python線程池ThreadPoolExecutor用法

線程池，進程池

python的多線程並不是完全雞肋的存在，得分情況來看。在IO密集型任務下，能提高多倍效率。在CPU密集型任務下，使用多進程也能規避GIL鎖。
python3標准庫concurrent.futures比原Thread封裝更高，多線程concurrent.futures.ThreadPoolExecutor，多進程concurrent.futures.ProcessPoolExecutor
利用concurrent.futures.Future來進行各種便捷的數據交互，包括處理異常，都在result()中再次拋出。

模板

import time
from concurrent import futures
from concurrent.futures import ThreadPoolExecutor


def display(args):
    print(time.strftime('[%H:%M:%S]', time.localtime()), end=' ')
    print(args)


def task(n):
    """只是休眠"""
    display('begin sleep {}s.'.format(n))
    time.sleep(n)
    display('ended sleep {}s.'.format(n))


def do_many_task_inorder():
    """多線程
    按任務發布順序依次等待完成
    """
    tasks = [5, 4, 3, 2, 1]
    with ThreadPoolExecutor(max_workers=3) as executor:
        future_list = [executor.submit(task, arg) for arg in tasks]

        display('非阻塞運行')

        for future in future_list:
            display(future)

        display('統一結束(有序)')

        for future in future_list:
            display(future.result())


def do_many_task_disorder():
    """多線程執行
    先完成先顯示
    """
    tasks = [5, 4, 3, 2, 1]
    with ThreadPoolExecutor(max_workers=3) as executor:
        future_list = [executor.submit(task, arg) for arg in tasks]

        display('非阻塞運行')

        for future in future_list:
            display(future)

        display('統一結束(無序)')

        done_iter = futures.as_completed(future_list)  # generator

        for done in done_iter:
            display(done)


if __name__ == '__main__':
    do_many_task_inorder()
    do_many_task_disorder()