使用Python实现Hadoop MapReduce程序

转自：使用Python实现Hadoop MapReduce程序

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 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：

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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: [/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。

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

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

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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()