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        "author": "Adam Kariv",
        "author_email": "adam.kariv@gmail.com",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.6",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "# kvfile\n\n[![Travis](https://img.shields.io/travis/akariv/kvfile/master.svg)](https://travis-ci.org/akariv/kvfile)\n[![Coveralls](http://img.shields.io/coveralls/akariv/kvfile.svg?branch=master)](https://coveralls.io/r/akariv/kvfile?branch=master)\n\nA simple Key-Value store that's file based - so can accommodate large data sets with a small memory footprint.\n\nInternally will use the faster `leveldb` as a storage backend or `sqlite3` as fallback if `leveldb` is not available.\n\n## The Basics\n\nThe API should feel familiar to anyone working with Python.\nIt exposes `get`, `keys` and `items` for reading from the DB, and `set` for setting a value in the DB.\n\n### Initializing\n\n```python\nimport datetime\nimport decimal\n\nfrom kvfile import KVFile\n\nkv = KVFile()\n```\n\n### Setting values\n\n```python\nkv.set('s', 'value')\nkv.set('i', 123)\nkv.set('d', datetime.datetime.fromtimestamp(12325))\nkv.set('n', decimal.Decimal('1234.56'))\nkv.set('ss', set(range(10)))\nkv.set('o', dict(d=decimal.Decimal('1234.58'), \n                 n=datetime.datetime.fromtimestamp(12325)))\n```\n\n### Getting values\n\n```python\nassert kv.get('s') == 'value'\nassert kv.get('i') == 123\nassert kv.get('d') == datetime.datetime.fromtimestamp(12325)\nassert kv.get('n') == decimal.Decimal('1234.56')\nassert kv.get('ss') == set(range(10))\nassert kv.get('o') == dict(d=decimal.Decimal('1234.58'), \n                           n=datetime.datetime.fromtimestamp(12325))\n```\n\n### Listing values\n\n`keys()` and `items()` methods return a generator yielding the values for efficient stream processing.\n\nThe returned data is sorted ascending (by default) based on the keys\n\n```python\nassert list(kv.keys()) == ['d', 'i', 'n', 'o', 's', 'ss']\nassert list(kv.items()) == [\n  ('d', datetime.datetime.fromtimestamp(12325)), \n  ('i', 123), \n  ('n', decimal.Decimal('1234.56')), \n  ('o', {'d': decimal.Decimal('1234.58'), \n         'n': datetime.datetime.fromtimestamp(12325)}), \n  ('s', 'value'), \n  ('ss', {0, 1, 2, 3, 4, 5, 6, 7, 8, 9})\n]\n```\n\nSet the `reverse` argument to True for the `keys()` and `items()` methods to sort in descending order.\n\n### Bulk inserting data\n\nThe SQLite DB backend can be very slow when bulk inserting data. You can use the insert method to insert efficiently in bulk.\n\n```python\nkv.insert(((str(i), ':{}'.format(i)) for i in range(50000)))\n```\n\nThe batch size is 1000 by default, you should modify it depending on the size of your data and available memory.\n\n```python\nkv.insert(((str(i), ':{}'.format(i)) for i in range(50000)), batch_size=40000)\n```\n\nIf you are inserting data from a generator and need to use the inserted data, use `insert_generator` method:\n\n```python\nfor key, value in kv.insert_generator(((str(i), ':{}'.format(i)) for i in range(50)), batch_size=10):\n    print(key, value)\n```\n\n## Installing leveldb\n\nOn Debian based Linux:\n```bash\n$ apt-get install libleveldb-dev libleveldb1\n```\n\nOn Alpine based Linux:\n```bash\n$ apk --repository http://dl-3.alpinelinux.org/alpine/edge/testing/ --update add leveldb leveldb-dev\n```\n\nOn OS X:\n```bash\n$ brew install leveldb\n```\n\n",
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