Memcached support, you can use ``pylibmc``:\n\n::\n\n    pip install pylibmc\n\nor ``python-memcache``:\n\n::\n\n    pip install python-memcache\n\nCache Access\n------------\n\nYou can create cache instances like this:\n\n::\n\n    memory = rupee.engine.Memory()\n    memcached = rupee.engine.Memcached(['localhost:11211'])\n    redis = rupee.engine.Redis('localhost:6379')\n\nAll instances conform to the same API, which offer the get/set/delete\noperations you'd expect:\n\n::\n\n    cache = rupee.engine.Memcached(['localhost:11211'])\n    cache.set('foo', 'bar', ttl=3600)\n    cache.set_multi({'baz': 1, 'qux': 2})\n    cache.get('baz') == 1\n    cache.get_multi(['foo', 'qux']) == {'foo': 'bar', 'qux': 2}\n    cache.delete('qux')\n    cache.delete_all(['foo', 'baz'])\n    cache.delete_all_data()\n\nCached Decorators\n-----------------\n\nYou can decorate functions to be cache their results:\n\n::\n\n    cache = rupee.engine.Redis('localhost:6379')\n\n    @rupee.cached(cache, ttl=3600)\n    def foo(bar, baz):\n        return _some_expensive_thing(bar, baz)\n\nTo clear the cache entry for a function call:\n\n::\n\n    foo.dirty(1, 2)\n\nFor functions that perform bulk operations, you can use the multi-cache\ndecorator:\n\n::\n\n    @rupee.multi_cached(cache):\n    def get(items):\n        return {item: _some_expensive_thing(item) for item in items}\n\nFunctions decorated with ``multi_cached`` must take a single list as an\nargument and return a dictionary keyed on the items in that list. Then,\nresults for each item will be cached separately, and only the needed\nitems will be passed to the function. To illustrate:\n\n::\n\n    get([1, 2, 3]) # calls _some_expensive_thing on 1, 2, and 3\n    get([1, 2, 3]) # _some_expensive_thing is never called\n    get([2, 3, 4]) # calls _some_expensive_thing only on 4",
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        "keywords": "cache caching redis memcache memcached",
        "license": "MIT",
        "maintainer": "",
        "maintainer_email": "",
        "name": "rupee",
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        "summary": "A fully-featured cache for Python applications.",
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