线程池的基类是 concurrent.futures 模块中的 Executor，Executor 提供了两个子类，即 ThreadPoolExecutor 和 ProcessPoolExecutor，其中 ThreadPoolExecutor 用于创建线程池，而 ProcessPoolExecutor 用于创建进程池。

示例代码1：   【使用map方法】

from concurrent.futures import ThreadPoolExecutor, TimeoutError
import time


def tasks(num):
    count = 0
    for i in range(num):
        count += i
        time.sleep(0.1)
    return f"total:{count}"


# 创建一个包含4个线程的线程池，使用map函数
def thread_pool_run():
    with ThreadPoolExecutor(max_workers=4) as pool:
        # 后面元组有3个元素，因此程序启动3条线程来执行tasks函数
        results = pool.map(tasks, [10, 20, 30])
        for i in results:
            print(i)


if __name__ == '__main__':
    thread_pool_run()

运行结果：

示例代码2：

from concurrent.futures import ThreadPoolExecutor, TimeoutError
import time


def tasks(num):
    count = 0
    for i in range(num):
        count += i
        time.sleep(0.1)
    return f"total:{count}"


# 创建一个包含4个线程的线程池，使用map函数
def thread_pool_run():
    with ThreadPoolExecutor(max_workers=4) as pool:
        # 后面元组有3个元素，因此程序启动3条线程来执行tasks函数
        results = pool.map(tasks, [10, 20, 30])
        for i in results:
            print(i)


# 设置线程超时
def thread_pool_set_timeout():
    futures = []
    with ThreadPoolExecutor(max_workers=4) as pool:
        for i in (10, 20, 30, 40, 50):
            future = pool.submit(tasks, i)
            futures.append([i, future])
        for future in futures:
            try:
                res = future[1].result(timeout=2)
                print(res)
            except TimeoutError:
                print(future[0], ': is timeout!')


if __name__ == '__main__':
    # thread_pool_run()
    thread_pool_set_timeout()

运行结果：