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.

• Just mincemeat.py (v 0.1.4)
• The full 0.1.4 release (includes documentation and examples)
• Clone this git repository: git clone https://github.com/michaelfairley/mincemeatpy.git

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",
        ]
# The data source can be any dictionary-like object
datasource = dict(enumerate(data))

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

def reducefn(k, vs):
    result = sum(vs)
    return result

s = mincemeat.Server()
s.datasource = datasource
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.

You can run the client manually from within other Python scripts (rather than running mincemeat.py directly):

import mincemeat

client = mincemeat.Client()
client.password	= "changeme"
client.conn("localhost", mincemeat.DEFAULT_PORT)

Shepherd.py provides more sophisticated ways to run clients, including having client that poll or are forked on the same machine.

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.

ziyuang has a fork of mincemeat.py that's comptable with python 3: ziyuang/mincemeatpy