点击这里成为作者 · 更新于 2017-11-24 02:00:57

ThreadLocal

我们知道,同一进程的多个线程之间是内存共享的,这意味着,当一个线程对全局变量做了修改,将会影响到其他所有线程,这是很危险的。为了避免多个线程同时修改全局变量,我们就需要对全局变量的修改加锁。

除了对全局变量的修改进行加锁,你可能也想到了可以使用线程自己的局部变量,因为局部变量只有线程自己能看见,对同一进程的其他线程是不可访问的。确实如此,让我们先看一个例子:

from threading import Thread, current_thread

def echo(num):
    print current_thread().name, num

def calc():
    print 'thread %s is running...' % current_thread().name
    local_num = 0
    for _ in xrange(10000):
        local_num += 1
    echo(local_num)
    print 'thread %s ended.' % current_thread().name

if __name__ == '__main__':
    print 'thread %s is running...' % current_thread().name

    threads = []
    for i in range(5):
        threads.append(Thread(target=calc))
        threads[i].start()
    for i in range(5):
        threads[i].join()

    print 'thread %s ended.' % current_thread().name

在上面的代码中,我们创建了 5 个线程,每个线程都对自己的局部变量 local_num 进行 10000 次的加 1 操作。由于对线程局部变量的修改不会影响到其他线程,因此,我们可以看到,每个线程结束时打印的 local_num 的值都为 10000,执行结果如下:

thread MainThread is running...
thread Thread-4 is running...
Thread-4 10000
thread Thread-4 ended.
thread Thread-5 is running...
Thread-5 10000
thread Thread-5 ended.
thread Thread-6 is running...
Thread-6 10000
thread Thread-6 ended.
thread Thread-7 is running...
Thread-7 10000
thread Thread-7 ended.
thread Thread-8 is running...
Thread-8 10000
thread Thread-8 ended.
thread MainThread ended.

上面这种线程使用自己的局部变量的方法虽然可以避免多线程对同一变量的访问冲突,但还是有一些问题。在实际的开发中,我们会调用很多函数,每个函数又有很多个局部变量,这时每个函数都这么传参数显然是不可取的。

为了解决这个问题,一个比较容易想到的做法就是创建一个全局字典,以线程的 ID 作为 key,线程的局部数据作为 value,这样就可以消除函数传参的问题,代码如下:

from threading import Thread, current_thread

global_dict = {}

def echo():
    num = global_dict[current_thread()]    # 线程根据自己的 ID 获取数据
    print current_thread().name, num

def calc():
    print 'thread %s is running...' % current_thread().name

    global_dict[current_thread()] = 0
    for _ in xrange(10000):
        global_dict[current_thread()] += 1
    echo()

    print 'thread %s ended.' % current_thread().name

if __name__ == '__main__':
    print 'thread %s is running...' % current_thread().name

    threads = []
    for i in range(5):
        threads.append(Thread(target=calc))
        threads[i].start()
    for i in range(5):
        threads[i].join()

    print 'thread %s ended.' % current_thread().name

看下执行结果:

thread MainThread is running...
thread Thread-64 is running...
thread Thread-65 is running...
thread Thread-66 is running...
thread Thread-67 is running...
thread Thread-68 is running...
Thread-67 10000
thread Thread-67 ended.
Thread-65 10000
thread Thread-65 ended.
Thread-68 10000
thread Thread-68 ended.
Thread-66 10000
thread Thread-66 ended.
Thread-64 10000
thread Thread-64 ended.
thread MainThread ended.

上面的做法虽然消除了函数传参的问题,但是还是有些不完美,为了获取线程的局部数据,我们需要先获取线程 ID,另外,global_dict 是个全局变量,所有线程都可以对它进行修改,还是有些危险。

那到底如何是好?

事实上,Python 提供了 ThreadLocal 对象,它真正做到了线程之间的数据隔离,而且不用查找 dict,代码如下:

from threading import Thread, current_thread, local

global_data = local()

def echo():
    num = global_data.num
    print current_thread().name, num

def calc():
    print 'thread %s is running...' % current_thread().name

    global_data.num = 0
    for _ in xrange(10000):
        global_data.num += 1
    echo()

    print 'thread %s ended.' % current_thread().name

if __name__ == '__main__':
    print 'thread %s is running...' % current_thread().name

    threads = []
    for i in range(5):
        threads.append(Thread(target=calc))
        threads[i].start()
    for i in range(5):
        threads[i].join()

    print 'thread %s ended.' % current_thread().name

在上面的代码中,global_data 就是 ThreadLocal 对象,你可以把它当作一个全局变量,但它的每个属性,比如 global_data.num 都是线程的局部变量,没有访问冲突的问题。

让我们看下执行结果:

thread MainThread is running...
thread Thread-94 is running...
thread Thread-95 is running...
thread Thread-96 is running...
thread Thread-97 is running...
thread Thread-98 is running...
Thread-96 10000
thread Thread-96 ended.
Thread-97 10000
thread Thread-97 ended.
Thread-95 10000
thread Thread-95 ended.
Thread-98 10000
thread Thread-98 ended.
Thread-94 10000
thread Thread-94 ended.
thread MainThread ended.

小结

  • 使用 ThreadLocal 对象来线程绑定自己独有的数据。

参考资料

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