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From "Erb, Stephan" <Stephan....@blue-yonder.com>
Subject Re: Speeding up Aurora client job creation
Date Mon, 16 Mar 2015 23:11:59 GMT
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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