使用Python的Twisted框架编写非阻塞程序的代码示例

先来看一段代码：

    # ~*~ Twisted - A Python tale ~*~

    from time import sleep

    # Hello, I'm a developer and I mainly setup Wordpress.
    def install_wordpress(customer):
      # Our hosting company Threads Ltd. is bad. I start installation and...
      print "Start installation for", customer
      # ...then wait till the installation finishes successfully. It is
      # boring and I'm spending most of my time waiting while consuming
      # resources (memory and some CPU cycles). It's because the process
      # is *blocking*.
      sleep(3)
      print "All done for", customer

    # I do this all day long for our customers
    def developer_day(customers):
      for customer in customers:
        install_wordpress(customer)

    developer_day(["Bill", "Elon", "Steve", "Mark"])

运行一下，结果如下所示：

    $ ./deferreds.py 1

    ------ Running example 1 ------
    Start installation for Bill
    All done for Bill
    Start installation
    ...
    * Elapsed time: 12.03 seconds

这是一段顺序执行的代码。四个消费者，为一个人安装需要3秒的时间，那么四个人就是12秒。这样处理不是很令人满意，所以看一下第二个使用了线程的例子：

    import threading

    # The company grew. We now have many customers and I can't handle the
    # workload. We are now 5 developers doing exactly the same thing.
    def developers_day(customers):
      # But we now have to synchronize... a.k.a. bureaucracy
      lock = threading.Lock()
      #
      def dev_day(id):
        print "Goodmorning from developer", id
        # Yuck - I hate locks...
        lock.acquire()
        while customers:
          customer = customers.pop(0)
          lock.release()
          # My Python is less readable
          install_wordpress(customer)
          lock.acquire()
        lock.release()
        print "Bye from developer", id
      # We go to work in the morning
      devs = [threading.Thread(target=dev_day, args=(i,)) for i in range(5)]
      [dev.start() for dev in devs]
      # We leave for the evening
      [dev.join() for dev in devs]

    # We now get more done in the same time but our dev process got more
    # complex. As we grew we spend more time managing queues than doing dev
    # work. We even had occasional deadlocks when processes got extremely
    # complex. The fact is that we are still mostly pressing buttons and
    # waiting but now we also spend some time in meetings.
    developers_day(["Customer %d" % i for i in xrange(15)])

运行一下：

    $ ./deferreds.py 2

    ------ Running example 2 ------
    Goodmorning from developer 0Goodmorning from developer
    1Start installation forGoodmorning from developer 2
    Goodmorning from developer 3Customer 0
    ...
    from developerCustomer 13 3Bye from developer 2
    * Elapsed time: 9.02 seconds

这次是一段并行执行的代码，使用了5个工作线程。15个消费者每个花费3s意味着总共45s的时间，不过用了5个线程并行执行总共只花费了9s的时间。这段代码有点复杂，很大一部分代码是用于管理并发，而不是专注于算法或者业务逻辑。另外，程序的输出结果看起来也很混杂，可读性也天津市。即使是简单的多线程的代码同样也难以写得很好，所以我们转为使用Twisted：

    # For years we thought this was all there was... We kept hiring more
    # developers, more managers and buying servers. We were trying harder
    # optimising processes and fire-fighting while getting mediocre
    # performance in return. Till luckily one day our hosting
    # company decided to increase their fees and we decided to
    # switch to Twisted Ltd.!

    from twisted.internet import reactor
    from twisted.internet import defer
    from twisted.internet import task

    # Twisted has a slightly different approach
    def schedule_install(customer):
      # They are calling us back when a Wordpress installation completes.
      # They connected the caller recognition system with our CRM and
      # we know exactly what a call is about and what has to be done next.
      #
      # We now design processes of what has to happen on certain events.
      def schedule_install_wordpress():
          def on_done():
            print "Callback: Finished installation for", customer
        print "Scheduling: Installation for", customer
        return task.deferLater(reactor, 3, on_done)
      #
      def all_done(_):
        print "All done for", customer
      #
      # For each customer, we schedule these processes on the CRM
      # and that
      # is all our chief-Twisted developer has to do
      d = schedule_install_wordpress()
      d.addCallback(all_done)
      #
      return d

