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From Hussein Elgridly <huss...@broadinstitute.org>
Subject Re: Speeding up Aurora client job creation
Date Tue, 17 Mar 2015 02:52:34 GMT
I'm not quite sure I understand your question, so I'll be painfully
explicit instead.

I don't want to use the existing Aurora client because it's slow (Pystachio
+ repeated HTTP connection overheads, as detailed earlier in this thread).
Instead, I want to use the Thrift interface to talk to the Aurora scheduler
directly - I can skip Pystachio entirely and keep the HTTP connection open).

I cannot use the official Thrift bindings for Python as they do not yet
support Python 3 [1]. There is a third-party, pure Python implementation of
Thrift that does support Python 3 called thriftpy [2]. However, thriftpy
does not include a THTTPClient transport, which is what the Aurora
scheduler uses. I will therefore have to write my own THTTPClient transport
(and probably contribute it back to thriftpy).

[1] https://issues.apache.org/jira/browse/THRIFT-1857
[2] https://github.com/eleme/thriftpy

Hussein Elgridly
Senior Software Engineer, DSDE
The Broad Institute of MIT and Harvard


On 16 March 2015 at 19:11, Erb, Stephan <Stephan.Erb@blue-yonder.com> wrote:

> Just to make sure I get this correctly: You say, you cannot use the
> existing python client because it is python 2.7 only so you want to write a
> new one in python 3?
>
> Regards,
> Stephan
> ________________________________________
> From: Hussein Elgridly <hussein@broadinstitute.org>
> Sent: Monday, March 16, 2015 11:44 PM
> To: dev@aurora.incubator.apache.org
> Subject: Re: Speeding up Aurora client job creation
>
> So this has now bubbled back to the top of my TODO list and I'm actively
> working on it. I am entirely new to Thrift so please forgive the newbie
> questions...
>
> I would like to talk to the Aurora scheduler directly from my (Python)
> application using Thrift. Since I'm on Python 3.4 I've had to use thriftpy:
> https://github.com/eleme/thriftpy
>
> As far as I can tell, the following should work (by default, thriftpy uses
> a TBufferedTransport around a TSocket):
>
> ---
> import thriftpy
> import thriftpy.rpc
>
> aurora_api = thriftpy.load("api.thrift")
>
> client = thriftpy.rpc.make_client(aurora_api.AuroraSchedulerManager,
> host="localhost", port=8081,
> proto_factory=thriftpy.protocol.TJSONProtocolFactory() )
>
> print(client.getJobSummary())
> ---
>
> Obviously I wouldn't be writing this email if it did work :) It hangs.
>
> I jumped into pdb and found it was sending the following payload:
>
> b'\x00\x00\x00\\{"metadata": {"name": "getJobSummary", "seqid": 0, "ttype":
> 1, "version": 1}, "payload": {}}'
>
> to a socket that looked like this:
>
> <socket.socket fd=3, family=AddressFamily.AF_INET, type=2049, proto=0,
> laddr=('<localhost's_private_ip>', 49167), raddr=('localhost's_private_ip',
> 8081)>
>
> ...but was waiting forever to receive any data. Adding a timeout just
> triggered the timeout.
>
> I'm stumped. Any clues?
>
>
> Hussein Elgridly
> Senior Software Engineer, DSDE
> The Broad Institute of MIT and Harvard
>
>
> On 12 February 2015 at 04:15, Erb, Stephan <Stephan.Erb@blue-yonder.com>
> wrote:
>
> > Hi Hussein,
> >
> > we also had slight performance problems when talking to Aurora. We ended
> > up using the existing python client directly in our code (see
> > apache.aurora.client.api.__init__.py). This allowed us to reuse the api
> > object and its scheduler connection, dropping a connection latency of
> about
> > 0.3-0.4 seconds per request.
> >
> > Best Regards,
> > Stephan
> > ________________________________________
> > From: Bill Farner <wfarner@apache.org>
> > Sent: Wednesday, February 11, 2015 9:29 PM
> > To: dev@aurora.incubator.apache.org
> > Subject: Re: Speeding up Aurora client job creation
> >
> > To reduce that time you will indeed want to talk directly to the
> > scheduler.  This will definitely require you to roll up your sleeves a
> bit
> > and set up a thrift client to our api (based on api.thrift [1]), since
> you
> > will need to specify your tasks in a format that the thermos executor can
> > understand.  Turns out this is JSON data, so it should not be *too*
> > prohibitive.
> >
> > However, there is another technical limitation you will hit for the
> > submission rate you are after.  The scheduler is backed by a durable
> store
> > whose write latency is at minimum the amount of time required to fsync.
> >
> > [1]
> >
> >
> https://github.com/apache/incubator-aurora/blob/master/api/src/main/thrift/org/apache/aurora/gen/api.thrift
> >
> > -=Bill
> >
> > On Wed, Feb 11, 2015 at 11:46 AM, Hussein Elgridly <
> > hussein@broadinstitute.org> wrote:
> >
> > > Hi folks,
> > >
> > > I'm looking at a use cases that involves submitting potentially
> hundreds
> > of
> > > jobs a second to our Mesos cluster. My tests show that the aurora
> client
> > is
> > > taking 1-2 seconds for each job submission, and that I can run about
> four
> > > client processes in parallel before they peg the CPU at 100%. I need
> more
> > > throughput than this!
> > >
> > > Squashing jobs down to the Process or Task level doesn't really make
> > sense
> > > for our use case. I'm aware that with some shenanigans I can batch jobs
> > > together using job instances, but that's a lot of work on my current
> > > timeframe (and of questionable utility given that the jobs certainly
> > won't
> > > have identical resource requirements).
> > >
> > > What I really need is (at least) an order of magnitude speedup in terms
> > of
> > > being able to submit jobs to the Aurora scheduler (via the client or
> > > otherwise).
> > >
> > > Conceptually it doesn't seem like adding a job to a queue should be a
> > thing
> > > that takes a couple of seconds, so I'm baffled as to why it's taking so
> > > long. As an experiment, I wrapped the call to client.execute() in
> > > client.py:proxy_main in cProfile and called aurora job create with a
> very
> > > simple test job.
> > >
> > > Results of the profile are in the Gist below:
> > >
> > > https://gist.github.com/helgridly/b37a0d27f04a37e72bb5
> > >
> > > Our of a 0.977s profile time, the two things that stick out to me are:
> > >
> > > 1. 0.526s spent in Pystachio for a job that doesn't use any templates
> > > 2. 0.564s spent in create_job, presumably talking to the scheduler (and
> > > setting up the machinery for doing so)
> > >
> > > I imagine I can sidestep #1 with a check for "{{" in the job file and
> > > bypass Pystachio entirely. Can I also skip the Aurora client entirely
> and
> > > talk directly to the scheduler? If so what does that entail, and are
> > there
> > > any risks associated?
> > >
> > > Thanks,
> > > -Hussein
> > >
> > > Hussein Elgridly
> > > Senior Software Engineer, DSDE
> > > The Broad Institute of MIT and Harvard
> > >
> >
>

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