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 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）。 [python] view plaincopy #!/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 [python] view plaincopy #!/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： [plain] view plaincopy 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) [plain] view plaincopy 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 [plain] view plaincopy hadoop@derekUbun:/usr/local/hadoop$ ls -l book 总用量 636 -rw-rw-r-- 1 derek derek 649669 3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： [plain] view plaincopy 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。 [plain] view plaincopy hadoop@derekUbun:/usr/local/hadoop$hadoop jar contrib/streaming/hadoop-streaming-1.1.2.jar -mapper /usr/local/hadoop/mapper.py -reducer /usr/local/hadoop/reducer.py -input book/* -output book-output 在运行中，如果你想更改Hadoop的一些设置，如增加Reduce任务的数量，你可以使用“-jobconf”选项： [plain] view plaincopy hadoop@derekUbun:/usr/local/hadoop$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 [plain] view plaincopy 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： [plain] view plaincopy 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: [/usr/local/hadoop/tmp/hadoop-unjar4835873410426602498/] [] /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(): [/usr/local/hadoop/tmp/mapred/local] 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。 [plain] view plaincopy 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 [plain] view plaincopy hadoop@derekUbun:/usr/local/hadoop$ hadoop dfs -cat book-output/part-00000 about 1 eBooks. 1 the 1 to 2 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 [python] view plaincopy #!/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 [python] view plaincopy #!/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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