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....@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 <huss...@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....@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 <wfar...@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 < > > huss...@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 > > > > > >
