Python多线程同步教程

概述

  • 多线程给我们带来的好处是可以并发的执行多个任务,特别是对于I/O密集型的业务,使用多线程,可以带来成倍的性能增长。
  • 可是当我们多个线程需要修改同一个数据,在不做任何同步控制的情况下,产生的结果往往是不可预料的,比如两个线程,一个输出hello,一个输出world,实际运行的结果,往往可能是一个是hello world,一个是world hello。
  • python里提供了多个用于控制多线程同步的同步原语,这些原语,包含在python的标准库threading.py当中。我今天简单的介绍一下python里的这些控制多线程同步的原语,包括:Locks、RLocks、Semaphores、Events、Conditions和Barriers,你也可以继承这些类,实现自己的同步控制原语。

Lock(锁)

  • Locks是python里最简单的同步原语,只包括两个状态:locked和unlocked,刚创建时状态是unlocked。Locks有两个方法,acquire和release。acquire方法加锁,release方法释放锁,如果acquire枷锁失败,则阻塞,表明其他线程已经加锁。release方法只有当状态是locked调用方法True,如果是unlocked状态,调用release方法会抛出RunTimeError异常。例如代码:

    from threading import Lock, Thread
    lock = Lock()
    g = 0
    
    def add_one():
       """
       Just used for demonstration. It’s bad to use the ‘global’
       statement in general.
       """
       global g
       lock.acquire()
       g += 1
       lock.release()
    
    def add_two():
       global g
       lock.acquire()
       g += 2
       lock.release()
    
    threads = []
    for func in [add_one, add_two]:
       threads.append(Thread(target=func))
       threads[-1].start()
    
    for thread in threads:
       """
       Waits for threads to complete before moving on with the main
       script.
       """
       thread.join()
    
    print(g)
    
  • 最终输出的结果是3,通过Lock的使用,虽然在两个线程中修改了同一个全局变量,但两个线程是顺序计算出结果的。

RLock(循环锁)

  • 上面的Lock对象虽然能达到同步的效果,但是无法得知当前是那个线程获取到了锁。如果锁没被释放,则其他获取这个锁的线程都会被阻塞住。如果不想阻塞,可以使用RLock,例如:
    # 使用Lock
    import threading
    num = 0
    lock = Threading.Lock()
    
    lock.acquire()
    num += 1
    lock.acquire() # 这个地方阻塞
    num += 2
    lock.release()
    
    # 使用RLock
    lock = Threading.RLock()
    lock.acquire()
    num += 3
    lock.acquire() # 这不会阻塞
    num += 4
    lock.release()
    lock.release() # 这个地方注意是释放两次锁
    

Semaphores

  • Semaphores是个最简单的计数器,有两个方法acquire()和release(),如果有多个线程调用acquire()方法,acquire()方法会阻塞住,每当调用次acquire方法,就做一次减1操作,每当release()方法调用此次,就加1,如果最后的计数数值大于调用acquire()方法的线程数目,release()方法会抛出ValueError异常。下面是个生产者消费者的示例。
    import random, time
    from threading import BoundedSemaphore, Thread
    max_items = 5
    container = BoundedSemaphore(max_items)
    def producer(nloops):
        for i in range(nloops):
            time.sleep(random.randrange(2, 5))
            print(time.ctime(), end=": ")
            try:
                container.release()
                print("Produced an item.")
            except ValueError:
                print("Full, skipping.")
    def consumer(nloops):
        for i in range(nloops):
            time.sleep(random.randrange(2, 5))
            print(time.ctime(), end=": ")
            if container.acquire(False):
                print("Consumed an item.")
            else:
                print("Empty, skipping.")
    threads = []
    nloops = random.randrange(3, 6)
    print("Starting with %s items." % max_items)
    threads.append(Thread(target=producer, args=(nloops,)))
    threads.append(Thread(target=consumer, args=(random.randrange(nloops, nloops+max_items+2),)))
    for thread in threads:  # Starts all the threads.
        thread.start()
    for thread in threads:  # Waits for threads to complete before moving on with the main script.
        thread.join()
    print("All done.")
    

