使用Python实现Hadoop MapReduce程序
转自：使用Python实现Hadoop MapReduce程序 http://blog.csdn.net/zhaoyl03/article/details/8657031/英文原文：Writing an Hadoop MapReduce Program in Python
根据上面两篇文章，下面是我在自己的ubuntu上的运行过程。文字基本采用博文使用Python实现Hadoop MapReduce程序，打字很浪费时间滴。
在这个实例中，我将会向大家介绍如何使用Python 为 Hadoop编写一个简单的MapReduce程序。
尽管Hadoop 框架是使用Java编写的但是我们仍然需要使用像C++、Python等语言来实现 Hadoop程序。尽管Hadoop官方网站给的示例程序是使用Jython编写并打包成Jar文件，这样显然造成了不便，其实，不一定非要这样来实现，我们可以使用Python与Hadoop 关联进行编程，看看位于/src/examples/python/WordCount.py的例子，你将了解到我在说什么。
我们想要做什么？
我们将编写一个简单的 MapReduce 程序，使用的是C-Python，而不是Jython编写后打包成jar包的程序。
我们的这个例子将模仿 WordCount 并使用Python来实现，例子通过读取文本文件来统计出单词的出现次数。结果也以文本形式输出，每一行包含一个单词和单词出现的次数，两者中间使用制表符来想间隔。
先决条件
编写这个程序之前，你学要架设好Hadoop 集群，这样才能不会在后期工作抓瞎。如果你没有架设好，那么在后面有个简明教程来教你在Ubuntu Linux 上搭建（同样适用于其他发行版linux、unix）
如何在Ubuntu Linux 上搭建hadoop的单节点模式和伪分布模式，请参阅博文Ubuntu上搭建Hadoop环境（单机模式+伪分布模式）
Python的MapReduce代码
使用Python编写MapReduce代码的技巧就在于我们使用了 HadoopStreaming 来帮助我们在Map 和 Reduce间传递数据通过STDIN (标准输入)和STDOUT (标准输出).我们仅仅使用Python的sys.stdin来输入数据，使用sys.stdout输出数据，这样做是因为HadoopStreaming会帮我们办好其他事。这是真的，别不相信！
Map: mapper.py
将下列的代码保存在/usr/local/hadoop/mapper.py中，他将从STDIN读取数据并将单词成行分隔开，生成一个列表映射单词与发生次数的关系：
注意：要确保这个脚本有足够权限（chmod +x mapper.py）。
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[*]#!/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)
在这个脚本中，并不计算出单词出现的总数，它将输出 "<word> 1" 迅速地，尽管<word>可能会在输入中出现多次，计算是留给后来的Reduce步骤（或叫做程序）来实现。当然你可以改变下编码风格，完全尊重你的习惯。Reduce: reducer.py
将代码存储在/usr/local/hadoop/reducer.py 中，这个脚本的作用是从mapper.py 的STDIN中读取结果，然后计算每个单词出现次数的总和，并输出结果到STDOUT。
同样，要注意脚本权限：chmod +x reducer.py
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[*]#!/usr/bin/env python
[*]
[*]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('\t', 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\t%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\t%s' % (current_word, current_count)
测试你的代码（cat data | map | sort | reduce）
我建议你在运行MapReduce job测试前尝试手工测试你的mapper.py 和 reducer.py脚本，以免得不到任何返回结果
这里有一些建议，关于如何测试你的Map和Reduce的功能：
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[*]hadoop@derekUbun:/usr/local/hadoop$ echo "foo foo quux labs foo bar quux" | ./mapper.py
[*]foo 1
[*]foo 1
[*]quux 1
[*]labs 1
[*]foo 1
[*]bar 1
[*]quux 1
[*]hadoop@derekUbun:/usr/local/hadoop$ echo "foo foo quux labs foo bar quux" |./mapper.py | sort |./reducer.py
[*]bar 1
[*]foo 3
[*]labs 1
[*]quux 2
