multiprocessor(下)
一、数据共享
展望未来,基于消息传递的并发编程是大势所趋即便是使用线程,推荐做法也是将程序设计为大量独立的线程集合,通过消息队列交换数据。这样极大地减少了对使用锁定和其他同步手段的需求,还可以扩展到分布式系统中。
进程间应该尽量避免通信,即便需要通信,也应该选择进程安全的工具来避免加锁带来的问题,应该尽量避免使用本节所讲的共享数据的方式,以后我们会尝试使用数据库来解决进程之间的数据共享问题。
进程之间数据共享的模块之一Manager模块。
进程间数据是独立的,可以借助于队列或管道实现通信,二者都是基于消息传递的
虽然进程间数据独立,但可以通过Manager实现数据共享,事实上Manager的功能远不止于此
A manager object returned by Manager() controls a server process which holds Python objects and allows other processes to manipulate them using proxies.
A manager returned by Manager() will support types list, dict, Namespace, Lock, RLock, Semaphore, BoundedSemaphore, Condition, Event, Barrier, Queue, Value and Array.
多进程共同去处理共享数据的时候,就和我们多进程同时去操作一个文件中的数据是一样的,不加锁就会出现错误的结果,进程不安全的,所以也需要加锁。
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from multiprocessing import Manager,Process,Lock
def work(d,lock):
with lock: #不加锁而操作共享的数据,肯定会出现数据错乱
d['count']-=1
if __name__ == '__main__':
lock=Lock()
with Manager() as m:
dic=m.dict({'count':100})
p_l=[]
for i in range(100):
p=Process(target=work,args=(dic,lock))
p_l.append(p)
p.start()
for p in p_l:
p.join()
print(dic) #0,加了锁后
#100减100次1这么慢? 不是减操作造成的 而是开启进程 管理进程 销毁进程拖慢了程序的执行速度
# 为什么在这里出现了数据不安全的现象?
# 什么情况下会出现数据不安全 : Manager类当中对字典\列表 += -= *= /=
# 如何解决 : 加锁
总结一下:进程之间通信:队列、管道、数据共享也算
下面要讲的信号量和事件也相当于锁,也是全局的,所有进程都能拿到这些锁的状态,进程之间这些锁啊信号量啊事件啊等等的通信,其实底层还是socekt,只不过是基于文件的socket通信,而不是跟上面的数据共享啊空间共享啊之类的机制,我们之前学的是基于网络的socket通信,还记得socket的两个家族吗,一个文件的一个网络的,所以将来如果说这些锁之类的报错,可能你看到的就是类似于socket的错误,简单知道一下就可以啦~~~
工作中常用的是锁,信号量和事件不常用,但是信号量和事件面试的时候会问到,你能知道就行啦
二、信息量(进程中的,也有线程的)
信息量Semaphore介绍
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互斥锁同时只允许一个线程更改数据,而信号量Semaphore是同时允许一定数量的线程更改数据 。
假设商场里有4个迷你唱吧,所以同时可以进去4个人,如果来了第五个人就要在外面等待,等到有人出来才能再进去玩。
实现:
信号量同步基于内部计数器,每调用一次acquire(),计数器减1;每调用一次release(),计数器加1.当计数器为0时,acquire()调用被阻塞。这是迪科斯彻(Dijkstra)信号量概念P()和V()的Python实现。信号量同步机制适用于访问像服务器这样的有限资源。
信号量与进程池的概念很像,但是要区分开,信号量涉及到加锁的概念
比如大保健:提前设定好,一个房间只有4个床(计数器现在为4),那么同时只能四个人进来,谁先来的谁先占一个床(acquire,计数器减1),4个床满了之后(计数器为0了),第五个人就要等着,等其中一个人出来(release,计数器加1),他就去占用那个床了。
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from multiprocessing import Process,Semaphore
import time,random
def go_ktv(sem,user):
sem.acquire()
print('%s 占到一间ktv小屋' %user)
time.sleep(random.randint(0,3)) #模拟每个人在ktv中待的时间不同
sem.release()
if __name__ == '__main__':
sem=Semaphore(4)
p_l=[]
for i in range(13):
p=Process(target=go_ktv,args=(sem,'user%s' %i,))
p.start()
p_l.append(p)
for i in p_l:
i.join()
print('============》')
#模拟6个人去ktv唱歌
import time
import random
from multiprocessing import Process,Semaphore
def ktv(i):
print('person %s 进来唱歌了'%i)
time.sleep(random.randint(1,5))
print('person %s 从ktv出去了'%i)
if __name__ == '__main__':
for i in range(6): # 模拟6个人
Process(target=ktv,args=(i,)).start() #结果发现有问题,6个人一拥而入,没有次序乱了
#使用semaphore来设置一次进去的人数,KTV 4个人
import time
import random
from multiprocessing import Process,Semaphore
def ktv(i,sem):
sem.acquire() #取得锁
print('person %s 进来唱歌了'%i)
time.sleep(random.randint(1,5))
print('person %s 从ktv出去了'%i)
sem.release() #释放锁
if __name__ == '__main__':
sem = Semaphore(4) #初始化信号量,数量为4
for i in range(6): # 模拟6个人
Process(target=ktv,args=(i,sem)).start()
#在同一时间,最多有4个人进去
acquire()是一个阻塞行为,信号量和锁有点类似
那么它们之间的区别在于:
信号量,相当于计数器
它是锁+计数器
调用acquire() 计数器-1
当计数器到 0 时,再调用 acquire() 就会阻塞,直到其他线程来调用release()
调用release() 计数器+1
三、事件
四、进程池
五、死锁(进程)
在多道程序系统中,由于多个进程的并发执行,改善了系统资源的利用率并提高了系统的处理能力。然而,多个进程的并发执行也带来了新的问题——死锁。所谓死锁是指多个进程因竞争资源而造成的一种僵局,若无外力作用,这些进程都将无法向前推进。
出现的原因:
代码示例
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from threading import Thread,Lock
import time
mutexA=Lock()
mutexB=Lock()
class MyThread(Thread):
def run(self):
self.func1()
self.func2()
def func1(self):
mutexA.acquire()
print('\033[41m%s 拿到A锁\033[0m' %self.name)
mutexB.acquire()
print('\033[42m%s 拿到B锁\033[0m' %self.name)
mutexB.release()
mutexA.release()
def func2(self):
mutexB.acquire()
print('\033[43m%s 拿到B锁\033[0m' %self.name)
time.sleep(2)
mutexA.acquire()
print('\033[44m%s 拿到A锁\033[0m' %self.name)
mutexA.release()
mutexB.release()
if __name__ == '__main__':
for i in range(5):
t=MyThread()
t.start()
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