前景回顾

抢票系统的代码优化，使用了Lock类

from multiprocessing import Process,Lock

import os,time,json

with open('user', 'w', encoding='utf-8')as f:

    dic={'count':2}

    json.dump(dic,f)

def search():

    with open('user','r',encoding='utf-8')as f:

        data=json.load(f)

        print(data.get('count'))

def get():

    with open('user','r',encoding='utf-8')as f:

        data = json.load(f)

    if data['count'] > 0:

        data['count'] -= 1

        with open('user','w',encoding='utf-8')as f:

                json.dump(data,f)

                print('success')

                time.sleep(1)

    else:

        print('票已售空')

def piao(lock):

    search()

    lock.acquire()

    get()

    lock.release()

if __name__ == '__main__':

    lock=Lock()

    for i in range(5):

        p=Process(target=piao,args=(lock,))

        p.start()

队列

1

from multiprocessing import Queue,Process

q=Queue()

q.put(1)

print(q)

data=q.get()

print(data)

data=q.get()

print(data) #默认会一直等待拿值

q.put(5)

2

from multiprocessing import Queue,Process

q=Queue(4)

q.put(1)

q.put(5)

q.put(5)

q.put(5)

q.put(5)    #此处队列满了，就会成阻塞状态

q.get()

q.get()

3

from multiprocessing import Queue,Process

q=Queue(4)

q.put(1)

q.put(5)

q.put(5)

q.put(5)

q.put(5,block=True,timeout=3)    #此处队列满了，就会成阻塞状态,block为等待状态，timeout为等待时间，等不到就报错

q.get()

q.get()

4

from multiprocessing import Queue,Process

q=Queue(4)

q.put(1)

q.get()

q.get(block=True,timeout=3) #队列已被取空，此时再取就会阻塞，block为阻塞状态，timeout为阻塞时间

5

from multiprocessing import Queue,Process

q=Queue(1)

q.put(1)

q.put_nowait(5) #队列已满，此时再存就会阻塞，相当于put的默认block为False

生产者消费者模型

版本一

from multiprocessing import Queue,Process

import time

def produce(q,name,msg):

    for i in range(3):

        q.put(msg+str(i))

        print(f'{name}生产了{msg+str(i)}')

        time.sleep(1)

    q.put(None)				#用None来终止消费者

def cost(q,name):

    while True:

        msg=q.get()

        if msg==None:

            break

        print(f'{name}吃了{msg}')

        time.sleep(1)

if __name__ == '__main__':

    q=Queue()

    p1=Process(target=produce,args=(q,'wind','card'))

    p2=Process(target=produce,args=(q,'nick','niunai'))

    # p1=Process(target=Process,args=(q,'wind','card'))

    c1=Process(target=cost,args=(q,'chanyuli'))

    c2=Process(target=cost,args=(q,'zhongshifu'))

    p1.start()

    p2.start()

    c1.start()

    c2.start()

版本二

from multiprocessing import Queue,Process,JoinableQueue

import time

def produce(q,name,msg):

    for i in range(3):

        q.put(msg+str(i))

        print(f'{name}生产了{msg+str(i)}')

        time.sleep(1)

def cost(q,name):

    while True:

        msg=q.get()

        q.task_done()

        if msg==None:

            break

        print(f'{name}吃了{msg}')

        time.sleep(1)

if __name__ == '__main__':

    q=JoinableQueue()

    p1=Process(target=produce,args=(q,'wind','card'))

    p2=Process(target=produce,args=(q,'nick','niunai'))

    # p1=Process(target=Process,args=(q,'wind','card'))

    c1=Process(target=cost,args=(q,'chanyuli'))

    c2=Process(target=cost,args=(q,'zhongshifu'))

    c1.daemon=True

    c2.daemon=True

    p1.start()

    p2.start()

    c1.start()

    c2.start()

    p1.join()

    p2.join()

    q.join()

巴特西

python-day37(正式学习)

前景回顾

队列

1

2

3

4

5

生产者消费者模型

版本一

版本二

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