    # Yes, we don't need many developers anymore or any synchronization.
    # ~~ Super-powered Twisted developer ~~
    def twisted_developer_day(customers):
      print "Goodmorning from Twisted developer"
      #
      # Here's what has to be done today
      work = [schedule_install(customer) for customer in customers]
      # Turn off the lights when done
      join = defer.DeferredList(work)
      join.addCallback(lambda _: reactor.stop())
      #
      print "Bye from Twisted developer!"
    # Even his day is particularly short!
    twisted_developer_day(["Customer %d" % i for i in xrange(15)])

    # Reactor, our secretary uses the CRM and follows-up on events!
    reactor.run()

运行结果：

    ------ Running example 3 ------
    Goodmorning from Twisted developer
    Scheduling: Installation for Customer 0
    ....
    Scheduling: Installation for Customer 14
    Bye from Twisted developer!
    Callback: Finished installation for Customer 0
    All done for Customer 0
    Callback: Finished installation for Customer 1
    All done for Customer 1
    ...
    All done for Customer 14
    * Elapsed time: 3.18 seconds

这次我们得到了完美的执行代码和可读性强的输出结果，并且没有使用线程。我们并行地处理了15个消费者，也就是说，本来需要45s的执行时间在3s之内就已经完成。这个窍门就是我们把所有的阻塞的对sleep()的调用都换成了Twisted中对等的task.deferLater()和回调函数。由于现在处理的操作在其他地方进行，我们就可以毫不费力地同时服务于15个消费者。
前面提到处理的操作发生在其他的某个地方。现在来解释一下，算术运算仍然发生在CPU内，但是现在的CPU处理速度相比磁盘和网络操作来说非常快。所以给CPU提供数据或者从CPU向内存或另一个CPU发送数据花费了大多数时间。我们使用了非阻塞的操作节省了这方面的时间，例如，task.deferLater()使用了回调函数，当数据已经传输完成的时候会被激活。
另一个很重要的一点是输出中的Goodmorning from Twisted developer和Bye from Twisted developer!信息。在代码开始执行时就已经打印出了这两条信息。如果代码如此早地执行到了这个地方，那么我们的应用真正开始运行是在什么时候呢？答案是，对于一个Twisted应用（包括Scrapy）来说是在reactor.run()里运行的。在调用这个方法之前，必须把应用中可能用到的每个Deferred链准备就绪，然后reactor.run()方法会监视并激活回调函数。
注意，reactor的主要一条规则就是，你可以执行任何操作，只要它足够快并且是非阻塞的。
现在好了，代码中没有那么用于管理多线程的部分了，不过这些回调函数看起来还是有些杂乱。可以修改成这样：

    # Twisted gave us utilities that make our code way more readable!
    @defer.inlineCallbacks
    def inline_install(customer):
      print "Scheduling: Installation for", customer
      yield task.deferLater(reactor, 3, lambda: None)
      print "Callback: Finished installation for", customer
      print "All done for", customer

    def twisted_developer_day(customers):
      ... same as previously but using inline_install() instead of schedule_install()

    twisted_developer_day(["Customer %d" % i for i in xrange(15)])
    reactor.run()

运行的结果和前一个例子相同。这段代码的作用和上一个例子是一样的，但是看起来更加简洁明了。inlineCallbacks生成器可以使用一些一些Python的机制来使得inline_install()函数暂停或者恢复执行。inline_install()函数变成了一个Deferred对象并且并行地为每个消费者运行。每次yield的时候，运行就会中止在当前的inline_install()实例上，直到yield的Deferred对象完成后再恢复运行。
现在唯一的问题是，如果我们不止有15个消费者，而是有，比如10000个消费者时又该怎样？这段代码会同时开始10000个同时执行的序列（比如HTTP请求、数据库的写操作等等）。这样做可能没什么问题，但也可能会产生各种失败。在有巨大并发请求的应用中，例如Scrapy，我们经常需要把并发的数量限制到一个可以接受的程度上。在下面的一个例子中，我们使用task.Cooperator()来完成这样的功能。Scrapy在它的Item Pipeline中也使用了相同的机制来限制并发的数目（即CONCURRENT_ITEMS设置）：

