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）。

#!/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

 

#!/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：

 

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)

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
 

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：

 

 

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。

 

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：

 

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

 

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：

 

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：

 

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

 

 

 

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

 

#!/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

 

#!/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()