11-9 多线程和多进程对比

对于耗cpu的操作，用多进程编程，
对于IO操作来说，使用多线程编程，进程切换代价要高于线程

1. 对于耗费cpu的操作，多进程优于多线程，见下例

def fib(n):
    if n<=2:
        return 1
    return fib(n-1)+fib(n-2)

if __name__ == "__main__":
    with ThreadPoolExecutor(3) as executor:
        all_task = [executor.submit(fib, (num)) for num in range(25,40)]
        start_time = time.time()
        for future in as_completed(all_task):
            data = future.result()
            print("exe result: {}".format(data))

        print("last time is: {}".format(time.time()-start_time))

#运行结果：last time is: 51.336591958999634

-------------------------------------------------------
if __name__ == '__main__':

    with ProcessPoolExecutor(3) as executor:
        all_task = [executor.submit(fib, (num)) for num in range(25,40)]
        start_time = time.time()
        for future in as_completed(all_task):
            data=future.result()
            print('exe result: {}'.format(data))

        print("last time is: {}".format(time.time()-start_time))
#运行结果last time is: 27.97818684577942

2.对于io操作来说，多线程优于多进程

def random_sleep(n):
    time.sleep(n)
    return n

if __name__ == "__main__":
    with ProcessPoolExecutor(3) as executor:
        all_task = [executor.submit(random_sleep, (num)) for num in [1]*30]
        start_time = time.time()
        for future in as_completed(all_task):
            data = future.result()
            print("exe result: {}".format(data))

        print("last time is: {}".format(time.time()-start_time))
#运行时间结果：last time is: 14.078815221786499

#ProcessPoolExecutor更换为ThreadPoolExecutor
#运行时间结果：last time is: 14.001399993896484