理解MapReduce计算构架

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

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

   　　

   　编写两个函数

   　　mapper.py:

   #!/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:

   #!/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

   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

   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

   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

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

   　　

6. 用Hadoop Streaming命令提交任务

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

   　　

   gedit ~/.bashrc
   

   　　

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

   　　

   source ~/.bashrc
   echo $STREAM
   

   　　

   gedit run.sh
   

   　　

   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
   

   　　

   source run.sh