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Thin MapReduce-like layer on top of the Python multiprocessing library.

Project description

Thin MapReduce-like layer on top of the Python multiprocessing library.

PyPI version and link.

Package Installation and Usage

The package is available on PyPI:

python -m pip install mr4mp

The library can be imported in the usual way:

import mr4mp


Word-Document Index

Suppose we have some functions that we can use to build an index of randomly generated words:

def word(): # Generate a random 7-letter "word".
    return ''.join(choice(ascii_lowercase) for _ in range(7))

def index(id): # Build an index mapping some random words to an identifier.
    return {w:{id} for w in {word() for _ in range(100)}}

def merge(i, j): # Merge two index dictionaries i and j.
    return {k:(i.get(k,set()) | j.get(k,set())) for k in i.keys() | j.keys()}

We can then construct an index in the following way:

from random import choice
from string import ascii_lowercase
from timeit import default_timer
start = default_timer()
pool = mr4mp.pool()
pool.mapreduce(index, merge, range(100))
print("Finished in " + str(default_timer()-start) + "s using " + str(len(pool)) + " process(es).")

The above might yield the following output:

Finished in 0.664681524217187s using 2 process(es).

Suppose we had instead explicitly specified that only one process can be used:

pool = mr4mp.pool(1)

After the above modification, we might see the following output from the code block:

Finished in 2.23329004518571s using 1 process(es).

Project details

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