Hadoop: 在Azure Cluster上使用MapReduce

Azure对于学生账户有260刀的免费试用，火急火燎地创建Hadoop Cluster！本例子是使用Hadoop MapReduce来统计一本电子书中各个单词的出现个数.

Let's get hands dirty!

首先，我们在Azure中创建了一个Cluster，并且使用putty Ssh访问了该集群，ls一下：

在cluster上创建一个/home/hduser/文件夹

OK，接下来在本地创建一个mapper.py文件和reducer.py文件，注意权限：chmod +x reducer.py（mapper.py）

mapper.py代码

#!/usr/bin/env python
"""mapper.py"""

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	%s' % (word, 1)

reducer.py代码

#!/usr/bin/env python
"""reducer.py"""

from operator import itemgetter
import sys

current_word = None
current_count = 0
word = None

# input comes from STDIN
for line in sys.stdin:
    # remove leading and trailing whitespace
    line = line.strip()

    # parse the input we got from mapper.py
    word, count = line.split('	', 1)

    # convert count (currently a string) to int
    try:
        count = int(count)
    except ValueError:
        # count was not a number, so silently
        # ignore/discard this line
        continue

    # 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:
            # write result to STDOUT
            print '%s	%s' % (current_word, current_count)
        current_count = count
        current_word = word

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

本地mapper.py测试: echo "foo foo quux labs foo bar quux" | ./mapper.py

并复制到Cluster上：

本例中，我们使用一本电子书，地址是http://www.gutenberg.org/cache/epub/20417/pg20417.txt，直接在linux客户端下载后，上传到cluster中

OK,万事俱备，运行MapReduce

查看输出文件前20行