理解MapReduce

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

   # map函数
   import sys
   for i in stdin:
        i = i.strip()
        words = i.split()
        for word in words:
            print '%s	%s' % (word,1)
   

   #reduce函数
   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('	', 1)
       try:
           count = int(count)
       except ValueError: 
           continue
       if current_word == word:
           current_count += count
       else:
           if current_word:
               print "%s	%s" % (current_word, current_count)
           current_count = count
           current_word = word
    
   if word == current_word: 
       print "%s	%s" % (current_word, current_count)
   

2. 将其权限作出相应修改

   chmod a+x /home/hadoop/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.p
   

4. 放到HDFS上运行
5. 下载并上传文件到hdfs上

   

   

   

6. 用Hadoop Streaming命令提交任务先找到Streaming的Jar包

   

   　　配置默认环境变量

   

   　　让配置生效并测试

   

   　　编写run.sh脚本程序

   

   　

   

   运行结果