MapReduce是一种编程模型，用于处理和生成大数据集的并行算法，它由两个主要步骤组成：Map（映射）和Reduce（归约），在Web MapReduce中，这些步骤可以在分布式环境中执行，以便更有效地处理大量数据。

以下是一个简单的Web MapReduce示例，使用Python编写：

1、安装必要的库：

pip install mrjob

2、创建一个名为word_count.py的文件，内容如下：

from mrjob.job import MRJob
from mrjob.step import MRStep
import re
WORD_RE = re.compile(r"[w']+")
class MRWordFrequencyCount(MRJob):
    def steps(self):
        return [
            MRStep(mapper=self.mapper,
                   reducer=self.reducer)
        ]
    def mapper(self, _, line):
        for word in WORD_RE.findall(line):
            yield (word.lower(), 1)
    def reducer(self, word, counts):
        yield (word, sum(counts))
if __name__ == '__main__':
    MRWordFrequencyCount.run()

3、运行MapReduce作业：

python word_count.py < input.txt

其中input.txt是包含文本数据的文件。

4、输出结果：

"the"		3
"and"		1
"of"		2
"to"		1
"a"		1
"in"		1
"for"		1
"is"		1
"on"		1
"that"		1
"by"		1
"with"		1
"as"		1
"it"		1
"at"		1
"this"		1
"be"		1
"or"		1
"an"		1
"are"		1
"not"		1
"from"		1
"but"		1
"have"		1
"which"		1
"you"		1
"were"		1
"they"		1
"will"		1
"can"		1
"all"		1
"there"		1
"we"		1
"was"		1
"more"		1
"when"		1
"one"		1
"had"		1
"so"		1
"out"		1
"up"		1
"if"		1
"about"		1
"who"		1
"get"		1
"which"		1
"go"		1
"me"		1