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        "author": "Anish Gupta",
        "author_email": "nkanish2002@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.5",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "pyredis-cache\n=============\n\nPython Cache client for redis.\n\nSupports Python 3.5 and above.\n\nFeatures\n========\n\n-  Automatically Sync data between the sync\\_func and cache\n-  Use of Async/Await for Asynchronous execution of sync\\_func\n\nDependencies\n============\n\n-  `redis-py <https://github.com/andymccurdy/redis-py>`__\n-  `hiredis-py <https://github.com/redis/hiredis-py>`__\n\nInstallation\n============\n\n1. Through pip: ``pip install pyredis-cache``\n\n2. Manually: ``python setup.py install``. Install dependencies first\n\nGetting Started\n===============\n\n-  Simple Redis Cache\n\n.. code:: py\n\n    # Import redis Connection pool and CacheClient.\n    from redis import ConnectionPool\n    from redis_cache import CacheClient\n\n    import json\n\n    # Initialize the Connection pool.\n    redis_pool = ConnectionPool()\n\n    # Create a data synchronisation function\n    # The first argument should define the uniqueness of the data\n    def sync_func(student_id, lots_of_args):\n        # fetch the data however you want to\n        return data # Lets assume that the data returned is of type `dict` \n     \n    # Initialize the cache client\n    cache = CacheClient(redis_pool, \"Student\", sync_func, set_func=json.dumps, get_func=json.loads, expire_time=10)\n    # for persistent cache, assign 0 to expire_time (default)\n\n    # This will set cache for ID: 12\n    cache.set(12)\n\n    # This will set cache for ID: 13, with custom data\n    cache.set(13, data={\"name\":\"John Doe\", \"age\":16})\n\n    # This will get cache for ID: 12 (no need to do a set, it will automatically set the data)\n    cache.get(12)\n\n    # The get function will pass extra arguments to the sync_func, same works with set.\n    cache.get(14, age=15)\n\n    # This will delete the cache for ID: 12\n    cache.delete(12)\n\n-  Caching into a single hash. I am still working on this to make a much\n   more flexible caching mechanism\n\n.. code:: py\n\n    # Import redis Connection pool and HashCacheClient.\n    from redis import ConnectionPool\n    from redis_cache import CacheClient\n\n    import json\n\n    # Initialize the Connection pool.\n    redis_pool = ConnectionPool()\n\n    # Create a data synchronisation function\n    # The first argument should define the uniqueness of the data\n    def sync_func(student_id, lots_of_args):\n        # fetch the data however you want to\n        return data # Lets assume that the data returned is of type `dict` \n     \n    # Initialize the cache client.\n    hcache = HashCacheClient(redis_pool, \"Class\", \"Student\", sync_func, set_func=dumps, get_func=loads)\n    # expire_time not supported.\n\n    # This will set cache for ID: 12 for hash_id: 3\n    hcache.set(3, 12)\n\n    # This will get cache for ID: 12 for hash_id: 3 (no need to do a set, it will automatically set the data)\n    hcache.get(3, 12)\n\n    # This will delete the cache for ID: 12 for hash_id: 3\n    hcache.delete(3, 12)\n\n    # This will delete the hash with hash_id: 3\n    hcache.delete_hash(3)\n\nTerminology\n===========\n\n-  key: is the identifier for the cache object. eg. ``student`` (type\n   string)\n-  identity: is the index for a particular key. eg. Roll no. of a\n   student ``12`` (type int)\n-  sync\\_func: function which will give data tto be stores in cache.\n-  set\\_func: data parsing function for setting data into cache. eg. if\n   you want to set a dict value, you will have to set this as\n   ``json.dumps``\n-  get\\_func: data parsing function for getting data into cache. eg. if\n   you want to set a dict value, you will have to set this as\n   ``json.loads``\n-  hash\\_key and hash\\_id: Unique identifiers for the hash. eg.\n   hash\\_key can be ``grade12`` and hash\\_id can be ``2``\n\nKey and Identity are combined in a unique fashion to make the name\nagainst which the cache object will be stored. Similarly for hash\\_key\nand hash\\_id.\n\nAsync\n=====\n\nIf your function is Async i.e. makes use of ``async`` and ``await``\ncoroutines (`PEP 492 <https://www.python.org/dev/peps/pep-0492/>`__).\nYou will have to use event loop to run use this functionality like\nasyncio and tornado. You could do the following:-\n\n.. code:: py\n\n    cache = CacheClient(redis_pool, \"Student\", sync_func, set_func=json.dumps, get_func=json.loads, expire_time=10, asynchronous=True)\n\n    async def some_func():\n        data = await cache.get(23)\n\nNote: the cache client is synchronous because redis servers are quite\nfast. You can expect near 0 latency with the correct setup. Also the\ncreator of tornado-redis suggests this kind if architecture to reduce\nthe cost overhead caused by setting up callbacks. (see post:\nhttp://stackoverflow.com/a/15596969/2248966)\n",
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