Python实现Hadoop MapReduce程序

时间:2021-06-27 02:28:19

1.概述

Hadoop Streaming提供了一个便于进行MapReduce编程的工具包,使用它可以基于一些可执行命令、脚本语言或其他编程语言来实现Mapper和 Reducer,从而充分利用Hadoop并行计算框架的优势和能力,来处理大数据。需要注意的是,Streaming方式是基于Unix系统的标准输入 输出来进行MapReduce Job的运行,它区别与Pipes的地方主要是通信协议,Pipes使用的是Socket通信,是对使用C++语言来实现MapReduce Job并通过Socket通信来与Hadopp平台通信,完成Job的执行。任何支持标准输入输出特性的编程语言都可以使用Streaming方式来实现MapReduce Job,基本原理就是输入从Unix系统标准输入,输出使用Unix系统的标准输出。

2.Hadoop Streaming原理

mapper和reducer会从标准输入中读取用户数据,一行一行处理后发送给标准输出。Streaming工具会创建MapReduce作业,发送给各个tasktracker,同时监控整个作业的执行过程。

如果一个文件(可执行或者脚本)作为mapper,mapper初始化时,每一个mapper任务会把该文件作为一个单独进程启动,mapper任 务运行时,它把输入切分成行并把每一行提供给可执行文件进程的标准输入。 同时,mapper收集可执行文件进程标准输出的内容,并把收到的每一行内容转化成key/value对,作为mapper的输出。 默认情况下,一行中第一个tab之前的部分作为key,之后的(不包括tab)作为value如果没有tab,整行作为key值,value值为null。

对于reducer,类似。以上是Map/Reduce框架和streaming mapper/reducer之间的基本通信协议。

3.Hadoop Streaming用法

Usage: $HADOOP_HOME/bin/hadoop jar \

$HADOOP_HOME/share/hadoop/tools/lib/hadoop-streaming-2.7.0.jar

options:

  (1)-input:输入文件路径

  (2)-output:输出文件路径

  (3)-mapper:用户自己写的mapper程序,可以是可执行文件或者脚本

  (4)-reducer:用户自己写的reducer程序,可以是可执行文件或者脚本

  (5)-file:打包文件到提交的作业中,可以是mapper或者reducer要用的输入文件,如配置文件,字典等。

  (6)-partitioner:用户自定义的partitioner程序

  (7)-combiner:用户自定义的combiner程序(必须用java实现)

  (8)-D:作业的一些属性(以前用的是-jonconf),具体有:
1)mapred.map.tasks:map task数目
2)mapred.reduce.tasks:reduce task数目
3)stream.map.input.field.separator/stream.map.output.field.separator: map task输入/输出数据的分隔符,默认均为\t。
4)stream.num.map.output.key.fields:指定map task输出记录中key所占的域数目
5)stream.reduce.input.field.separator/stream.reduce.output.field.separator:reduce task输入/输出数据的分隔符,默认均为\t。
6)stream.num.reduce.output.key.fields:指定reduce task输出记录中key所占的域数目

另外,Hadoop本身还自带一些好用的Mapper和Reducer:

4.使用示例

使用Python编写MapReduce代码的技巧就在于我们使用了 HadoopStreaming 来帮助我们在Map 和 Reduce间传递数据通过STDIN (标准输入)和STDOUT (标准输出).我们仅仅使用Python的sys.stdin来输入数据,使用sys.stdout输出数据,这样做是因为 HadoopStreaming会帮我们办好其他事。这是真的,别不相信!

举例

将下列的代码保存在/usr/local/hadoop/mapper.py中,他将从STDIN读取数据并将单词成行分隔开,生成一个列表映射单词与发生次数的关系:注意:要确保这个脚本有足够权限(chmod +x mapper.py)。

#!/usr/bin/env python  

import sys  

# input comes from STDIN (standard input)
for line in sys.stdin:
# remove leading and trailing whitespace
line = line.strip()
# split the line into words
words = line.split()
# increase counters
for word in words:
# write the results to STDOUT (standard output);
# what we output here will be the input for the
# Reduce step, i.e. the input for reducer.py
#
# tab-delimited; the trivial word count is 1
print '%s\t%s' % (word, 1)

