多线程与多进程的比较

多线程适合于多io操作

多进程适合于耗cpu(计算)的操作

# 多进程编程
# 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
import time
from  concurrent.futures import ThreadPoolExecutor, as_completed
from  concurrent.futures import ProcessPoolExecutor


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

if __name__ == '__main__':

    # 1. 对于耗cpu操作,多进程优于多线程

    # with ThreadPoolExecutor(3) as executor:
    #     all_task = [executor.submit(fib, num) for num in range(25, 35)]
    #     start_time = time.time()
    #     for future in as_completed(all_task):
    #         data = future.result()
    #         print(data)
    #     print("last time :{}".format(time.time() - start_time))  # 3.905290126800537


    
    # 多进程 ,在window环境 下必须放在main方法中执行,否则抛异常
    with ProcessPoolExecutor(3) as executor:
        all_task = [executor.submit(fib, num) for num in range(25, 35)]
        start_time = time.time()
        for future in as_completed(all_task):
            data = future.result()
            print(data)
        print("last time :{}".format(time.time() - start_time))  # 2.6130592823028564

可以看到在耗cpu的应用中,多进程明显优于多线程     2.6130592823028564 < 3.905290126800537

下面模拟一个io操作

# 多进程编程
# 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
import time
from  concurrent.futures import ThreadPoolExecutor, as_completed
from  concurrent.futures import ProcessPoolExecutor

def io_operation(n):
    time.sleep(2)
    return n


if __name__ == '__main__':

    # 1. 对于耗cpu操作,多进程优于多线程

    # with ThreadPoolExecutor(3) as executor:
    #     all_task = [executor.submit(io_operation, num) for num in range(25, 35)]
    #     start_time = time.time()
    #     for future in as_completed(all_task):
    #         data = future.result()
    #         print(data)
    #     print("last time :{}".format(time.time() - start_time))  # 8.00358772277832



    # 多进程 ,在window环境 下必须放在main方法中执行,否则抛异常
    with ProcessPoolExecutor(3) as executor:
        all_task = [executor.submit(io_operation, num) for num in range(25, 35)]
        start_time = time.time()
        for future in as_completed(all_task):
            data = future.result()
            print(data)
        print("last time :{}".format(time.time() - start_time))  # 8.12435245513916

可以看到 8.00358772277832 < 8.12435245513916, 即是多线程比多进程更牛逼!