(From ziyuang: this repository is the port of mincemeatpy for Python 3.x, see the commits of mincemeat.py. Below is the original README texts)

mincemeat.py: MapReduce on Python

Introduction

mincemeat.py is a Python implementation of the MapReduce distributed computing framework.

mincemeat.py is:

• Lightweight - All of the code is contained in a single Python file (currently weighing in at <13kB) that depends only on the Python Standard Library. Any computer with Python and mincemeat.py can be a part of your cluster.
• Fault tolerant - Workers (clients) can join and leave the cluster at any time without affecting the entire process.
• Secure - mincemeat.py authenticates both ends of every connection, ensuring that only authorized code is executed.
• Open source - mincemeat.py is distributed under the MIT License, and consequently is free for all use, including commercial, personal, and academic, and can be modified and redistributed without restriction.

Download

• Just mincemeat.py (v 0.1.2)
• The full 0.1.2 release (includes documentation and examples)
• Clone this git repository: git clone https://github.com/michaelfairley/mincemeatpy.git

Example

Let's look at the canonical MapReduce example, word counting:

example.py:

#!/usr/bin/env python
import mincemeat

data = ["Humpty Dumpty sat on a wall",
        "Humpty Dumpty had a great fall",
        "All the King's horses and all the King's men",
        "Couldn't put Humpty together again",
        ]

def mapfn(k, v):
    for w in v.split():
        yield w, 1

def reducefn(k, vs):
    result = 0
    for v in vs:
        result += v
    return result

s = mincemeat.Server()

# The data source can be any dictionary-like object
s.datasource = dict(enumerate(data))
s.mapfn = mapfn
s.reducefn = reducefn

results = s.run_server(password="changeme")
print(results)

Execute this script on the server:

python example.py

Run mincemeat.py as a worker on a client:

python mincemeat.py -p changeme [server address]

And the server will print out:

{'a': 2, 'on': 1, 'great': 1, 'Humpty': 3, 'again': 1, 'wall': 1, 'Dumpty': 2, 'men': 1, 'had': 1, 'all': 1, 'together': 1, "King's": 2, 'horses': 1, 'All': 1, "Couldn't": 1, 'fall': 1, 'and': 1, 'the': 2, 'put': 1, 'sat': 1} 

This example was overly simplistic, but changing the datasource to be a collection of large files and running the client on multiple machines will work just as well. In fact, mincemeat.py has been used to produce a word frequency lists for many gigabytes of text using a slightly modified version of this code.

Imports

One potential gotcha when using mincemeat.py: Your mapfn and reducefn functions don't have access to their enclosing environment, including imported modules. If you need to use an imported module in one of these functions, be sure to include import whatever in the functions themselves.