threading进程池

在Python中，可以使用`threading`模块来实现线程池，但是线程池并不一定能够提高程序的性能。相反，过多的线程会导致线程切换的开销，从而降低程序的性能。

下面是一个简单的使用`threading`模块实现线程池的示例：

import threading
from queue import Queue

class ThreadPool:
    def __init__(self, num_threads):
        self.queue = Queue()
        self.threads = []
        for i in range(num_threads):
            thread = threading.Thread(target=self._worker)
            thread.daemon = True
            thread.start()
            self.threads.append(thread)

    def _worker(self):
        while True:
            func, args, kwargs = self.queue.get()
            try:
                func(*args, **kwargs)
            except Exception as e:
                print(e)
            self.queue.task_done()

    def submit(self, func, *args, **kwargs):
        self.queue.put((func, args, kwargs))

    def join(self):
        self.queue.join()

使用时，首先需要创建一个线程池，然后调用`submit`方法来提交任务。最后，调用`join`方法等待所有任务执行完成。

def worker(num):
    print("Worker %d started" % num)
    time.sleep(1)
    print("Worker %d finished" % num)

pool = ThreadPool(4)
for i in range(10):
    pool.submit(worker, i)
pool.join()

上面的代码创建了一个包含4个线程的线程池，然后提交了10个任务，每个任务执行1秒钟。输出结果如下：

Worker 0 started
Worker 1 started
Worker 2 started
Worker 3 started
Worker 0 finished
Worker 4 started
Worker 1 finished
Worker 5 started
Worker 2 finished
Worker 6 started
Worker 3 finished
Worker 7 started
Worker 4 finished
Worker 8 started
Worker 5 finished
Worker 9 started
Worker 6 finished
Worker 7 finished
Worker 8 finished
Worker 9 finished