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

　　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

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	%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('	', 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 current_word == word:
    print '%s	%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

　　下载文本文件或爬取网页内容存成的文本文件：

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

　　寻找你的streaming的jar文件存放地址：

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

　　打开环境变量配置文件:

gedit ~/.bashrc

　　在里面写入streaming路径:

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

　　让环境变量生效:

source ~/.bashrc
echo $STREAM

　　建立一个shell名称为run.sh来运行：

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