# using one of the ebooks as example input
# (see below on where to get the ebooks)
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[*]hadoop@derekUbun:/usr/local/hadoop$ cat book/book.txt |./mapper.pysubscribe 1
[*]to 1
[*]our 1
[*]email 1
[*]newsletter 1
[*]to 1
[*]hear 1
[*]about 1
[*]new 1
[*]eBooks. 1
在Hadoop平台上运行Python脚本
为了这个例子，我们将需要一本电子书，把它放在/usr/local/hadpoop/book/book.txt之下
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[*]hadoop@derekUbun:/usr/local/hadoop$ ls -l book
[*]总用量 636
[*]-rw-rw-r-- 1 derek derek 6496693月 12 12:22 book.txt
复制本地数据到HDFS
在我们运行MapReduce job 前，我们需要将本地的文件复制到HDFS中：
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[*]hadoop@derekUbun:/usr/local/hadoop$ hadoop dfs -copyFromLocal /usr/local/hadoop/book book
[*]hadoop@derekUbun:/usr/local/hadoop$ hadoop dfs -ls
[*]Found 3 items
[*]drwxr-xr-x - hadoop supergroup 0 2013-03-12 15:56 /user/hadoop/book
执行 MapReduce job现在，一切准备就绪，我们将在运行Python MapReduce job 在Hadoop集群上。像我上面所说的，我们使用的是HadoopStreaming 帮助我们传递数据在Map和Reduce间并通过STDIN和STDOUT，进行标准化输入输出。
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[*]hadoop@derekUbun:/usr/local/hadoop$hadoop jar contrib/streaming/hadoop-streaming-1.1.2.jar
[*]-mapper /usr/local/hadoop/mapper.py
[*]-reducer /usr/local/hadoop/reducer.py
[*]-input book/*
[*]-output book-output
在运行中，如果你想更改Hadoop的一些设置，如增加Reduce任务的数量，你可以使用“-jobconf”选项：
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[*]hadoop@derekUbun:/usr/local/hadoop$hadoop jar contrib/streaming/hadoop-streaming-1.1.2.jar
[*]-jobconf mapred.reduce.tasks=4
[*]
[*]-mapper /usr/local/hadoop/mapper.py
[*]-reducer /usr/local/hadoop/reducer.py
[*]-input book/*
[*]-output book-output
如果上面两个运行出错，请参考下面一段代码。注意，重新运行，需要删除dfs中的output文件
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[*]bin/hadoop jar contrib/streaming/hadoop-streaming-1.1.2.jar
[*]-mapper task1/mapper.py
[*]-file task1/mapper.py
[*]-reducer task1/reducer.py
[*]-file task1/reducer.py
[*]-input url
[*]-output url-output
[*]-jobconf mapred.reduce.tasks=3
一个重要的备忘是关于Hadoop does not honor mapred.map.tasks 这个任务将会读取HDFS目录下的book并处理他们，将结果存储在独立的结果文件中，并存储在HDFS目录下的book-output目录。之前执行的结果如下：
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[*]hadoop@derekUbun:/usr/local/hadoop$ hadoop jar contrib/streaming/hadoop-streaming-1.1.2.jar -jobconf mapred.reduce.tasks=4 -mapper /usr/local/hadoop/mapper.py -reducer /usr/local/hadoop/reducer.py -input book/* -output book-output
[*]13/03/12 16:01:05 WARN streaming.StreamJob: -jobconf option is deprecated, please use -D instead.
