MapReduce实现

用python实现编程实现文件合并和去重操作

1.查看python环境

2.编写mapper函数和reducer函数

在/usr/local/hadoop/wc文件夹下（如果没有文件夹就创建一个）

gedit mapper.py

mapper.py

#!/usr/bin/env python3
# encoding=utf-8

lines = {
    
    }

import sys
for line in sys.stdin:
    line = line.strip()
    key, value = line.split()
    if key not in lines.keys():
        lines[key] = []
    if value not in lines[key]:
        lines[key].append(value)
        lines[key] = sorted(lines[key])

for key, value in lines.items():
    print(key, end = ' ')
    for i in value:
        print(i, end = ' ')
    print()

gedit reducer.py

reducer.py

#!/usr/bin/env python3
# encoding=utf-8

import sys

key, values = None, []

for line in sys.stdin:
    line = line.strip().split(' ')
    if key == None:
        key = line[0]
    if line[0] != key:
        key = line[0]
        values = []
    if line[1] not in values:
        print('%s\t%s' % (line[0], line[1]))
        values.append(line[1])

3.可运行权限设定与查看

chomod a+x ./mapper.py
chomod a+x ./mapper.py

4.在Hadoop上运行实现文件合并和去重操作

编写A.txt,B.txt

gedit A.txt

20150101 x
20150102 y
20150103 x
20150104 y
20150105 z
20150106 x

gedit B.txt

20150101 y
20150102 y
20150103 x
20150104 z
20150105 y

在Hadoop上运行Python代码

如果hdfs命令有问题就添加下环境变量

export PATH=$PATH:/usr/local/hadoop/bin

如果没有input文件夹则需要创建

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

hadoop jar $STREAM  \
-file /usr/local/hadoop/wc/mapper.py \
-mapper /usr/local/hadoop/wc/mapper.py \
-file /usr/local/hadoop/wc/reducer.py \
-reducer /usr/local/hadoop/wc/reducer.py \
-input /input/* \
-output /output