  • threading模块还提供了一个Semaphore对象,它允许你可以任意次的调用release函数,但是最好还是使用BoundedSemaphore对象,这样在release调用次数过多时会报错,有益于查找错误。Semaphores最长用来限制资源的使用,比如最多十个进程。

Events

  • event可以充当多进程之间的通信工具,基于一个内部的标志,线程可以调用set()和clear()方法来操作这个标志,其他线程则阻塞在wait()函数,直到标志被设置为True。下面的代码展示了如何利用Events来追踪行为。
    import random, time
    from threading import Event, Thread
    
    event = Event()
    
    def waiter(event, nloops):
        for i in range(nloops):
        print(%s. Waiting for the flag to be set. % (i+1))
        event.wait() # Blocks until the flag becomes true.
        print(Wait complete at:, time.ctime())
        event.clear() # Resets the flag.
        print()
    
    def setter(event, nloops):
        for i in range(nloops):
        time.sleep(random.randrange(2, 5)) # Sleeps for some time.
        event.set()
    
    threads = []
    nloops = random.randrange(3, 6)
    
    threads.append(Thread(target=waiter, args=(event, nloops)))
    threads[-1].start()
    threads.append(Thread(target=setter, args=(event, nloops)))
    threads[-1].start()
    
    for thread in threads:
        thread.join()
    
    print(All done.)
    

Conditions

  • conditions是比events更加高级一点的同步原语,可以用户多线程间的通信和通知。比如A线程通知B线程资源已经可以被消费。其他的线程必须在调用wait()方法前调用acquire()方法。同样的,每个线程在资源使用完以后,要调用release()方法,这样其他线程就可以继续执行了。下面是使用conditions实现的一个生产者消费者的例子。
    import random, time
    from threading import Condition, Thread
    condition = Condition()
    box = []
    def producer(box, nitems):
        for i in range(nitems):
            time.sleep(random.randrange(2, 5))  # Sleeps for some time.
            condition.acquire()
            num = random.randint(1, 10)
            box.append(num)  # Puts an item into box for consumption.
            condition.notify()  # Notifies the consumer about the availability.
            print("Produced:", num)
            condition.release()
    def consumer(box, nitems):
        for i in range(nitems):
            condition.acquire()
            condition.wait()  # Blocks until an item is available for consumption.
            print("%s: Acquired: %s" % (time.ctime(), box.pop()))
            condition.release()
    threads = []
    nloops = random.randrange(3, 6)
    for func in [producer, consumer]:
        threads.append(Thread(target=func, args=(box, nloops)))
        threads[-1].start()  # Starts the thread.
    for thread in threads:
        thread.join()
    print("All done.")
    

  • conditions还有其他很多用户,比如实现一个数据流API,当数据准备好了可以通知其他线程去处理数据。

Barriers

  • barriers是个简单的同步原语,可以用户多个线程之间的相互等待。每个线程都调用wait()方法,然后阻塞,直到所有线程调用了wait(),然后所有线程同时开始运行。例如:
    from random import randrange
    from threading import Barrier, Thread
    from time import ctime, sleep
    
    num = 4
    b = Barrier(num)
    names = [Harsh, Lokesh, George, Iqbal]
    
    def player():
        name = names.pop()
        sleep(randrange(2, 5))
        print(%s reached the barrier at: %s % (name, ctime()))
        b.wait()
    
    threads = []
    print(Race starts now…”)
    
    for i in range(num):
        threads.append(Thread(target=player))
        threads[-1].start()
    for thread in threads:
        thread.join()
    print()
    print(Race over!”)
    

总结

  • 多线程同步,说难也难,说不难也很容易,关键是要看你的业务场景和解决问题的思路,尽量降低多线程之间的依赖,理清楚业务流程,选择合适的方法,则事尽成。

Comments