Can you offer some more details on what the workload execution looks like?
Are these shell commands? An application that's provided different
configuration?
-=Bill
On Wed, Feb 26, 2014 at 12:45 PM, Bryan Helmkamp <br...@codeclimate.com>wrote:
> Thanks, Kevin. The idea of always-on workers of varying sizes is
> effectively what we have right now in our non-Mesos world. The problem
> is that sometimes we end up with not enough workers for certain
> classes of jobs (e.g. High Memory), while part of the cluster sits
> idle.
>
> Conceptually, in my mind we would define approximately a dozen Tasks,
> one for each type of work we need to perform (with different resource
> requirements), and then run Jobs, each with a Task and a unique
> payload, but I don't think this model works with Mesos. It seems we'd
> need to create a unique Task for every Job.
>
> -Bryan
>
> On Wed, Feb 26, 2014 at 3:35 PM, Kevin Sweeney <kevi...@apache.org> wrote:
> > A job is a group of nearly-identical tasks plus some constraints like
> rack
> > diversity. The scheduler considers each task within a job equivalently
> > schedulable, so you can't vary things like resource footprint. It's
> > perfectly fine to have several jobs with just a single task, as long as
> > each has a different job key (which is (role, environment, name)).
> >
> > Another approach is to have a bunch of uniform always-on workers (in
> > different sizes). This can be expressed as a Service like so:
> >
> > # workers.aurora
> > class Profile(Struct):
> > queue_name = Required(String)
> > resources = Required(Resources)
> > instances = Required(Integer)
> >
> > HIGH_MEM = Resources(cpu = 8.0, ram = 32 * GB, disk = 64 * GB)
> > HIGH_CPU = Resources(cpu = 16.0, ram = 4 * GB, disk = 64 * GB)
> >
> > work_forever = Process(name = 'work_forever',
> > cmdline = '''
> > # TODO: Replace this with something that isn't pseudo-bash
> > while true; do
> > work_item=`take_from_work_queue {{profile.queue_name}}`
> > do_work "$work_item"
> > tell_work_queue_finished "{{profile.queue_name}}" "$work_item"
> > done
> > ''')
> >
> > task = Task(processes = [work_forever],
> > * resources = '{{profile.resources}}, # Note this is static per
> > queue-name.*
> > )
> >
> > service = Service(
> > task = task,
> > cluster = 'west',
> > role = 'service-account-name',
> > environment = 'prod',
> > name = '{{profile.queue_name}}_processor'
> > *instances = '{{profile.instances}}', # Scale here.*
> > )
> >
> > jobs = [
> > service.bind(profile = Profile(
> > resources = HIGH_MEM,
> > queue_name = 'graph_traversals',
> > instances = 50,
> > )),
> > service.bind(profile = Profile(
> > resources = HIGH_CPU,
> > queue_name = 'compilations',
> > instances = 200,
> > )),
> > ]
> >
> >
> > On Wed, Feb 26, 2014 at 11:46 AM, Bryan Helmkamp <br...@codeclimate.com
> >wrote:
> >
> >> Thanks, Bill.
> >>
> >> Am I correct in understanding that is not possible to parameterize
> >> individual Jobs, just Tasks? Therefore, since I don't know the job
> >> definitions up front, I will have parameterized Task templates, and
> >> generate a new Task every time I need to run a Job?
> >>
> >> Is that the recommended route?
> >>
> >> Our work is very non-uniform so I don't think work-stealing would be
> >> efficient for us.
> >>
> >> -Bryan
> >>
> >> On Wed, Feb 26, 2014 at 12:49 PM, Bill Farner <wfar...@apache.org>
> wrote:
> >> > Thanks for checking out Aurora!
> >> >
> >> > My short answer is that Aurora should handle thousands of short-lived
> >> > tasks/jobs per day without trouble. (If you proceed with this
> approach
> >> and
> >> > encounter performance issues, feel free to file tickets!) The DSL
> does
> >> > have some mechanisms for parameterization. In your case since you
> >> probably
> >> > don't know all the job definitions upfront, you'll probably want to
> >> > parameterize with environment variables. I don't see this described
> in
> >> our
> >> > docs, but you there's a little detail at the option declaration [1].
> >> >
> >> > Another approach worth considering is work-stealing, using a single
> job
> >> as
> >> > your pool of workers. I would find this easier to manage, but it
> would
> >> > only be suitable if your work items are sufficiently-uniform.
> >> >
> >> > Feel free to continue the discussion! We're also pretty active in our
> >> IRC
> >> > channel if you'd prefer that medium.
> >> >
> >> >
> >> > [1]
> >> >
> >>
> https://github.com/apache/incubator-aurora/blob/master/src/main/python/apache/aurora/client/options.py#L170-L183
> >> >
> >> >
> >> > -=Bill
> >> >
> >> >
> >> > On Tue, Feb 25, 2014 at 10:11 PM, Bryan Helmkamp <
> br...@codeclimate.com
> >> >wrote:
> >> >
> >> >> Hello,
> >> >>
> >> >> I am considering Aurora for a key component of our infrastructure.
> >> >> Awesome work being done here.
> >> >>
> >> >> My question is: How suitable is Aurora for running short-lived tasks?
> >> >>
> >> >> Background: We (Code Climate) do static analysis of tens of thousands
> >> >> of repositories every day. We run a variety of forms of analysis,
> with
> >> >> heterogeneous resource requirements, and thus our interest in Mesos.
> >> >>
> >> >> Looking at Aurora, a lot of the core features look very helpful to
> us.
> >> >> Where I am getting hung up is figuring out how to model short-lived
> >> >> tasks as tasks/jobs. Long-running resource allocations are not really
> >> >> an option for us due to the variation in our workloads.
> >> >>
> >> >> My first thought was to create a Task for each type of analysis we
> >> >> run, and then start a new Job with the appropriate Task every time we
> >> >> want to run analysis (regulated by a queue). This doesn't seem to
> work
> >> >> though. I can't `aurora create` the same `.aurora` file multiple
> times
> >> >> with different Job names (as far as I can tell). Also there is the
> >> >> problem of how to customize each Job slightly (e.g. a payload).
> >> >>
> >> >> An obvious alternative is to create a unique Task every time we want
> >> >> to run work. This would result in tens of thousands of tasks being
> >> >> created every day, and from what I can tell Aurora does not intend to
> >> >> be used like that. (Please correct me if I am wrong.)
> >> >>
> >> >> Basically, I would like to hook my job queue up to Aurora to perform
> >> >> the actual work. There are a dozen different types of jobs, each with
> >> >> different performance requirements. Every time a job runs, it has a
> >> >> unique payload containing the definition of the work it should be
> >> >> performed.
> >> >>
> >> >> Can Aurora be used this way? If so, what is the proper way to model
> >> >> this with respect to Jobs and Tasks?
> >> >>
> >> >> Any/all help is appreciated.
> >> >>
> >> >> Thanks!
> >> >>
> >> >> -Bryan
> >> >>
> >> >> --
> >> >> Bryan Helmkamp, Founder, Code Climate
> >> >> br...@codeclimate.com / 646-379-1810 / @brynary
> >> >>
> >>
> >>
> >>
> >> --
> >> Bryan Helmkamp, Founder, Code Climate
> >> br...@codeclimate.com / 646-379-1810 / @brynary
> >>
>
>
>
> --
> Bryan Helmkamp, Founder, Code Climate
> br...@codeclimate.com / 646-379-1810 / @brynary
>
