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        "author": "Andy Gayton",
        "author_email": "andy@thecablelounge.com",
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        "description": "Fity3\n-----\n\nFity3 is a Twitter snowflake like scheme generator that fits in 53 bits.\n\n.. image:: https://travis-ci.org/cablehead/python-fity3.svg?branch=master\n       :target: https://travis-ci.org/cablehead/python-fity3\n\nIts scheme is::\n\n    timestamp | worker_id | sequence\n     41 bits  |  8 bits   |  4 bits\n\n`Twitter's snowflake scheme\n<https://blog.twitter.com/2010/announcing-snowflake>`_ for id generation has a\nbunch of nice properties. An enormous number of roughly-sorted ids can be\ncreated per second in an uncoordinated manner.\n\nHowever it's *painful* working with 64 bit integers in environments that use\nIEEE 754 floating points for numerics. Particularly `JavaScript\n<https://dev.twitter.com/overview/api/twitter-ids-json-and-snowflake>`_ and the\n`scores for Redis' sorted sets\n<http://stackoverflow.com/questions/20295544/redis-sorted-set-wrong-score>`_.\n\nThis scheme allows for:\n\n    * 69 years of ids (the same as snowflakes)\n    * at most 256 unique id generating workers\n    * each worker can produce at most 16 ids per millisecond\n    * so at most 4 million ids per second\n\nIf you're building a system in the sweet spot of not being too popular that\nusage will grow to creating more than 4 million new things a second but you\ncould benefit from uncoordinated incrementing id generation and don't want to\npunch yourself in the face every time you request data over a websocket from\nJavaScript, this scheme might be a useful alternative.\n\n.. code:: python\n\n    >>> import fity3\n    >>> f3 = fity3.generator(1)\n    >>> next(f3)\n    14127739136\n    >>> next(f3)\n    14132125952\n    >>> next(f3)\n    14135079168\n\n    # convenience to convert to a unix timestamp\n    >>> fity3.to_timestamp(14135079168)\n    1413374250\n\nLet's just hope we won't be the ones supporting any of these systems in 2079.",
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