Question

所以，我一直在关注这个网站上的Mapreduce python代码（http://www.michael-noll.com/tutorials/writing-an-hadoop-mapreduce-program-in-python/），该代码从文本文件中返回一个单词计数（即单词及其在文本中出现的次数）。但是，我想知道如何返回最大发生的单词。映射器和缩减器如下 -

#Mapper

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)

#Reducer

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)

所以，我知道我需要在减速器的末尾添加一些东西，但我还不完全确定是什么。

Answer 1

您只需要设置一个缩减器来聚合所有值（-numReduceTasks 1）

这个你的简化应该是这样的：

max_count = 0
max_word = None

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:
        # check if new word greater
        if current_count > max_count:
            max_count= current_count 
            max_word = current_word        
        current_count = count
        current_word = word

# do not forget to check last word if needed!
if current_count > max_count:
    max_count= current_count 
    max_word = current_word

print '%s\t%s' % (max_word, max_count)

但是只有一个reducer你会失去并行化，所以如果你在第一个之后运行这个工作可能会更快，而不是。这样，您的映射器将与reducer相同。

在Hadoop Mapreduce字数中获得最大值

1 个答案: