用python来写mapreduce的实际应用程序...

前几篇介绍了MapReduce环境的搭建，我们来做些更有实际意义的事情吧，用Python来写分布式的程序。这样速度快。便于调试，更有实际意义。
个人感觉MapReduce适合于对文本文件的处理及数据挖掘用：

　　在每台机器上：
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))

　　测测好不好用：
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

　　在各个节点上都要准备好这两个文件啊！！！

　　在master主节点上执行：

# 拷贝conf目录到hdfs文件系统中
$ cd /home/hadoop/hadoop-0.19.1
$ bin/hadoop dfs -copyFromLocal conf 111

　　# 查看一下是否已经拷过去了
$ bin/hadoop dfs -ls
Found 1 items
drwxr-xr-x - hadoop supergroup 0 2009-05-18 15:27 /user/hadoop/111

　　# 分布计算
$ 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
null=@@@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 [hadoop@wangyin4 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。大家可以拓展这个例子，写出自己的应用来。

转自：http://hi.baidu.com/lvmajia/blog/item/2c0126cefc4a5433b700c8f4.html