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    "info": {
        "author": "Jay Marcyes",
        "author_email": "jay@marcyes.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Environment :: Web Environment",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2.7",
            "Topic :: Database",
            "Topic :: Software Development :: Libraries",
            "Topic :: Utilities"
        ],
        "description": "Caches\n======\n\nA Python caching library that gives a similar interface to standard\nPython data structures like Dict and Set but is backed by redis.\n\nCaches was lovingly crafted for `First\nOpinion <http://firstopinion.co>`__.\n\nHow to use\n----------\n\nCaches can only use `Redis <http://redis.io>`__.\n\nCaches relies on setting the environment variable ``CACHES_DSN``:\n\n::\n\n    caches.interface.Redis://localhost/0\n\nIf you want to cache things using more than one redis server, you can\nactually set multiple environment variables:\n\n::\n\n    export CACHES_DSN_1=caches.interface.Redis://somedomain.com/0#redis1\n    export CACHES_DSN_2=caches.interface.Redis://someotherdomain.com/0#redis2\n\nAfter you've set the environment variable, then you just need to import\ncaches in your code:\n\n.. code:: python\n\n    import caches\n\nCaches will take care of parsing the url and creating the redis\nconnection, automatically, so after the import Caches will be ready to\nuse.\n\nInterface\n~~~~~~~~~\n\nAll caches caching classes have a similar interface, they take the\npassed in constructor ``*args`` and concat them to create a key:\n\n.. code:: python\n\n    c = KeyCache('foo', 'bar', 'che')\n    print c.key # foo.bar.che\n\nIf you would like to init your cache object with a value, use the\n``data`` ``**kwarg``:\n\n.. code:: python\n\n    c = KeyCache('foo', data=\"boom!\")\n    print c.key # foo\n    print c # \"boom!\"\n\nEach caches base caching class is meant to be extended so you can set\nsome parameters:\n\n-  **serialize** -- boolean -- True if you want all values pickled,\n   False if you don't (ie, you're caching ints or strings or something).\n\n-  **prefix** -- string -- This will be prepended to the key args you\n   pass into the constructor.\n\n-  **ttl** -- integer -- time to live, how many seconds to cache the\n   value. Set to like 2 hours by default, 0 means live forevor.\n\n-  **connection\\_name** -- string -- if you have more than one caches\n   dsn then you can use this to set the name of the connection you want\n   (the name of the connection is the ``#connection_name`` fragment of a\n   dsn url).\n\n.. code:: python\n\n    class MyIntCache(KeyCache):\n      serialize = False # don't bother to serialize values since we're storing ints\n      prefix = \"MyIntCache\" # every key will have this prefix, change to invalidate all currently cached values\n      ttl = 7200 # store each int for 2 hours\n\nCache Classes\n~~~~~~~~~~~~~\n\nKeyCache\n^^^^^^^^\n\nThis is the traditional caching object, it sets a value into a key:\n\n.. code:: python\n\n    c = KeyCache('foo')\n    c.data = 5 # cache 5\n    c += 10 # increment 5 by 10, store 15 in the cache\n\n    c.clear()\n    print c # None\n\nDictCache\n^^^^^^^^^\n\nThis caching object acts more or less like a Python\n`dictionary <http://docs.python.org/2/library/stdtypes.html#mapping-types-dict>`__:\n\n.. code:: python\n\n    c = DictCache('foo')\n    c['bar'] = 'b'\n    c['che'] = 'c'\n    for key, val in c.iteritems():\n      print key, val # will print bar b and then che c\n\nSetCache\n^^^^^^^^\n\nThis caching object acts more or less like a Python\n`set <http://docs.python.org/2/library/stdtypes.html#set>`__:\n\n.. code:: python\n\n    c = SetCache('foo')\n    c.add('bar')\n    c.add('che')\n    print 'che' in c # True\n\nSortedSetCache\n^^^^^^^^^^^^^^\n\nThis caching object acts more or less like a Python\n`set <http://docs.python.org/2/library/stdtypes.html#set>`__ but has\nsome changes:\n\n-  The add() method can take a score value\n-  The pop() method will pop off the lowest score from the set, and pops\n   a tuple: (elem, score)\n-  An rpop() method allows you to pop the highest score from the set.\n-  Iterating through the set results in tuples of (elem, score), not\n   just elem like in a normal set or the ``SetCache``.\n-  The chunk(limit, offset) and rchunk(limit, offset) methods will work\n   through sections of the list working either forwards or backwards.\n\n.. code:: python\n\n    c = SortedSetCache('foo')\n    c.add('bar', 1)\n    c.add('che', 10)\n    print 'che' in c # True\n    print c.pop() # (bar, 1)\n\nCounterCache\n^^^^^^^^^^^^\n\nThis caching object acts more or less like a Python\n`collections.Counter <http://docs.python.org/2/library/collections.html#collections.Counter>`__:\n\n.. code:: python\n\n    c = CounterCache('foo')\n    c['bar'] = 5\n    c['bar'] += 5\n\n    print c['bar'] # 10\n\nDecorator\n~~~~~~~~~\n\nCaches exposes a decorator to make caching the return value of a\nfunction easy. This only works for ``KeyCache`` derived caching.\n\nThe ``cached`` decorator can accept a caching class and also a key\nfunction (similar to the python `built-in ``sorted()``\nfunction <http://docs.python.org/2/library/functions.html#sorted>`__ key\nargument), except caches key argument returns a list that can be passed\nto the constructor of the caching class as ``*args``.\n\n.. code:: python\n\n    from caches import KeyCache\n\n    @KeyCache.cached(key=\"some_cache_key\")\n    def foo(*args):\n        return reduce(lambda x, y: x+y, args)\n\n    foo(1, 2) # will compute the value and cache the return value\n    foo(1, 2) # return value from cache\n\n    foo(1, 2, 3) # uh-oh, wrong value, our key was too static\n\nLet's try again, this time with a dynamic key\n\n.. code:: python\n\n    @KeyCache.cached(key=lambda *args: args)\n    def foo(*args):\n        return reduce(lambda x, y: x+y, args)\n\n    foo(1, 2) # compute and cache, key func returned [1, 2]\n    foo(1, 2) # grabbed from cache\n    foo(1, 2, 3) # compute and cache because our key func returned [1, 2, 3]\n\nWhat about custom caches classes?\n\n.. code:: python\n\n    class CustomCache(KeyCache): pass\n\n    @CustomCache.cached(key=lambda *args: args)\n    def foo(*args):\n        return reduce(lambda x, y: x+y, args)\n\nInstall\n-------\n\nUse pip from pypi:\n\n::\n\n    pip install caches\n\nor from source using pip:\n\n::\n\n    pip install git+https://github.com/firstopinion/caches#egg=caches\n\nAcknowledgements\n----------------\n\nCaches uses the very cool `redis\\_collections\nmodule <https://redis-collections.readthedocs.org/en/latest/>`__.\n\nSome of the interface is inspired from a module that `Ryan\nJohnson <https://github.com/bismark>`__ wrote for Undrip.\n\nLicense\n-------\n\nMIT\n\nOther links\n-----------\n\n`Dogpile <http://dogpilecache.readthedocs.org/en/latest/usage.html>`__\n",
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