Twisted是基于异步模式的开发框架,因而利用Twisted进行非阻塞编程自然也是必会的用法,下面我们就来一起看一下使用Python的Twisted框架编写非阻塞程序的代码示例:
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# For years we thought this was all there was... We kept hiring more
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# developers, more managers and buying servers. We were trying harder
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# optimising processes and fire-fighting while getting mediocre
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# performance in return. Till luckily one day our hosting
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# company decided to increase their fees and we decided to
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# switch to Twisted Ltd.!
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from twisted.internet import reactor
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from twisted.internet import defer
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from twisted.internet import task
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# Twisted has a slightly different approach
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def schedule_install(customer):
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# They are calling us back when a Wordpress installation completes.
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# They connected the caller recognition system with our CRM and
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# we know exactly what a call is about and what has to be done next.
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#
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# We now design processes of what has to happen on certain events.
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def schedule_install_wordpress():
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def on_done():
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print "Callback: Finished installation for", customer
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print "Scheduling: Installation for", customer
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return task.deferLater(reactor, 3, on_done)
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#
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def all_done(_):
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print "All done for", customer
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#
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# For each customer, we schedule these processes on the CRM
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# and that
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# is all our chief-Twisted developer has to do
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d = schedule_install_wordpress()
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d.addCallback(all_done)
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#
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return d
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# Yes, we don't need many developers anymore or any synchronization.
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# ~~ Super-powered Twisted developer ~~
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def twisted_developer_day(customers):
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print "Goodmorning from Twisted developer"
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#
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# Here's what has to be done today
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work = [schedule_install(customer) for customer in customers]
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# Turn off the lights when done
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join = defer.DeferredList(work)
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join.addCallback(lambda _: reactor.stop())
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#
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print "Bye from Twisted developer!"
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# Even his day is particularly
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twisted_developer_day(["Customer %d" % i for i in xrange(15)])
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# Reactor, our secretary uses the CRM and follows-up on
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reactor.run()
运行结果:
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------ Running example 3 ------
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Goodmorning from Twisted developer
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Scheduling: Installation for Customer 0
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....
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Scheduling: Installation for Customer 14
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Bye from Twisted developer!
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Callback: Finished installation for Customer 0
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All done for Customer 0
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Callback: Finished installation for Customer 1
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All done for Customer 1
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...
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All done for Customer 14
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* 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:
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# Twisted gave us utilities that make our code way more
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@defer.inlineCallbacks
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def inline_install(customer):
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print "Scheduling: Installation for", customer
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yield task.deferLater(reactor, 3, lambda: None)
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print "Callback: Finished installation for", customer
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print "All done for", customer
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def twisted_developer_day(customers):
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... same as previously but using inline_install() instead of schedule_install()
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twisted_developer_day(["Customer %d" % i for i in xrange(15)])
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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):
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@defer.inlineCallbacks
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def inline_install(customer):
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... same as above
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# The new "problem" is that we have to manage all this concurrency to
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# avoid causing problems to others, but this is a nice problem to have.
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def twisted_developer_day(customers):
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print "Goodmorning from Twisted developer"
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work = (inline_install(customer) for customer in customers)
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#
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# We use the Cooperator mechanism to make the secretary not
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# service more than 5 customers simultaneously.
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coop = task.Cooperator()
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join = defer.DeferredList([coop.coiterate(work) for i in xrange(5)])
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#
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join.addCallback(lambda _: reactor.stop())
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print "Bye from Twisted developer!"
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twisted_developer_day(["Customer %d" % i for i in xrange(15)])
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reactor.run()
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# We are now more lean than ever, our customers happy, our hosting
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# bills ridiculously low and our performance stellar.
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# ~*~ THE END ~*~
运行结果:
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$ ./deferreds.py 5
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------ Running example 5 ------
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Goodmorning from Twisted developer
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Bye from Twisted developer!
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Scheduling: Installation for Customer 0
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...
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Callback: Finished installation for Customer 4
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All done for Customer 4
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Scheduling: Installation for Customer 5
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...
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Callback: Finished installation for Customer 14
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All done for Customer 14
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* 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
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..
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# 代码片段
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def dataReceived(self, data):
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now = int(time.time())
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for ftype, data in self.fpcodec.feed(data):
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if ftype == 'oob':
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self.msg('OOB:', repr(data))
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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)
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self.msg('FP.PONG:', repr(data))
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else:
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self.msg('TODO:', (ftype, data))
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d = deferToThread(self.redis.zadd, "beier:fpstat:fps", now, self.devid)
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d.addCallback(self._doResult, extra)
下面这儿完整的例子可以给大家参考一下
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# -*- coding: utf-8 -*-
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from twisted.internet import defer, reactor
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from twisted.internet.threads import deferToThread
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import functools
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import time
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# 耗时操作 这是一个同步阻塞函数
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def mySleep(timeout):
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time.sleep(timeout)
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# 返回值相当于加进了callback里
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return 3
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def say(result):
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print "耗时操作结束了, 并把它返回的结果给我了", result
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# 用functools.partial包装一下, 传递参数进去
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cb = functools.partial(mySleep, 3)
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d = deferToThread(cb)
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d.addCallback(say)
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print "你还没有结束我就执行了, 哈哈"
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reactor.run()
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