pymr's Introduction

pyParallelMR

Simple Map Reduce package in python that supports parallel processing

In a nutshell

pyParallelMR leverages the fact that greater speeds in computation can be achieved using more memory.
It is not applicable to problems which require optimized solutions (IoT for example).
A simple queue based map reduce engine which executes tasks in a batch wise parallel manner. (forcible fanciness 😉)

Usage

from pyMR import Master
from collections import defaultdict


# Map logic for word count
def map_wc(lines):
    word_count = defaultdict(int)

    for line in lines:
        for word in line.split():
            word_count[word] += 1

    return word_count


# Reduce logic for word count
def red_wc(kv1, kv2):
    for word, count in kv1.items():
        kv2[word] += count
    return kv2


def main():
    file_path = '/path/to/file'
    master = Master(num_workers=9) # Initialize engine
    master.create_job(data=open(file_path, encoding='utf-8'),
                      map_fn=map_wc, red_fn=red_wc) # Submit the data
    master.start() # Run the job

    result =  master.result()
    print(result)


if __name__ == '__main__':
    main()

Results

Finding primes in a given range
Word count of a document

Finding primes in a given range

The objective of this painstaking ordeal of heavy multiprocessing and a bit of bad python code (sarcasm 😝) is to achieve the high speeds with respect to CPUs completely ignoring the memory usage.