将代码存储在/usr/local/hadoop/reducer.py 中,这个脚本的作用是从mapper.py 的STDIN中读取结果,然后计算每个单词出现次数的总和,并输出结果到STDOUT。同样,要注意脚本权限:chmod +x reducer.py

#!/usr/bin/env python  

from operator import itemgetter
import sys current_word = None
current_count = 0
word = None # input comes from STDIN
for line in sys.stdin:
# remove leading and trailing whitespace
line = line.strip() # parse the input we got from mapper.py
word, count = line.split('\t', 1) # convert count (currently a string) to int
try:
count = int(count)
except ValueError:
# count was not a number, so silently
# ignore/discard this line
continue # this IF-switch only works because Hadoop sorts map output
# by key (here: word) before it is passed to the reducer
if current_word == word:
current_count += count
else:
if current_word:
# write result to STDOUT
print '%s\t%s' % (current_word, current_count)
current_count = count
current_word = word # do not forget to output the last word if needed!
if current_word == word:
print '%s\t%s' % (current_word, current_count)

测试结果:

hadoop@derekUbun:/usr/local/hadoop$ echo "foo foo quux labs foo bar quux" | ./mapper.py
foo 1
foo 1
quux 1
labs 1
foo 1
bar 1
quux 1
hadoop@derekUbun:/usr/local/hadoop$ echo "foo foo quux labs foo bar quux" |./mapper.py | sort |./reducer.py
bar 1
foo 3
labs 1
quux 2

实例

需求:这里面只是个小练习,没有多高深,简单的不能再简单,只是一个小实例,做个抛砖的作用。

写一个mapreduce streaming程序(可使用任意语言,这里我们用python),将数据转换成“key=value”的格式,其中,key包括“ip”、“time”、“path”三个,

比如,175.44.30.93 - - [29/Sep/2013:00:10:16 +0800] "GET /structure/heap/ HTTP/1.1" 200 22539 "-" "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1;SV1;)"

转化为:ip=175.44.30.93|time=29/Sep/2013:00:10:16|path=/structure/heap/ 其中,不同key/value之间用“|”分割。

具体步骤:

1.将日志文件上传到hdfs上 hadoop fs -put 文件 目的地

2.编程程序,这个比较简单,我觉得只用mapper就能实现,我就只写了一个mapper。

 #!/usr/bin/env python
# -*- coding: utf-8 -*- import sys for line in sys.stdin: #接受系统的标准输入
line = line.strip()
lists = line.split()
print 'ip=%s|time=%s|path=%s' %(lists[0],lists[3].strip('[]'),lists[6])#处理成想要的结果

3.测试程序执行命令

hadoop jar /home/biedong/hadoop-2.7.0/share/hadoop/tools/lib/hadoop-streaming-2.7.0.jar -mapper /home/biedong/test/mapper1.py -input /home/zuoye/access.log -output /home/zuoye/book-output

执行报错:提示找不多执行程序, 比如“Caused by: java.io.IOException: Cannot run program “/user/hadoop/Mapper”: error=2, No such file or directory”:

解决办法:可在提交作业时,采用-file选项指定这些文件, 比如上面例子中,可以使用“-file Mapper -file Reducer” 或者 “-file Mapper.py -file Reducer.py”, 这样,Hadoop会将这两个文件自动分发到各个节点上。

hadoop jar /home/biedong/hadoop-2.7.0/share/hadoop/tools/lib/hadoop-streaming-2.7.0.jar -mapper /home/biedong/test/mapper1.py -file /home/biedong/test/mapper1.py -input /home/zuoye/access.log -output /home/zuoye/book-output

执行完成后在hdfs上的结果:文件输出正常,结果也正常1904条。

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" alt="" />

4.加个reducer吧,这个比较简单,因为mapper已经处理好了,我直接接受mapper的输入,完了直接打印出来。

#!/usr/bin/env python
# -*- coding: utf-8 -*- import sys for line in sys.stdin:
print line

问题是:多出一个空行

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" alt="" />

原因查找:默认情况下,Streaming使用\t分离记录中得键和值,当没有\t时,整个记录被视为键,值为空白文本。 在mapper输出的时候会自动在尾行加上\t 因此在reducer接受后,会把数据直接按照\t拆分成k和v两个,只是k是mapper的数据行,v是空白,如果咱们直接输出结果的话,就会有空白行。

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