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。