Skip to main content

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





Project details

Release history Release notifications | RSS feed

This version


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

tinymr-0.1.tar.gz (16.8 kB view hashes)

Uploaded source

Built Distribution

tinymr-0.1-py2.py3-none-any.whl (15.1 kB view hashes)

Uploaded py2 py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page