Skip to main content

Thin MapReduce-like layer that wraps the Python multiprocessing library.

Project description

Thin MapReduce-like layer that wraps the Python multiprocessing library.

PyPI version and link. travis coveralls

Purpose

This package provides a streamlined interface for the built-in Python multiprocessing library. The interface makes it possible to parallelize in a succinct way (sometimes using only one line of code) a data workflow that can be expressed in a MapReduce-like form. More background information about this package’s design and implementation can be found in a related article.

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 Example

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

from random import choice
from string import ascii_lowercase

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 timeit import default_timer

start = default_timer()
pool = mr4mp.pool()
pool.mapreduce(index, merge, range(100))
pool.close()
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 that we instead explicitly specify 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).

Testing and Conventions

All unit tests are executed and their coverage is measured when using nose (see setup.cfg for configution details):

nosetests

Style conventions are enforced using Pylint:

pylint mr4mp

Contributions

In order to contribute to the source code, open an issue or submit a pull request on the GitHub page for this library.

Versioning

Beginning with version 0.1.0, the version number format for this library and the changes to the library associated with version number increments conform with Semantic Versioning 2.0.0.

Project details


Download files

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

Source Distribution

mr4mp-2.0.0.tar.gz (6.3 kB view details)

Uploaded Source

Built Distribution

mr4mp-2.0.0-py3-none-any.whl (5.5 kB view details)

Uploaded Python 3

File details

Details for the file mr4mp-2.0.0.tar.gz.

File metadata

  • Download URL: mr4mp-2.0.0.tar.gz
  • Upload date:
  • Size: 6.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.25.1 setuptools/42.0.1 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.8.0

File hashes

Hashes for mr4mp-2.0.0.tar.gz
Algorithm Hash digest
SHA256 535220297a69693e9b0f273cb099eeee55091f5a79e2f76ca11d0a77d3a6aa31
MD5 7a9318e9f6e118deffb122199b31aef6
BLAKE2b-256 c2a9e2d2d3b90b71ba2f52b428dee1901b2c66745e158eb8a82ef5361b556658

See more details on using hashes here.

Provenance

File details

Details for the file mr4mp-2.0.0-py3-none-any.whl.

File metadata

  • Download URL: mr4mp-2.0.0-py3-none-any.whl
  • Upload date:
  • Size: 5.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.25.1 setuptools/42.0.1 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.8.0

File hashes

Hashes for mr4mp-2.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 05779b19335aa0e3d9e509d38799d354a318f591249cc5b36e69905127102503
MD5 29e9eec26d92f1aa6c81e8968a8909b8
BLAKE2b-256 1a746253802d6faee024694854f44246c614314871f2d3b8f7344b55b003851c

See more details on using hashes here.

Provenance

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