Funny, that's actually how the scheduler API originally worked. I think
this is worth exploring, and would indeed completely sidestep the paradigm
shift i mentioned above.
On Mon, Jan 11, 2016 at 9:20 PM, John Sirois <j...@conductant.com> wrote:
> On Mon, Jan 11, 2016 at 10:10 PM, Bill Farner <wfar...@apache.org> wrote:
>
> > There's a chicken and egg problem though. That variable will only be
> filled
> > in on the executor, when we're already running in the docker environment.
> > In this case, the parameter is used to *define* the docker environment.
> >
>
> So, from a naive standpoint, the fact that Job is exploded into Tasks by
> the scheduler but that explosion is not exposed to the client seems to be
> the impedance mismatch here.
> I have not thought through this much at all, but say that fundamentally the
> scheduler took a Job that was a list of Tasks - possibly heterogeneous.
> The current Job expands to homogeneous Tasks could be just a standard
> convenience.
>
> In that sort of world, the customized params could be injected client side
> to form a list of heterogeneous tasks and the Scheduler could stay dumb -
> at least wrt Task parameterization.
>
>
> > On Mon, Jan 11, 2016 at 9:07 PM, ben...@gmail.com <ben...@gmail.com>
> > wrote:
> >
> > > As a starting point, you might be able to cook up something involving
> > > {{mesos.instance}} as a lookup key to a pystachio list. You do have a
> > > unique integer task number per instance to work with.
> > >
> > > cf.
> > >
> > >
> >
> http://aurora.apache.org/documentation/latest/configuration-reference/#template-namespaces
> > >
> > > On Mon, Jan 11, 2016 at 8:05 PM Bill Farner <wfar...@apache.org>
> wrote:
> > >
> > > > I agree that this appears necessary when parameters are needed to
> > define
> > > > the runtime environment of the task (in this case, setting up the
> > docker
> > > > container).
> > > >
> > > > What's particularly interesting here is that this would call for the
> > > > scheduler to fill in the parameter values prior to launching each
> task.
> > > > Using pystachio variables for this is certainly the most natural in
> the
> > > > DSL, but becomes a paradigm shift since the scheduler is currently
> > > ignorant
> > > > of pystachio.
> > > >
> > > > Possibly only worth mentioning for shock value, but in the DSL this
> > > starts
> > > > to look like lambdas pretty quickly.
> > > >
> > > > On Mon, Jan 11, 2016 at 7:46 PM, Mauricio Garavaglia <
> > > > mauriciogaravag...@gmail.com> wrote:
> > > >
> > > > > Hi guys,
> > > > >
> > > > > We are using the docker rbd volume plugin
> > > > > <
> > > >
> > >
> >
> https://ceph.com/planet/getting-started-with-the-docker-rbd-volume-plugin>
> > > > > to
> > > > > provide persistent storage to the aurora jobs that runs in the
> > > > containers.
> > > > > Something like:
> > > > >
> > > > > p = [Parameter(name='volume', value='my-ceph-volume:/foo'), ...]
> > > > > jobs = [ Service(..., container = Container(docker = Docker(...,
> > > > parameters
> > > > > = p)))]
> > > > >
> > > > > But in the case of jobs with multiple instances it's required to
> > start
> > > > each
> > > > > container using different volumes, in our case different ceph
> images.
> > > > This
> > > > > could be achieved by deploying, for example, 10 instances and then
> > > update
> > > > > each one independently to use the appropiate volume. Of course this
> > is
> > > > > quite inconvenient, error prone, and adds a lot of logic and state
> > > > outside
> > > > > aurora.
> > > > >
> > > > > We where thinking if it would make sense to have a way to
> > parameterize
> > > > the
> > > > > task instances, in a similar way that is done with portmapping for
> > > > example.
> > > > > In the job definition have something like
> > > > >
> > > > > params = [
> > > > > Parameter( name='volume',
> > > > > value='service-{{instanceParameters.volume}}:/foo' )
> > > > > ]
> > > > > ...
> > > > > jobs = [
> > > > > Service(
> > > > > name = 'logstash',
> > > > > ...
> > > > > instanceParameters = { "volume" : ["foo", "bar", "zaa"]},
> > > > > instances = 3,
> > > > > container = Container(
> > > > > docker = Docker(
> > > > > image = 'image',
> > > > > parameters = params
> > > > > )
> > > > > )
> > > > > )
> > > > > ]
> > > > >
> > > > >
> > > > > Something like that, it would create 3 instances of the tasks, each
> > one
> > > > > running in a container that uses the volumes foo, bar, and zaa.
> > > > >
> > > > > Does it make sense? I'd be glad to work on it but I want to
> validate
> > > the
> > > > > idea with you first and hear comments about the api/implementation.
> > > > >
> > > > > Thanks
> > > > >
> > > > >
> > > > > Mauricio
> > > > >
> > > >
> > >
> >
>
>
>
> --
> John Sirois
> 303-512-3301
>
