理解MapReduce计算构架

Posted 陈子翔

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用Python编写WordCount程序任务

程序

WordCount

输入

一个包含大量单词的文本文件

输出

文件中每个单词及其出现次数(频数),并按照单词字母顺序排序,每个单词和其频数占一行,单词和频数之间有间隔

 

1.编写map函数,reduce函数

  首先在/home/hadoop路径下建立wc文件夹,在wc文件夹下创建文件mapper.py和reducer.py

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cd /home/hadoop
mkdir wc
cd /home/hadoop/wc
touch mapper.py
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touch reducer.py

  

  编写两个函数

  mapper.py:

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#!/usr/bin/env python
import sys
for line in sys.stdin:
    line = line.strip()
    words = line.split()
    for word in words:
        print \'%s\\t%s\' % (word,1)

  

  reducer.py:

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#!/usr/bin/env python
from operator import itemgetter
import sys
 
current_word = None
current_count = 0
word=None
 
for line in sys.stdin:
    line = line.strip()
    word, count = line.split(\'\\t\', 1)
    try:
        count=int(count)
    except ValueError:
        continue
 
    if current_word == word:
        current_count += count
    else:
        if current_word:
            print \'%s\\t%s\' % (current_word,  current_count)
        current_count = count
        current_word = word
if current_word == word:
    print \'%s\\t%s\' % (current_word,  current_count)

  

2.将其权限作出相应修改

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chmod a+x /home/hadoop/wc/mapper.py
chmod a+x /home/hadoop/wc/reducer.py

 

3.本机上测试运行代码

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echo "foo foo quux labs foo bar quux" | /home/hadoop/wc/mapper.py
 
echo "foo foo quux labs foo bar quux" | /home/hadoop/wc/mapper.py | sort -k1,1 | /home/hadoop/wc/reducer.py

  

 

4.放到HDFS上运行

  下载文本文件或爬取网页内容存成的文本文件:

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cd  /home/hadoop/wc
wget http://www.gutenberg.org/files/5000/5000-8.txt
wget http://www.gutenberg.org/cache/epub/20417/pg20417.txt

  

5.下载并上传文件到hdfs上

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hdfs dfs -put /home/hadoop/hadoop/gutenberg/*.txt /user/hadoop/input

 

6.用Hadoop Streaming命令提交任务

   寻找你的streaming的jar文件存放地址:

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cd /usr/local/hadoop/share/hadoop/tools/lib/hadoop-streaming-2.7.1.jar

  打开环境变量配置文件:

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gedit ~/.bashrc

  在里面写入streaming路径:

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export STREAM=$HADOOP_HOME/share/hadoop/tools/lib/hadoop-streaming-*.jar

  让环境变量生效:

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source ~/.bashrc
echo $STREAM

  建立一个shell名称为run.sh来运行:

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gedit run.sh
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hadoop jar $STREAM
-file /home/hadoop/wc/mapper.py \\
-mapper /home/hadoop/wc/mapper.py \\
-file /home/hadoop/wc/reducer.py \\
-reducer /home/hadoop/wc/reducer.py \\
-input /user/hadoop/input/*.txt \\
-output /user/hadoop/wcoutput
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source run.sh

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