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From: |
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- http://www.laurentluce.com/posts/python-threads-synchronization-locks-rlocks-semaphores-conditions-events-and-queues/ |
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- http://yoyzhou.github.io/blog/2013/02/28/python-threads-synchronization-locks/ |
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- http://blog.chinaunix.net/uid-429659-id-3186991.html |
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- http://blog.csdn.net/yidangui/article/details/8707187 |
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- http://blog.csdn.net/yidangui/article/details/8707205 |
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- http://blog.csdn.net/yidangui/article/details/8707209 |
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- http://blog.csdn.net/yidangui/article/details/8707197 |
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## threads: Python threads synchronization: Locks, RLocks, Semaphores, Conditions, Events and Queues. |
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### threading简介 |
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python是支持多线程的,并且是native的线程。主要是通过thread和threading这两个模块来实现的。 |
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#### 实现模块 |
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- thread:多线程的底层支持模块,一般不建议使用; |
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- threading:对thread进行了封装,将一些线程的操作对象化。 |
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#### threading模块 |
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- Timer与Thread类似,但要等待一段时间后才开始运行; |
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- Lock 锁原语,这个我们可以对全局变量互斥时使用; |
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- RLock 可重入锁,使单线程可以再次获得已经获得的锁; |
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- Condition 条件变量,能让一个线程停下来,等待其他线程满足某个“条件”; |
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- Event 通用的条件变量。多个线程可以等待某个事件发生,在事件发生后,所有的线程都被激活; |
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- Semaphore为等待锁的线程提供一个类似“等候室”的结构; |
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- BoundedSemaphore 与semaphore类似,但不允许超过初始值; |
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- Queue:实现了多生产者(Producer)、多消费者(Consumer)的队列,支持锁原语,能够在多个线程之间提供很好的同步支持。 |
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thread是比较底层的模块,threading是对thread做了一些包装的,可以更加方便的被使用。创建thread的方式有: |
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- 第一种方式:创建一个threading.Thread()的实例对象,给它一个函数。在它的初始化函数(__init__)中将可调用对象作为参数传入 |
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- 第二种方式:创建一个threading.Thread的实例,传给它一个可调用类对象,类中使用__call__()函数调用函数 |
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- 第三种方式:是通过继承Thread类,重写它的run方法; |
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第一种和第三种常用。 |
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第一种方式举例: |
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``` |
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#coding=utf-8 |
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import threading |
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def thread_fun(num): |
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for n in range(0, int(num)): |
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print " I come from %s, num: %s" %( threading.currentThread().getName(), n) |
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def main(thread_num): |
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thread_list = list(); |
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# 先创建线程对象 |
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for i in range(0, thread_num): |
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thread_name = "thread_%s" %i |
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thread_list.append(threading.Thread(target = thread_fun, name = thread_name, args = (20,))) |
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# 启动所有线程 |
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for thread in thread_list: |
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thread.start() |
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# 主线程中等待所有子线程退出 |
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for thread in thread_list: |
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thread.join() |
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if __name__ == "__main__": |
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main(3) |
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``` |
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第三种方式举例1: |
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``` |
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#!/usr/bin/env python |
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import threading |
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import time |
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count=1 |
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class KissThread(threading.Thread): |
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def run(self): |
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global count |
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print "Thread # %s:Pretending to do stuff" % count |
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count+=1 |
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time.sleep(2) |
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print "done with stuff" |
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for t in range(5): |
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KissThread().start() |
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``` |
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第三种方式举例2: |
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``` |
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import threading |
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class MyThread(threading.Thread): |
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def __init__(self): |
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threading.Thread.__init__(self) |
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def run(self): |
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print "I am %s" % (self.name) |
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if __name__ == "__main__": |
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for i in range(0, 5): |
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my_thread = MyThread() |
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my_thread.start() |
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``` |
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### Thread类常用方法 |
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#### getName(self) |
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返回线程的名字 |
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#### setName方法 |
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可以指定每一个thread的name |
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``` |
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def __init__(self): |
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threading.Thread.__init__(self) |
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self.setName("new" + self.name) |
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``` |
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#### isAlive(self) |
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布尔标志,表示这个线程是否还在运行中 |
