MR hadoop streaming job的学习 combiner

代码已经拷贝到了公司电脑的：

/Users/baidu/Documents/Data/Work/Code/Self/hadoop_mr_streaming_jobs

首先是主控脚本 main.sh

调用的是 extract.py

然后发现写的不太好。其中有一个combiner，可以看这里：

https://blog.csdn.net/u010700335/article/details/72649186

streaming 脚本的时候，是以管道为基础的：

（5）  Python脚本

1 2 3

import sys for line in sys.stdin: .......

#!/usr/bin/env python

import sys

# maps words to their counts
word2count = {}

# input comes from STDIN (standard input)
for line in sys.stdin:
    # remove leading and trailing whitespace
    line = line.strip()
    # split the line into words while removing any empty strings
    words = filter(lambda word: word, 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
        print '%s\t%s' % (word, )
#---------------------------------------------------------------------------------------------------------
#!/usr/bin/env python

from operator import itemgetter
import sys

# maps words to their counts
word2count = {}

# 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()
    # convert count (currently a string) to int
    try:
        count = int(count)
        word2count[word] = word2count.get(word, ) + count
    except ValueError:
        # count was not a number, so silently
        # ignore/discard this line
        pass

# sort the words lexigraphically;
#
# this step is NOT required, we just do it so that our
# final output will look more like the official Hadoop
# word count examples
sorted_word2count = sorted(word2count.items(), key=itemgetter())

# write the results to STDOUT (standard output)
for word, count in sorted_word2count:
    print '%s\t%s'% (word, count)