python进程池和线程池的区别

from concurrent.futures import ThreadPoolExecutor, as_completed, wait, ALL_COMPLETED


def test(a):
    print(f'一个假的进程任务{a}')
    return a

def add_thread():
    threads_list = [i for i in range(10)]
    th_executor = ThreadPoolExecutor(max_workers=10)
    for i in threads_list:
        # 线程池执行任务方法1
        th_executor.submit(test, i)
    # 线程池执行任务方法2
    th_executor.map(test, threads_list)

    # 线程池获取结果方法1：使用as_completed方法，然后用其result属性输出结果
    all_thread = [th_executor.submit(test, i) for i in threads_list]
    results1 = [j.result() for j in as_completed(all_thread)]
    print('result1结果:', results1)
	# result1结果: [3, 0, 2, 4, 1, 5, 6, 8, 9, 7]

    # 线程池获取结果方法2
    results2 = [k for k in th_executor.map(test, threads_list)]
    print('results2结果:', results2)
    # results2结果: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

    # 也可以使用等待函数,fs:任务序列;timeout任务最大等待时间,在规定时间内,任务未执行完成也会停止;
    # return_when:wait返回结果的条件，默认为ALL_COMPLETED,全部执行完成再返回
    # 譬如,此处以下的代码是展示wait的用法
    # task_list = th_executor.map(test, threads_list)
    # 等待任务完成
    # wait(task_list)
    # 任务完成之后再进行结果的获取
    # task_result = [k for k in task_list]


if __name__ == '__main__':

    add_thread()

from concurrent.futures import ProcessPoolExecutor, as_completed, wait, ALL_COMPLETED

def test(a):
    print(f'一个假的线程任务{a}')
    return a

def add_process():
    with ProcessPoolExecutor(max_workers=8) as pool:
        process_list = [i for i in range(10)]
        for i in process_list:
            # 进程池执行任务方法1
            pool.submit(test, i)
        # 进程池执行任务方法2
        pool.map(test, process_list)

        # 进程池获取结果方法1：使用as_completed方法，然后用其result属性输出结果
        all_process = [pool.submit(test, i) for i in process_list]
        results1 = [j.result() for j in as_completed(all_process)]
        print('result1结果:', results1)
        # results1结果: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

        # 进程池获取结果方法2
        results2 = [k for k in pool.map(test, process_list)]
        print('results2结果:', results2)
    	# results2结果: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

        # 也可以使用等待函数,fs:任务序列;timeout任务最大等待时间,在规定时间内,任务未执行完成也会停止;
        # return_when:wait返回结果的条件，默认为ALL_COMPLETED,全部执行完成再返回
        # 譬如,此处是展示wait的用法
        # task_list = pool.map(test, process_list)
        # # 等待任务完成
        # wait(task_list)
        # # 任务完成之后再进行结果的获取
        # task_result = [k for k in task_list]



if __name__ == '__main__':

    add_process()