Python 获取Hadoop Mapreduce中出现的最大字数字数

因此，我一直在关注这个网站上的Mapreduce python代码，它从文本文件返回一个单词计数，即单词及其在文本中出现的次数。但是，我想知道如何返回出现的最大单词数。映射器和还原器如下所示：

#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)

---

因此，我知道我需要在减速机的末尾添加一些内容，但我不完全确定是什么。

您只需要设置一个减速机来聚合所有值—numreductasks 1

您的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)

---

但是由于只有一个减速器，您会失去并行化，因此如果在第一个减速器之后运行此作业，可能会更快，而不是相反。这样，您的映射器将与reducer相同。

因此，您只需找到计数最大的单词，并准确地输出它。计数最大的单词和计数本身。我猜在减缩器的末尾有一点代码要添加，但我尝试了无效。