{
    "info": {
        "author": "Gabriela Surita",
        "author_email": "gsurita@loggi.com",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Developers",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6"
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        "description": "Redis Schematics\n================\n\n*Provides Redis persistence to Schematics models with cutomizable abstraction levels.*\n\n|travis|\n\n.. |travis| image:: https://travis-ci.org/loggi/redis-schematics.svg?branch=master\n    :target: https://travis-ci.org/loggi/redis-schematics\n\n\nInstalling\n----------\n\nUsing pip::\n\n    pip install redis_schematics\n\n\nUnderstanding Persistence layers\n--------------------------------\n\nThere are several ways to implement complex objects persitence on a key-value-set\ndatabase such as redis. The best way to do it depends on your application constraints.\nWe think that providing a good storage model for your application is to allow you\nto choose which abstraction you want to use. Below you can find a comparison on different\nprovided abstraction layers.\n\n*Currently we only support a SimpleRedisMixin and SimpleRedisModel, but you can\nuse BaseRedisMixin to build your own persistance layers.*\n\n\n**SimpleRedisMixin**\n\nAdd Redis persistance to an object using a simple approach. Each object\ncorrenspond to a single key on redis prefixed with the object namespace,\nwhich correnponds to a serialized object. To use this mixin you just need\nto declare a primary key as on the example below.\n\nYou may use this Mixin when you have frequent matches on primary key and set\noperations, unique expires, hard memory contraints or just wants a 1-1 object-key\napproach. You may not use this Mixin if you need performance on ``filter``, ``all``\nand ``get`` on non primary key operations.\n\n**HashRedisMixin**\n\nAdd Redis persistance to an object using a single hash approach. Each type\ncorrenspond to a single key on redis containing a hash set with every instance\nas an entry on the set which contains a serialized object.\n\nYou may use this Mixin when you have frequent matches on primary key, set and\nall operations, hard memory contraints or wants a single key approach.\nYou may not use this Mixin if you need performance on ``filter`` and ``get`` on\nnon primary key operations.\n\n\nQuickstart\n----------\n\n**Creating models with persistence**\n\nNote: you should include a pk, but don't bother setting it's value manually.\nWe can infer it from an ``id`` field or by setting a tuple of field names using\n``__unique_together__``.\n\n\n.. code-block:: python\n\n    from datetime import datetime, timedelta\n\n    from redis import StrictRedis\n    from redis_schematics import SimpleRedisMixin\n    from schematics import models, types\n\n\n    class IceCreamModel(models.Model, SimpleRedisMixin):\n        pk = types.StringType()  # Just include a pk\n        id = types.StringType()\n        flavour = types.StringType()\n        amount_kg = types.IntType()\n        best_before = types.DateTimeType()\n\n\n**Setting on Redis**\n\nSaving is simple as ``set()``.\n\n.. code-block:: python\n\n    vanilla = IceCreamModel(dict(\n        id='vanilla',\n        flavour='Sweet Vanilla',\n        amount_kg=42,\n        best_before=datetime.now() + timedelta(days=2),\n    ))\n\n   chocolate = IceCreamModel(dict(\n        id='chocolate',\n        flavour='Delicious Chocolate',\n        amount_kg=12,\n        best_before=datetime.now() + timedelta(days=3),\n    ))\n\n    vanilla.set()\n    chocolate.set()\n\n**Getting from Redis**\n\nThere are two basic ways to get an element from Redis: by pk or by value.\nYou can use the classmethods ``match_for_pk(pk)`` or ``match_for_values(**Kwargs)``\nor just simply ``match(**kwargs)`` to let us choose which one. Notice that the\nperformance from both methods is a lot different, so you may avoid matching\nfor values on high performance environments. You may also use refresh to reload\nan object from storage if it has been modified.\n\n.. code-block:: python\n\n    IceCreamModel.match_for_pk('vanilla')\n    IceCreamModel.match_for_values(amount__gte=30)\n\n    IceCreamModel.match(id='vanilla')  # match on pk\n    IceCreamModel.match(best_before__gte=datetime.now())  # match on values\n\n    vanilla.refresh()\n\n\n**Fetching all and filtering**\n\nYou can also use ``all()`` to deserialize all and filters. Notice that\nthis invlolves deserializing all stored objects.\n\n.. code-block:: python\n\n    IceCreamModel.all()\n    IceCreamModel.filter(amount__gte=30)\n\n\n**Deleting and expiring**\n\nTo remove objects, you can set ``__expire__`` or use the ``delete()`` method.\nNotice that expires work differently on single key and multiple keys approaches.\n\n.. code-block:: python\n\n    class MyVolatileModel(models.Model, SimpleRedisMixin):\n        __expire__ = 3600  # model expire (in seconds)\n        pk = types.StringType()\n\n    vanilla.delete()\n\n\nJSON\n----\n\nIf you want json serialization, you have at least two options:\n\n1. Patch the default serializer.\n2. Write a custom JSONEncoder.\n\nWe've implemented a handy patch funtion, you need to add this\ncode to somewhere at the top of everything to automagically add\njson serialization capabilities:\n\n.. code:: python\n\n    from redis_schematics.patches import patch_json\n    patch_json()\n\n.. note::\n\n    Eventually ``__json__`` will be added to the stdlib, see\n    https://bugs.python.org/issue27362\n\nRoadmap\n-------\n\nWe are still ``0.x``, but we are very close to a stable API. Check `our roadmap <https://github.com/loggi/redis-schematics/issues/40>`_ for a glance of what's missing.",
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        "license": "Apache License (2.0)",
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