    @defer.inlineCallbacks
    def inline_install(customer):
      ... same as above

    # The new "problem" is that we have to manage all this concurrency to
    # avoid causing problems to others, but this is a nice problem to have.
    def twisted_developer_day(customers):
      print "Goodmorning from Twisted developer"
      work = (inline_install(customer) for customer in customers)
      #
      # We use the Cooperator mechanism to make the secretary not
      # service more than 5 customers simultaneously.
      coop = task.Cooperator()
      join = defer.DeferredList([coop.coiterate(work) for i in xrange(5)])
      #
      join.addCallback(lambda _: reactor.stop())
      print "Bye from Twisted developer!"

    twisted_developer_day(["Customer %d" % i for i in xrange(15)])
    reactor.run()

    # We are now more lean than ever, our customers happy, our hosting
    # bills ridiculously low and our performance stellar.
    # ~*~ THE END ~*~

运行结果：

    $ ./deferreds.py 5
    ------ Running example 5 ------
    Goodmorning from Twisted developer
    Bye from Twisted developer!
    Scheduling: Installation for Customer 0
    ...
    Callback: Finished installation for Customer 4
    All done for Customer 4
    Scheduling: Installation for Customer 5
    ...
    Callback: Finished installation for Customer 14
    All done for Customer 14
    * Elapsed time: 9.19 seconds

从上面的输出中可以看到，程序运行时好像有5个处理消费者的槽。除非一个槽空出来，否则不会开始处理下一个消费者的请求。在本例中，处理时间都是3秒，所以看起来像是5个一批次地处理一样。最后得到的性能跟使用线程是一样的，但是这次只有一个线程，代码也更加简洁更容易写出正确的代码。

PS：deferToThread使同步函数实现非阻塞
wisted的defer.Deferred (from twisted.internet import defer)可以返回一个deferred对象.

注:deferToThread使用线程实现的,不推荐过多使用
把同步函数变为异步(返回一个Deferred)
twisted的deferToThread(from twisted.internet.threads import deferToThread)也返回一个deferred对象,不过回调函数在另一个线程处理,主要用于数据库/文件读取操作

    ..

    # 代码片段

      def dataReceived(self, data):
        now = int(time.time())

        for ftype, data in self.fpcodec.feed(data):
          if ftype == 'oob':
            self.msg('OOB:', repr(data))
          elif ftype == 0x81: # 对服务器请求的心跳应答(这个是解析 防疲劳驾驶仪,发给gps上位机的,然后上位机发给服务器的)
            self.msg('FP.PONG:', repr(data))
          else:
            self.msg('TODO:', (ftype, data))
          d = deferToThread(self.redis.zadd, "beier:fpstat:fps", now, self.devid)
          d.addCallback(self._doResult, extra)

下面这儿完整的例子可以给大家参考一下

    # -*- coding: utf-8 -*-

    from twisted.internet import defer, reactor
    from twisted.internet.threads import deferToThread

    import functools
    import time

    # 耗时操作 这是一个同步阻塞函数
    def mySleep(timeout):
      time.sleep(timeout)

      # 返回值相当于加进了callback里
      return 3 

    def say(result):
      print "耗时操作结束了, 并把它返回的结果给我了", result

    # 用functools.partial包装一下, 传递参数进去
    cb = functools.partial(mySleep, 3)
    d = deferToThread(cb) 
    d.addCallback(say)

    print "你还没有结束我就执行了, 哈哈"

    reactor.run()