{
    "info": {
        "author": "James Socol",
        "author_email": "me@jamessocol.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Environment :: Console",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Apache Software License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Topic :: Utilities"
        ],
        "description": "========\npluribus\n========\n\nHaving just finished reading the original Google `MapReduce paper`_, I\nobviously felt the need to try to implement such a system in Python.\n\nMy goals are to implement enough of the functionality described in the\npaper to be usable, though I strongly warn against ever using this code\nfor anything real.\n\nSince one of the goals (see Goals, below) is simplicity from an end-user\nstandpoint, I am following some of Kenneth Reitz's advice_ and starting\nwith a readme and documentation.\n\n\nExamples\n========\n\nThe canonical word-count example::\n\n    # myjob.py\n    from pluribus import job\n\n\n    @job.map_\n    def emit_words(key, value):\n        # key: document name\n        # value: document contents\n        for word in value.split():\n            yield word, 1\n\n\n    @job.reduce_\n    def sum_occurences(key, values):\n        # key: a word\n        # values: a list of counts\n        return sum(values)\n\n\nAssuming you're running everything on one host, you can ignore the\nnetwork connection information.\n\nStart a pluribus **master**::\n\n    $ pluribus master\n\nStart a pluribus **worker** (or several hundred)::\n\n    $ pluribus worker\n\nOn the master or on another machine that can talk to the master::\n\n    $ pluribus job myjob\n    # ... wait\n    <results>\n\n\nGoals\n=====\n\nExplicit goals are:\n\n* Simple to use, both as an administrator and end-user.\n* Well-documented.\n* Robust to worker failure.\n* Fast-enough.\n* Use only the Python (2.7+) standard library (at least to run).\n\nExplicit non-goals are:\n\n* Be a filesystem.\n* Robust to master failure.\n\n\n.. _MapReduce paper: http://research.google.com/archive/mapreduce.html\n.. _advice: http://docs.writethedocs.org/en/2013/conference/talks.html#kenneth-reitz",
        "description_content_type": null,
        "docs_url": null,
        "download_url": "UNKNOWN",
        "downloads": {
            "last_day": -1,
            "last_month": -1,
            "last_week": -1
        },
        "home_page": "https://github.com/jsocol/pluribus",
        "keywords": null,
        "license": "Apache v2.0",
        "maintainer": null,
        "maintainer_email": null,
        "name": "pluribus",
        "package_url": "https://pypi.org/project/pluribus/",
        "platform": "UNKNOWN",
        "project_url": "https://pypi.org/project/pluribus/",
        "project_urls": {
            "Download": "UNKNOWN",
            "Homepage": "https://github.com/jsocol/pluribus"
        },
        "release_url": "https://pypi.org/project/pluribus/0.0.1/",
        "requires_dist": null,
        "requires_python": null,
        "summary": "A pure-python highly-distributed MapReduce cluster.",
        "version": "0.0.1"
    },
    "last_serial": 675854,
    "releases": {
        "0.0.1": []
    },
    "urls": []
}