Asynchronous task queueing with Twisted: threaded, multicore, and remote.
AsynQueue provides asynchronous task queueing based on the Twisted framework, with task prioritization and a powerful worker interface. Worker implementations are included for running tasks asynchronously in the main thread, in separate threads, and in separate Python interpreters (multiprocessing).
Includes deferred iteration capability: Calling a task that returns an iterator can return a Deferator instead, which does the iteration in a Twisted-friendly fashion, even over a network connection. You can also supply an object conforming to Twisted's IConsumer interface and iterations will be fed to it as they become available.
The util module contains a DeferredTracker object that makes the import worthwhile all on its own. You can use its put method to track Twisted Deferred objects without inserting anything into their callback chains. Then you can wait in non-blocking Twisted fashion for all, any, or some of the tracked deferreds to fire (again, without getting tangled up with any of their callbacks) using the tracker's deferToAll, deferToAny, and deferUntilFewer methods.
There's a detailed usage example below. Also, see the one provided in
the example package "mcmandelbrot" that accompanies this README file
and installs with asynqueue. There is a console entry point for
mcm. Give it a try and see what asynchronous multiprocessing can do.
Multicore Made Easy
Here is a simplified example of how I use it to get a few CPU cores parsing logfiles in the logalyzer package:
class Reader: # Maximum number of logfiles to process concurrently N = 6 # <Stuff...> @defer.inlineCallbacks def run(self): def dispatch(fileName): filePath = self.pathInDir(fileName) # Get a ProcessConsumer for this file consumer = self.rk.consumerFactory(fileName) self.consumers.append(consumer) # Call the ProcessReader on one of my subordinate # processes to have it feed the consumer with # misbehaving IP addresses and filtered records return self.pq.call( self.pr, filePath, consumer=consumer).addCallback(done) def done(consumer): self.consumers.remove(consumer) dList =  self.lock.acquire() self.pq = asynqueue.ProcessQueue(3) # We have at most two files being parsed concurrently for each # worker servicing my process queue ds = defer.DeferredSemaphore(min([self.N, 2*len(self.pq)])) # "Wait" for everything to start up and get a list of # known-bad IP addresses ipList = yield self.rk.startup() # Warn workers to ignore records from the bad IPs yield self.pq.update(self.pr.ignoreIPs, ipList) # Dispatch files as permitted by the semaphore for fileName in self.fileNames: if not self.isRunning(): break # "Wait" for the number of concurrent parsings to fall # back to the limit yield ds.acquire() # If not running, break out of the loop if not self.isRunning(): break # References to the deferreds from dispatch calls are # stored in the process queue, and we wait for their # results. d = dispatch(fileName) d.addCallback(lambda _: ds.release()) dList.append(d) yield defer.DeferredList(dList) ipList = self.rk.getNewIPs() defer.returnValue(ipList) # Can now shut down, regularly or due to interruption self.lock.release()
In fact, each call to the reader object's task queue,
subclass instance, passes along a reference to
self.pr as a
callable. But that's not a problem, even over the interprocess
pipe. Python's built-in
multiprocessing module pickles the
reference very efficiently, and almost no CPU time is spent doing so.
Everything done by each subordinate Python process is contained in the following two methods of its copy of the process.ProcessUniverse object:
def next(self, ID): if ID in self.iterators: try: value = self.iterators[ID].next() except StopIteration: del self.iterators[ID] return None, False return value, True return None, False def loop(self, connection): while True: # Wait here for the next call callSpec = connection.recv() if callSpec is None: # Termination call, no reply expected; just exit the # loop break elif isinstance(callSpec, str): # A next-iteration call connection.send(self.next(callSpec)) else: # A task call status, result = self.runner(callSpec) if status == 'i': # Due to the pipe between worker and process, we # hold onto the iterator here and just # return an ID to it ID = str(hash(result)) self.iterators[ID] = result result = ID connection.send((status, result)) # Broken out of loop, ready for the process to end connection.close()
Bridging the Blocking Gap
Yes, the process blocks when it waits for the next call with
connection.recv. No a problem in this case, because it's not running
Twisted; the subordinate Python interpreter's whole purpose in life is
to run tasks sent to it via the task queue. And on the main
Twisted-running interpreter, here's what
does. Note the magic that happens in the line with
@defer.inlineCallbacks def run(self, task): if task is None: # A termination task, do after pending tasks are done yield self.dLock.acquire() self.cMain.send(None) # Wait (a very short amount of time) for the process loop # to exit self.process.join() self.dLock.release() else: # A regular task self.tasks.append(task) yield self.dLock.acquire(task.priority <= -20) # Our turn! #------------------------------------------------------------------ consumer = task.callTuple.pop('consumer', None) self.cMain.send(task.callTuple) # "Wait" here (in Twisted-friendly fashion) for a response # from the process yield self.delay.untilEvent(self.cMain.poll) status, result = self.cMain.recv() self.dLock.release() if status == 'i': # What we get from the process is an ID to an iterator # it is holding onto, but we need to hook up to it # with a Prefetcherator and then make a Deferator, # which we will either return to the caller or couple # to a consumer provided by the caller. ID = result pf = iteration.Prefetcherator(ID) ok = yield pf.setup(self.next, ID) if ok: result = iteration.Deferator(pf) if consumer: result = iteration.IterationProducer(result, consumer) else: # The process returned an iterator, but it's not # one I could prefetch from. Probably empty. result =  if task in self.tasks: self.tasks.remove(task) task.callback((status, result))
The iteration.Delay object has this very cool capability of providing a Deferred that fires after an event happens. It checks whatever no-argument callable you provide to see if the event has happened yet, and fires the Deferred if so. If not, it waits a while and checks again, with exponential back off to keep the interval between checks approximately proportionate to the amount of time that's passed. It's efficient and works very well.
The main Reader object running on the main Python interpreter also has
object, referenced by
self.rk, that can provide implementors of
twisted.internet.interfaces.IConsumer. Those consumer objects
receive the iterations that are produced by
iteration.IterationProducer instances, iterating asynchronously
"over the wire" (actually, over the interprocess connection pipe).
Copyright (C) 2006-2007, 2015, 2018-19 by Edwin A. Suominen
See edsuom.com for API documentation as well as information about Ed's background and other projects, software and otherwise.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
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