用Python來寫分散式的程式。這樣速度快。便於除錯，更有實際意義。MapReduce適合於對文字檔案的處理及資料探勘用：

　　在每臺機器上：
su - hadoop
wget http://www.python.org/ftp/python/3.0.1/Python-3.0.1.tar.bz2
tar jxvf Python-3.0.1.tar.bz2
cd Python-3.0.1
./configure --prefix=/home/hadoop/python;make;make install

vi /home/hadoop/mapper.py

#!/home/hadoop/python/bin/python3.0

import sys
for line in sys.stdin:
    line = line.strip()
    words = line.split()
    for word in words:
        print ("%st%s" % (word, 1))

vi /home/hadoop/reduce.py

#!/home/hadoop/python/bin/python3.0

from operator import itemgetter
import sys

word2count = {}

for line in sys.stdin:
    line = line.strip()
    word, count = line.split('t', 1)
    try:
        count = int(count)
        word2count[word] = word2count.get(word, 0) + count
    except ValueError:
        pass

sorted_word2count = sorted(word2count.items(), key=itemgetter(0))

for word, count in sorted_word2count:
    print ("%st%s" % (word, count))

　　測測好不好用：
echo "foo foo quux labs foo bar quux" | /home/hadoop/mapper.py
foo 1
foo 1
quux 1
labs 1
foo 1
bar 1
quux 1

echo "foo foo quux labs foo bar quux" | /home/hadoop/mapper.py | sort | /home/hadoop/reduce.py
bar 1
foo 3
labs 1
quux 2

　　在各個節點上都要準備好這兩個檔案啊！！！

　　在master主節點上執行：

# 拷貝conf目錄到hdfs檔案系統中
$ cd /home/hadoop/hadoop-0.19.1
$ bin/hadoop dfs -copyFromLocal conf 111

　　# 檢視一下是否已經拷過去了
$ bin/hadoop dfs -ls
Found 1 items
drwxr-xr-x - hadoop supergroup 0 2009-05-18 15:27 /user/hadoop/111


　　# 分佈計算
$ bin/hadoop jar contrib/streaming/hadoop-0.19.1-streaming.jar -mapper /home/hadoop/mapper.py -reducer /home/hadoop/reduce.py -input 111/* -output 111-output
additionalConfSpec_:null
[email protected]@@userJobConfProps_.get(stream.shipped.hadoopstreaming
packageJobJar: [/tmp/hadoop-hadoop/hadoop-unjar29198/] [] /tmp/streamjob29199.jar tmpDir=null
[...] INFO mapred.FileInputFormat: Total input paths to process : 12
[...] INFO streaming.StreamJob: getLocalDirs(): [/tmp/hadoop-hadoop/mapred/local]
[...] INFO streaming.StreamJob: Running job: job_200905191453_0001
[...] INFO streaming.StreamJob: To kill this job, run:
...
[...]
[...] INFO streaming.StreamJob: map 0% reduce 0%
[...] INFO streaming.StreamJob: map 43% reduce 0%
[...] INFO streaming.StreamJob: map 86% reduce 0%
[...] INFO streaming.StreamJob: map 100% reduce 0%
[...] INFO streaming.StreamJob: map 100% reduce 33%
[...] INFO streaming.StreamJob: map 100% reduce 70%
[...] INFO streaming.StreamJob: map 100% reduce 77%
[...] INFO streaming.StreamJob: map 100% reduce 100%
[...] INFO streaming.StreamJob: Job complete: job_200905191453_0001
[...] INFO streaming.StreamJob: Output: 111-output [[email protected] hadoop-0.19.1]$
$ bin/hadoop dfs -ls 111-output
Found 2 items
drwxr-xr-x - hadoop supergroup 0 2009-05-19 14:54 /user/hadoop/111-output/_logs
-rw-r--r-- 2 hadoop supergroup 30504 2009-05-19 16:26 /user/hadoop/111-output/part-00000
$ bin/hadoop dfs -cat 111-output/part-00000
you 3
you've 1
your 1
zero 3
zero, 1

Over，搞定。大家可以拓展這個例子，寫出自己的應用來。