线程池与进程池

最新推荐文章于 2024-12-04 11:10:14 发布

原创最新推荐文章于 2024-12-04 11:10:14 发布 · 117 阅读

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博客围绕线程和进程池展开，提及可重复利用的线程、简单线程池，重点介绍了Python线程池和进程池，还展示了使用`multiprocessing`模块的`Pool`实现并发服务器。

1.可重复利用的线程

from threading import Thread
from queue import Queue
import time,random

class MyThread(Thread):
    def __init__(self,queue):
        super().__init__()
        self.queue = queue
        self.start()

    def run(self):
        while True:
            func,args= self.queue.get()
            func(*args)
            self.queue.task_done()

def task(info):
    print(info)
    time.sleep(3)


if __name__ == '__main__':
    queue = Queue()
    for i in range(6):
        queue.put((task,(random.randint(1,100),)))
    for i in range(4):
        t = MyThread(queue)

2.简单线程池

from threading import Thread
from queue import Queue
import time, os


class Pool(object):
    def __init__(self, num):
        if num is None:
            num = os.cpu_count() * 2 or 1
        if num < 1:
            raise ValueError("子线程的数量最少是1")
        self.num = num
        self.queue = Queue()
        for i in range(self.num):
            t = Thread(target=self.job,daemon = True)
            t.start()

    def job(self):
        while True:
            func, args, kwargs = self.queue.get()
            func(*args, **kwargs)
            self.queue.task_done()

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

    def apply_asyc(self, func, args=(), kwargs=None):
        if kwargs is None:
            kwargs = {}
        self.queue.put((func, args, kwargs))


def task(attr):
    print(attr)
    time.sleep(3)
    print('%s号任务完成' %attr)


if __name__ == '__main__':
    pool = Pool(4)
    for i in range(4):
        pool.apply_asyc(task, args=(i,))
    pool.join()
    print('主进程结束')

3.python线程池

from threading import Thread
from queue import Queue
import time, random


class MyThread(Thread):
    def __init__(self, queue):
        super().__init__()
        self.daemon = True
        self.queue = queue
        self.start()

    def run(self):
        while True:
            func, args = self.queue.get()
            func(*args)
            self.queue.task_done()


def task(info):
    print(info)
    time.sleep(3)


if __name__ == '__main__':
    queue = Queue()
    for i in range(8):
        queue.put((task, (random.randint(1, 100),)))
    for i in range(4):
        t = MyThread(queue)
    queue.join()

3.python进程池

from multiprocessing import Pool
使用同线程池

实现并发服务器：

from multiprocessing import Pool
import socket

def task(sock):
    while True:
        r_data = sock.recv(1024)
        if r_data:
            sock.send(r_data)
            print(r_data.decode('utf-8'))
        else:
            sock.close()
            break

if __name__ == '__main__':
    pool = Pool(8)
    server = socket.socket()
    server.bind(('',8080))
    server.listen(1000)
    while True:
        conn,addr = server.accept()
        pool.apply_async(task,args=(conn,))