[*]packageJobJar: [] /tmp/streamjob5047485520312501206.jar tmpDir=null
[*]13/03/12 16:01:06 INFO util.NativeCodeLoader: Loaded the native-hadoop library
[*]13/03/12 16:01:06 WARN snappy.LoadSnappy: Snappy native library not loaded
[*]13/03/12 16:01:06 INFO mapred.FileInputFormat: Total input paths to process : 1
[*]13/03/12 16:01:06 INFO streaming.StreamJob: getLocalDirs():
[*]13/03/12 16:01:06 INFO streaming.StreamJob: Running job: job_201303121448_0010
[*]13/03/12 16:01:06 INFO streaming.StreamJob: To kill this job, run:
[*]13/03/12 16:01:06 INFO streaming.StreamJob: /usr/local/hadoop/libexec/../bin/hadoop job-Dmapred.job.tracker=localhost:9001 -kill job_201303121448_0010
[*]13/03/12 16:01:06 INFO streaming.StreamJob: Tracking URL: http://localhost:50030/jobdetails.jsp?jobid=job_201303121448_0010
[*]13/03/12 16:01:07 INFO streaming.StreamJob:map 0%reduce 0%
[*]13/03/12 16:01:10 INFO streaming.StreamJob:map 100%reduce 0%
[*]13/03/12 16:01:17 INFO streaming.StreamJob:map 100%reduce 8%
[*]13/03/12 16:01:18 INFO streaming.StreamJob:map 100%reduce 33%
[*]13/03/12 16:01:19 INFO streaming.StreamJob:map 100%reduce 50%
[*]13/03/12 16:01:26 INFO streaming.StreamJob:map 100%reduce 67%
[*]13/03/12 16:01:27 INFO streaming.StreamJob:map 100%reduce 83%
[*]13/03/12 16:01:28 INFO streaming.StreamJob:map 100%reduce 100%
[*]13/03/12 16:01:29 INFO streaming.StreamJob: Job complete: job_201303121448_0010
[*]13/03/12 16:01:29 INFO streaming.StreamJob: Output: book-output
[*]hadoop@derekUbun:/usr/local/hadoop$
如你所见到的上面的输出结果，Hadoop 同时还提供了一个基本的WEB接口显示统计结果和信息。
当Hadoop集群在执行时，你可以使用浏览器访问 http://localhost:50030/ ：
检查结果是否输出并存储在HDFS目录下的book-output中：
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[*]hadoop@derekUbun:/usr/local/hadoop$ hadoop dfs -ls book-output
[*]Found 6 items
[*]-rw-r--r-- 2 hadoop supergroup 0 2013-03-12 16:01 /user/hadoop/book-output/_SUCCESS
[*]drwxr-xr-x - hadoop supergroup 0 2013-03-12 16:01 /user/hadoop/book-output/_logs
[*]-rw-r--r-- 2 hadoop supergroup 33 2013-03-12 16:01 /user/hadoop/book-output/part-00000
[*]-rw-r--r-- 2 hadoop supergroup 60 2013-03-12 16:01 /user/hadoop/book-output/part-00001
[*]-rw-r--r-- 2 hadoop supergroup 54 2013-03-12 16:01 /user/hadoop/book-output/part-00002
[*]-rw-r--r-- 2 hadoop supergroup 47 2013-03-12 16:01 /user/hadoop/book-output/part-00003
[*]hadoop@derekUbun:/usr/local/hadoop$
可以使用dfs -cat 命令检查文件目录
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[*]hadoop@derekUbun:/usr/local/hadoop$ hadoop dfs -cat book-output/part-00000
[*]about 1
[*]eBooks. 1
[*]the 1
[*]to2
[*]hadoop@derekUbun:/usr/local/hadoop$
下面是原英文作者mapper.py和reducer.py的两个修改版本:
mapper.py
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[*]#!/usr/bin/env python
[*]"""A more advanced Mapper, using Python iterators and generators."""
[*]
[*]import sys
[*]
[*]def read_input(file):
[*] for line in file:
[*] # split the line into words
[*] yield line.split()
[*]
[*]def main(separator='\t'):
[*] # input comes from STDIN (standard input)
[*] data = read_input(sys.stdin)
[*] for words in data:
[*] # 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
[*] for word in words:
[*] print '%s%s%d' % (word, separator, 1)
[*]
[*]if __name__ == "__main__":
[*] main()
reducer.py
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[*]#!/usr/bin/env python
[*]"""A more advanced Reducer, using Python iterators and generators."""
[*]
[*]from itertools import groupby
[*]from operator import itemgetter
[*]import sys
[*]
[*]def read_mapper_output(file, separator='\t'):
[*] for line in file:
[*] yield line.rstrip().split(separator, 1)
[*]
[*]def main(separator='\t'):
[*] # input comes from STDIN (standard input)
[*] data = read_mapper_output(sys.stdin, separator=separator)
[*] # groupby groups multiple word-count pairs by word,
[*] # and creates an iterator that returns consecutive keys and their group:
[*] # current_word - string containing a word (the key)
[*] # group - iterator yielding all ["<current_word>", "<count>"] items
[*] for current_word, group in groupby(data, itemgetter(0)):
[*] try:
[*] total_count = sum(int(count) for current_word, count in group)
[*] print "%s%s%d" % (current_word, separator, total_count)
[*] except ValueError:
[*] # count was not a number, so silently discard this item
[*] pass
[*]
[*]if __name__ == "__main__":
[*] main()
[*]
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