【Python学习系列四】Python程序通过hadoop-streaming提交到Hadoop集群执行MapReduce

场景：将Python程序通过hadoop-streaming提交到Hadoop集群执行。

参考：http://www.michael-noll.com/tutorials/writing-an-hadoop-mapreduce-program-in-python/

1、Python编写Mapper

   业务逻辑是从会从标准输入（stdin）读取数据，默认以空格分割单词，然后按行输出单词机器出现频率到标准输出（stdout），不过整个Map处理过程并不会统计每个单词出现的总次数，而是直接输出“word,1”，以便作为Reduce的输入进行统计。
   代码如下：

#coding:utf-8

'''
Created on 2017年6月7日
@author: fjs
'''

#!/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)2、Python编写Reducer

   Reduce代码，它会从标准输入（stdin）读取mapper.py的结果，然后统计每个单词出现的总次数并输出到标准输出（stdout）。

   代码如下：
#coding:utf-8

'''
Created on 2017年6月7日
@author: fjs
'''

#!/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)

3、文件准备

   1)将python程序文件上传到Hadoop集群客户机，为文件赋予执行权限

   #chmod +x /data/etlcj/python/mapper.py

   #chmod +x /data/etlcj/python/reducer.py

   2)上传测试文件到集群

   #vi /data/etlcj/python/wcin.txt   加入：
foo foo quux labs foo bar quux abc bar see you by test welcome test abc labs foo me python hadoop ab ac bc bec python上传到集群

   #hadoop fs -put /data/etlcj/python/wcin.txt  /apps/etlcj/python/

4、基于hadoop-streaming执行MapReduce任务：

     执行语句：

#hadoop jar /usr/hdp/2.5.3.0-37/hadoop-mapreduce/hadoop-streaming-2.7.3.2.5.3.0-37.jar -files '/data/etlcj/python/mapper.py,/data/etlcj/python/reducer.py' -input /apps/etlcj/python/wcin.txt -output /apps/etlcj/python/out/ -mapper ./mapper.py -reducer ./reducer.py 执行过程中提示：

Error: java.lang.RuntimeException: PipeMapRed.waitOutputThreads(): subprocess failed with code 126
at org.apache.hadoop.streaming.PipeMapRed.waitOutputThreads(PipeMapRed.java:322)
at org.apache.hadoop.streaming.PipeMapRed.mapRedFinished(PipeMapRed.java:535)
at org.apache.hadoop.streaming.PipeMapper.close(PipeMapper.java:130)
at org.apache.hadoop.mapred.MapRunner.run(MapRunner.java:61)
at org.apache.hadoop.streaming.PipeMapRunner.run(PipeMapRunner.java:34)
at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:453)
at org.apache.hadoop.mapred.MapTask.run(MapTask.java:343)
at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:168)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1724)
at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:162)

怀疑是py脚本代码问题或版本环境不匹配问题，对python语法不熟悉，暂无法深入，但python提交到hadoop集群的方法可以。 

5、hadoop-streaming参数参考：

  Usage:hadoop jar $Haoop_Home$/hadoop-streaming-*.jar 

   -input <输入目录> \ # 可以指定多个输入路径，例如：-input '/user/foo/dir1' -input '/user/foo/dir2'

   -inputformat <输入格式 JavaClassName> 

   -output <输出目录> 

   -outputformat <输出格式 JavaClassName> 

   -mapper <mapper executable or JavaClassName> 

   -reducer <reducer executable or JavaClassName> 

   -combiner <combiner executable or JavaClassName> 

   -partitioner <JavaClassName> \

   -cmdenv <name=value> \ # 可以传递环境变量，可以当作参数传入到任务中，可以配置多个

   -file <依赖的文件> \ # 配置文件，字典等依赖

   -D <name=value> \ # 作业的属性配置