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#### isDaemon(self) |
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返回线程的daemon标志 |
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#### run(self) |
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定义线程的功能函数 |
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#### start方法 |
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启动线程 |
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#### join方法 |
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join方法原型如下,这个方法是用来程序挂起,直到线程结束,如果给出timeout,则最多阻塞timeout秒 |
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``` |
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def join(self, timeout=None): |
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``` |
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#### setDaemon方法 |
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当我们在程序运行中,执行一个主线程,如果主线程又创建一个子线程,主线程和子线程就分兵两路,当主线程完成想退出时,会检验子线程是否完成。如果子线程未完成,则主线程会等待子线程完成后再退出。但是有时候我们需要的是,只要主线程完成了,不管子线程是否完成,都要和主线程一起退出,这时就可以用setDaemon方法,并设置其参数为True。 |
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### Queue提供的类 |
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- Queue队列 |
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- LifoQueue后入先出(LIFO)队列 |
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- PriorityQueue 优先队列 |
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### 互斥锁 |
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Python编程中,引入了对象互斥锁的概念,来保证共享数据操作的完整性。每个对象都对应于一个可称为" 互斥锁" 的标记,这个标记用来保证在任一时刻,只能有一个线程访问该对象。在Python中我们使用threading模块提供的Lock类。添加一个互斥锁变量mutex = threading.Lock(),然后在争夺资源的时候之前我们会先抢占这把锁mutex.acquire(),对资源使用完成之后我们在释放这把锁mutex.release()。 |
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当一个线程调用Lock对象的acquire()方法获得锁时,这把锁就进入“locked”状态。因为每次只有一个线程可以获得锁,所以如果此时另一个线程试图获得这个锁,该线程就会变为同步阻塞状态。直到拥有锁的线程调用锁的release()方法释放锁之后,该锁进入“unlocked”状态。线程调度程序从处于同步阻塞状态的线程中选择一个来获得锁,并使得该线程进入运行(running)状态。 |
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``` |
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import threading |
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import time |
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counter = 0 |
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mutex = threading.Lock() |
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class MyThread(threading.Thread): |
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def __init__(self): |
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threading.Thread.__init__(self) |
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def run(self): |
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global counter, mutex |
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time.sleep(1); |
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if mutex.acquire(): |
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counter += 1 |
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print "I am %s, set counter:%s" % (self.name, counter) |
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mutex.release() |
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if __name__ == "__main__": |
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for i in range(0, 100): |
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my_thread = MyThread() |
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my_thread.start() |
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``` |
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### Condition条件变量 |
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Python提供的Condition对象提供了对复杂线程同步问题的支持。Condition被称为条件变量,除了提供与Lock类似的acquire和release方法外,还提供了wait和notify方法。使用Condition的主要方式为:线程首先acquire一个条件变量,然后判断一些条件。如果条件不满足则wait;如果条件满足,进行一些处理改变条件后,通过notify方法通知其他线程,其他处于wait状态的线程接到通知后会重新判断条件。不断的重复这一过程,从而解决复杂的同步问题。 |
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另外:Condition对象的构造函数可以接受一个Lock/RLock对象作为参数,如果没有指定,则Condition对象会在内部自行创建一个RLock;除了notify方法外,Condition对象还提供了notifyAll方法,可以通知waiting池中的所有线程尝试acquire内部锁。由于上述机制,处于waiting状态的线程只能通过notify方法唤醒,所以notifyAll的作用在于防止有线程永远处于沉默状态。 |
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#### “生产者-消费者”模型 |
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代码中主要实现了生产者和消费者线程,双方将会围绕products来产生同步问题,首先是2个生成者生产products ,而接下来的4个消费者将会消耗products. |
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实现举例: |
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``` |
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#coding=utf-8 |
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#!/usr/bin/env python |
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import threading |
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import time |
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condition = threading.Condition() |
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products = 0 |
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class Producer(threading.Thread): |
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def __init__(self): |
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threading.Thread.__init__(self) |
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def run(self): |
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global condition, products |
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while True: |
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if condition.acquire(): |
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if products < 10: |
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products += 1; |
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print "Producer(%s):deliver one, now products:%s" %(self.name, products) |
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condition.notify() |
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else: |
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print "Producer(%s):already 10, stop deliver, now products:%s" %(self.name, products) |
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condition.wait(); |
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condition.release() |
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time.sleep(1) |
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class Consumer(threading.Thread): |
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def __init__(self): |
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threading.Thread.__init__(self) |
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def run(self): |
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global condition, products |
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while True: |
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if condition.acquire(): |
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if products > 1: |
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products -= 1 |
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print "Consumer(%s):consume one, now products:%s" %(self.name, products) |
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condition.notify() |
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else: |
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print "Consumer(%s):only 1, stop consume, products:%s" %(self.name, products) |
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condition.wait(); |
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condition.release() |
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time.sleep(2) |
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if __name__ == "__main__": |
