1/ concurrent.futures模块 线程池：concurrent.futures.ThreadPoolExecutor(max_workers) 进程池：concurrent.futures.ProcessPoolExecutor(max_workers) 2、使用对比：进程的性能更好 import concurrent.futures import time number_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
def evaluate_item(x): # 计算总和，这里只是为了消耗时间 result_item = count(x) # 打印输入和输出结果 return result_item
def count(number) : for i in range(0, 10000000): i=i 1 return i * number
if name == "main": # 顺序执行 start_time = time.time() for item in number_list: print(evaluate_item(item)) print("Sequential execution in " str(time.time() - start_time), "seconds") # 线程池执行 start_time_1 = time.time() with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor: futures = [executor.submit(evaluate_item, item) for item in number_list] for future in concurrent.futures.as_completed(futures): print(future.result()) print ("Thread pool execution in " str(time.time() - start_time_1), "seconds") # 进程池 start_time_2 = time.time() with concurrent.futures.ProcessPoolExecutor(max_workers=5) as executor: futures = [executor.submit(evaluate_item, item) for item in number_list] for future in concurrent.futures.as_completed(futures): print(future.result()) print ("Process pool execution in " str(time.time() - start_time_2), "seconds")
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