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Pythonic in-memory MapReduce.

Project description

Experimental Pythonic MapReduce inspired by Spotify’s luigi framework.

Canonical Word Count Example

Currently there are two MapReduce implementations, one that includes sorting and one that does not. The example below would not benefit from sorting so we can take advantage of the inherent optimization of not sorting. The API is the same but tinymr.memory.MRSerial() sorts after partitioning and again between the reducer() and final_reducer().

import json
import re
import sys

from tinymr.memory import MRSerial

class WordCount(MRSerial):

    def __init__(self):
        self.pattern = re.compile('[\W_]+')

    def mapper(self, item):
        for word in item.split():
            word = self.pattern.sub('', word)
            if word:
                yield word.lower(), 1

    def reducer(self, key, values):
        yield key, sum(values)

    def final_reducer(self, pairs):
        return {k: tuple(v)[0] for k, v in pairs}

wc = WordCount()
with open('LICENSE.txt') as f:
    out = wc(f)
    print(json.dumps(out, indent=4, sort_keys=True))

Truncated output:

    "a": 1,
    "above": 2,
    "advised": 1,
    "all": 1,
    "and": 8,
    "andor": 1


$ git clone
$ cd tinymr
$ pip install -e .\[dev\]
$ py.test tests --cov tinymr --cov-report term-missing





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