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for p in range(0, 2): |
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p = Producer() |
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p.start() |
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for c in range(0, 4): |
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c = Consumer() |
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c.start() |
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``` |
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### 信号量semaphore |
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semaphore是一个变量,控制着对公共资源或者临界区的访问。信号量维护着一个计数器,指定可同时访问资源或者进入临界区的线程数。每次有一个线程获得信号量时,计数器-1。若计数器为0,其他线程就停止访问信号量,直到另一个线程释放信号量。 |
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``` |
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#coding=utf-8 |
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import threading |
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import random |
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import time |
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class SemaphoreThread(threading.Thread): |
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"""class using semaphore""" |
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availableTables=['A','B','C','D','E'] |
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def __init__(self,threadName,semaphore): |
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"""initialize thread""" |
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threading.Thread.__init__(self,name=threadName) |
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self.sleepTime=random.randrange(1,6) |
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#set the semaphore as a data attribute of the class |
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self.threadSemaphore=semaphore |
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def run(self): |
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"""Print message and release semaphore""" |
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#acquire the semaphore |
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self.threadSemaphore.acquire() |
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#remove a table from the list |
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table=SemaphoreThread.availableTables.pop() |
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print "%s entered;seated at table %s." %(self.getName(),table), |
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print SemaphoreThread.availableTables |
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time.sleep(self.sleepTime) |
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#free a table |
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print " %s exiting;freeing table %s." %(self.getName(),table), |
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SemaphoreThread.availableTables.append(table) |
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print SemaphoreThread.availableTables |
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#release the semaphore after execution finishes |
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self.threadSemaphore.release() |
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threads=[] #list of threads |
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#semaphore allows five threads to enter critical section |
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threadSemaphore=threading.Semaphore(len(SemaphoreThread.availableTables)) |
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#创建一个threading.Semaphore对象,他最多允许5个线程访问临界区。 |
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#Semaphore类的一个对象用计数器跟踪获取和释放信号量的线程数量。 |
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#create ten threads |
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for i in range(1,11): |
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threads.append(SemaphoreThread("thread"+str(i),threadSemaphore)) |
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#创建一个列表,该列表由SemaphoreThread对象构成,start方法开始列表中的每个线程 |
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#start each thread |
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for thread in threads: |
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thread.start() |
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``` |
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SemaphoreThread类的每个对象代表饭馆里的一个客人。类属性availableTables跟踪饭馆中可用的桌子。 |
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信号量有个内建的计数器,用于跟踪他的acquire和release方法调用的次数。内部计数器的初始值可作为参数传给Semaphore构造函数。默认值为1.计数器大于0,Semaphore的acquire方法就为线程获得信号量,并计数器自减。 |
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### 死锁现象 |
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所谓死锁: 是指两个或两个以上的进程在执行过程中,因争夺资源而造成的一种互相等待的现象,若无外力作用,它们都将无法推进下去。此时称系统处于死锁状态或系统产生了死锁,这些永远在互相等待的进程称为死锁进程。 由于资源占用是互斥的,当某个进程提出申请资源后,使得有关进程在无外力协助下,永远分配不到必需的资源而无法继续运行,这就产生了一种特殊现象死锁。 |
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``` |
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import threading |
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counterA = 0 |
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counterB = 0 |
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mutexA = threading.Lock() |
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mutexB = threading.Lock() |
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class MyThread(threading.Thread): |
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def __init__(self): |
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threading.Thread.__init__(self) |
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def run(self): |
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self.fun1() |
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self.fun2() |
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def fun1(self): |
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global mutexA, mutexB |
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if mutexA.acquire(): |
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print "I am %s , get res: %s" %(self.name, "ResA") |
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if mutexB.acquire(): |
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print "I am %s , get res: %s" %(self.name, "ResB") |
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mutexB.release() |
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mutexA.release() |
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def fun2(self): |
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global mutexA, mutexB |
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if mutexB.acquire(): |
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print "I am %s , get res: %s" %(self.name, "ResB") |
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if mutexA.acquire(): |
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print "I am %s , get res: %s" %(self.name, "ResA") |
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mutexA.release() |
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mutexB.release() |
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if __name__ == "__main__": |
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|
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for i in range(0, 100): |
|
|
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my_thread = MyThread() |
|
|
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my_thread.start() |
|
|
|
``` |