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        "author": "Andrei Lapets",
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        "description": "=====\nmr4mp\n=====\n\nThin MapReduce-like layer on top of the Python multiprocessing library.\n\n.. image:: https://badge.fury.io/py/mr4mp.svg\n   :target: https://badge.fury.io/py/mr4mp\n   :alt: PyPI version and link.\n\nPackage Installation and Usage\n------------------------------\nThe package is available on PyPI::\n\n    python -m pip install mr4mp\n\nThe library can be imported in the usual way::\n\n    import mr4mp\n\nExamples\n--------\n\nWord-Document Index\n~~~~~~~~~~~~~~~~~~~\n\nSuppose we have some functions that we can use to build an index of randomly generated words::\n\n    def word(): # Generate a random 7-letter \"word\".\n        return ''.join(choice(ascii_lowercase) for _ in range(7))\n    \n    def index(id): # Build an index mapping some random words to an identifier.\n        return {w:{id} for w in {word() for _ in range(100)}}\n    \n    def merge(i, j): # Merge two index dictionaries i and j.\n        return {k:(i.get(k,set()) | j.get(k,set())) for k in i.keys() | j.keys()}\n\nWe can then construct an index in the following way::\n\n    start = timer()\n    pool = mr4mp.pool()\n    pool.mapreduce(index, merge, range(100))\n    print(\"Finished in \" + str(timer()-start) + \"s using \" + str(len(pool)) + \" process(es).\")\n\nThe above might yield the following output::\n\n    Finished in 0.664681524217187s using 2 process(es).\n\nSuppose we had instead explicitly specified that only one process can be used::\n\n    pool = mr4mp.pool(1)\n\nAfter the above modification, we might see the following output from the code block::\n\n    Finished in 2.23329004518571s using 1 process(es).\n",
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