map_outputs = [map_function(item) for item in data] # 模拟Shuffle & Sort(Python字典自动处理) # 使用defaultdict来自动分组 grouped_data = defaultdict(list) for key, value in map_outputs: grouped_data[key].append(value) # 执行Reduce unique_results = [reduce_function(grouped[1]) for grouped...
words = "python is the best language for programming and python is easy to learn".split(" ") result = list(map_reduce_function(words, num_threads=4)) for i in result: print(i) 这里的本质一模一样,将字符串分割为四份,并且分发这四个字符串到不同的线程执行,最后将执行结果归约。只不过由于...
words = "python is the best language for programming and python is easy to learn".split(" ") result = list(map_reduce_function(words, num_threads=4)) for i in result: print(i) 这里的本质一模一样,将字符串分割为四份,并且分发这四个字符串到不同的线程执行,最后将执行结果归约。只不过由于...
threads.append(thread)forthreadinthreads: thread.join()returnmap(reducer, shuffler.items())if__name__ =="__main__": words ="python is the best language for programming and python is easy to learn".split(" ") result =list(map_reduce_function(words, num_threads=4))foriinresult:print(...
(chunk,mapper,shuffler)thread.start()threads.append(thread)forthreadinthreads:thread.join()returnmap(reducer,shuffler.items())if__name__=="__main__":words="python is the best language for programming and python is easy to learn".split(" ")result=list(map_reduce_function(words,num_...
function:代表函数 list:序列 initializer:初始值(可选) 比方说对一个序列求和,就可以用reduce()实现,这里需要注意,python中使用reduce函数时,需要加上from functools import reduce这一句: >>> from functools import reduce >>> def add(x, y): ... return x + y ... >>> reduce(add, [1, 2, 3...
map(function_to_apply, list_of_inputs) function_to_apply:代表函数 list_of_inputs:代表输入序列 注意:python3中 map函数返回的是迭代器 大多数时候,我们要把列表中所有元素一个个地传递给一个函数,并收集输出。 比方说: items=[1,2,3,4,5]# 列表squared=[]foriinitems:squared.append(i**2) ...
```python from collections import defaultdict import itertools # Map函数 def map_function(data):words = data.split()return [(word, 1) for word in words]# Reduce函数 def reduce_function(mapped_data):word_count = defaultdict(int)for word, count in mapped_data:word_count[word] += count re...
MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with...
"Python is fun", "Python programming" ] mapped_data = map_function(text_data) reduced_data = reduce_function(mapped_data) # 输出介绍形式的结果 print("Word Count") for word, count in reduced_data: print(f"{word} {count}") 执行上述代码,输出结果如下: ...