python - 获取 Hadoop Mapreduce 字数中出现的最大字数

Question

因此，我一直在关注此网站 ( http://www.michael-noll.com/tutorials/writing-an-hadoop-mapreduce-program-in-python/ ) 上的 Mapreduce python 代码，该代码从文本文件返回字数统计(即该字及其在文本中出现的次数)。但是，我想知道如何返回出现的最大单词。 mapper和reducer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 reducer 的末尾添加一些东西，但我不确定是什么。

Answer 1

你只需要设置一个reducer来聚合所有的值(-numReduceTasks 1)

你的 reduce 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 你失去了并行化，所以如果你在第一个之后运行这个作业可能会更快，而不是相反。这样，您的 mapper 将与 reducer 相同。