mapper.py

#!/usr/bin/env python

import sys

# input comes from STDIN (standard input)
for line in sys.stdin:
    # remove leading and trailing whitespace
    line = line.strip()
    # split the line into words
    words = line.split()
    # increase counters
    for word in words:
        # write the results to STDOUT (standard output);
        # what we output here will be the input for the
        # Reduce step, i.e. the input for reducer.py
        #
        # tab-delimited; the trivial word count is 1
        print '%s\t%s' % (word, 1)</pre>

mapper2.py

#!/usr/bin/env python

import sys
from my_awesome_algorithm import main_processing

for line in sys.stdin:
    line = line.strip()
    
    main_processing(line)

reducer.py

#!/usr/bin/env python
from operator import itemgetter
import sys

current_word = None
current_count = 0

for line in sys.stdin:
    line = line.strip()

    # parse the input we got from mapper.py
    word, count = line.split('\t', 1)
    count = int(count)

    # this IF-switch only works because Hadoop sorts map output
    # by key (here: word) before it is passed to the reducer
    if current_word == word:
        current_count += count
    else:
        if current_word:
            print '%s\t%s' % (current_word, current_count)
        current_count = count
        current_word = word

# do not forget to output the last word if needed!
print '%s\t%s' % (current_word, current_count)