[Twisted-Python] Re: Twisted-Python Digest, Vol 45, Issue 18
josef.robert.novak at gmail.com
Sat Dec 15 22:37:27 EST 2007
Hmm. This seems like a pretty nice solution, except that it seems it will
be blocking incoming calls during processing. What I really need to do is
pause the deferToThread objects based on their pid.
How can I obtain the pid of a new deferToThread object? If I can obtain it,
then I can just keep a persistent dictionary containing all the running
compute-intensive processes, and unregister each one in a callback after it
finishes up. Then, every time a new call comes in, I check my persistent
for pid in pid_list:
and every time a call finishes, I check the number of current calls, and
then if we're back to zero, we resume the compute intensive processes again:
if parentObj.live_calls == 0::
for pid in parentObj.pid_list:
so, my new question is...
How can I obtain the pid of a new deferToThread object?
I'm pretty sure that, while not very elegant, this should solve my problem -
but please let me know if this sounds really, cockamamey!
> Message: 2
> Date: Sat, 15 Dec 2007 07:50:02 -0800
> From: Ed Suominen <general at eepatents.com>
> Subject: Re: [Twisted-Python] finer control of deferToThread ?
> To: Twisted general discussion <twisted-python at twistedmatrix.com>
> Message-ID: <4763F7AA.2090800 at eepatents.com>
> Content-Type: text/plain; charset=ISO-8859-1
> You can do priority queuing to one or more threads using AsynQueue,
> http://foss.eepatents.com/AsynQueue or python-asynqueue in Debian. Use
> multiple threads and queue up the compute-intensive calls with a low
> priority and the other stuff with higher priorities.
> However, you will need to chop up your compute-intensive stuff into
> smaller pieces for this to be helpful. (That's good asynchronous
> processing practice, generally.) The priority queuing is only effective
> at deciding which calls to dispatch next, and each thread call is on its
> own once it is dispatched from the queue. Each call uses an entire
> thread for its entire duration, and will keep the queue from dispatching
> anything else to that thread while it's squatting on it, no matter how
> low-priority it is.
> Best regards, Ed
> Josef Novak wrote:
> > Hi,
> > I am writing a twisted application using StarPY fastagi API for
> > Asterisk. My application involves answering user calls, and then
> > based on their responses to an IVR dialogue, running some some
> > compute-intensive applications on the same server, after they hang up.
> > At the moment I am running the compute-intensive application (3rd
> > party code) in a deferred.deferToThread. The application works fine,
> > however if the number of callers goes above 2-3, and one of the
> > compute-intensive applications from a previous call has not finished
> > up, the audio for the call gets very jumpy because of CPU usage.
> > This compute-intensive process needs to be run immediately after
> > hangup, and I'd prefer to take care of everything on the same machine
> > (rather than send the compute-intensive application request somewhere
> > else).
> > What I'd like to do is pause the thread with the
> > deferred.deferToThread process any time a new call comes in (this
> > sort of violates what I'm saying above but the number of calls going
> > to a particular ASterisk trunk is limited so in most cases this would
> > never result in more than a couple-seconds delay, which is
> > acceptable).
> > Is there any way to control these deferred.deferToThread objects in
> > a more fine-grained manner? Say from a reactor factory? Can I
> > register them separately somehow, and then pause these
> > compute-intensive applications temporarily every time a new call comes
> > in? I'm imagining something as simple as as ctrl+z and $ fg linux
> > terminal commands... but I appreciate that it is probably not so
> > straightforward.
> > -Joe
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> End of Twisted-Python Digest, Vol 45, Issue 18
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