python + hadoop （案例）

python如何链接hadoop，并且使用hadoop的资源，这篇文章介绍了一个简单的案例！

一、python的map/reduce代码

首先认为大家已经对haoop已经有了很多的了解，那么需要建立mapper和reducer，分别代码如下：

1、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)

2、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:
    words = line.strip()
    word, count = words.split('\t')
    
    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)

建立了两个代码之后，测试一下：

[[email protected] /export/python]$ echo "I like python hadoop , hadoop very good" | ./mapper.py | sort -k 1,1 | ./reducer.py
,    1
good    1
hadoop    2
I    1
like    1
python    1
very    1

二、上传文件

发现没啥问题，那么成功一半了，下面上传几个文件到hadoop做进一步测试。我在线上找了几个文件，命令如下：

wget http://www.gutenberg.org/ebooks/20417.txt.utf-8
wget http://www.gutenberg.org/files/5000/5000-8.txt
wget http://www.gutenberg.org/ebooks/4300.txt.utf-8

查看下载的文件：

[[email protected] /export/python]$ ls
20417.txt.utf-8  4300.txt.utf-8  5000-8.txt  mapper.py  reducer.py  run.sh

上传文件到hadoop上面，命令如下：hadoop dfs -put ./*.txt /user/ticketdev/tmp （hadoop是配置好的，目录也是建立好的）

建立run.sh

hadoop jar $STREAM  \
 -files ./mapper.py,./reducer.py \
 -mapper ./mapper.py \
 -reducer ./reducer.py \
 -input /user/ticketdev/tmp/*.txt \
 -output /user/ticketdev/tmp/output

查看结果：

[[email protected] /export/python]$ hadoop dfs -cat /user/ticketdev/tmp/output/part-00000 | sort -nk 2 | tail 
DEPRECATED: Use of this script to execute hdfs command is deprecated.
Instead use the hdfs command for it.

it    2387
which    2387
that    2668
a    3797
is    4097
to    5079
in    5226
and    7611
of    10388
the    20583

三、参考文献：

http://www.cnblogs.com/wing1995/p/hadoop.html?utm_source=tuicool&utm_medium=referral