Thanks, Kevin. That pretty much looks like exactly what I need.
-Bryan
On Wed, Feb 26, 2014 at 8:16 PM, Kevin Sweeney <kevi...@apache.org> wrote:
> For a more dynamic approach to resource utilization you can use something
> like this:
>
> # dynamic.aurora
> *# Enqueue each individual work-item with aurora create -E
> work_item=$work_item -E resource_profile=graph_traversals
> west/service-account-name/prod/process_$work_item*
> class Profile(Struct):
> queue_name = Required(String)
> resources = Required(Resources)
>
> 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_on_one_item = Process(name = 'work_on_one_item',
> cmdline = '''
> do_work "{{work_item}}"
> ''',
> )
>
> task = Task(processes = [work_on_one_item],
> resources = '{{resources[{{resource_profile}}]}}')
>
> job = Job(
> task = task,
> cluster = 'west',
> role = 'service-account-name',
> environment = 'prod',
> name = 'process_{{work_item}}',
> )
>
> resources = {
> 'graph_traversals': HIGH_MEM,
> 'compilations': HIGH_CPU,
> }
>
> jobs = [job.bind(resources = resources)]
>
>
>
> On Wed, Feb 26, 2014 at 1:08 PM, Bryan Helmkamp <br...@codeclimate.com>wrote:
>
>> Sure. Yes, they are shell commands and yes they are provided different
>> configuration on each run.
>>
>> In effect we have a number of different job types that are queued up,
>> and we need to run as quickly as possible. Each job type has different
>> resource requirements. Every time we run the job, we provide different
>> arguments (the "payload"). For example:
>>
>> $ ./do_something.sh SOME_ID (Requires 1 CPU and 1GB RAM)
>> $ ./do_something_else.sh SOME_OTHER_ID (Requires 4 CPU and 4GB RAM)
>> [... there are about 12 of these ...]
>>
>> -Bryan
>>
>> On Wed, Feb 26, 2014 at 3:58 PM, Bill Farner <wfar...@apache.org> wrote:
>> > 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
>> >>
>>
>>
>>
>> --
>> Bryan Helmkamp, Founder, Code Climate
>> br...@codeclimate.com / 646-379-1810 / @brynary
>>
--
Bryan Helmkamp, Founder, Code Climate
br...@codeclimate.com / 646-379-1810 / @brynary
