这个Python例子 简单形象的演示了单线程和Map实现多线程的时间做了一个对比

import time

from multiprocessing import Pool

import requests

def decorator_timer(old_function):

def new_function(*arg, **dict_arg):

t1 = time.time()

result = old_function(*arg, **dict_arg)

t2 = time.time()

print(“time:“, t2 – t1)

return result

return new_function

def visit_once(i, address = “http://www.cnblogs.com“):

r = requests.get(address)

return r.status_code

def single_thread(f, counts):

result = map(f, range(counts))

return list(result)

def multiple_thread(f, counts, process_number = 10):

p = Pool(process_number)

return p.map(f, range(counts))

if __name__ == “__main__“:

TOTAL = 100

print(“single“)

print(single_thread(visit_once, TOTAL))

print(“multle“)

print(multiple_thread(visit_once, TOTAL))

启动10个进程, 并行处理100个网站下载请求, 单个下载进程描述为visit_once() 多线程用了map方法. 时间大为缩短.
引自 《从Python开始学编程》 这本入门书写的不错, 可惜作者英年早逝. 可见心理健康多么重要, 尤其在当前疫情下.