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From Romi Kuntsman <>
Subject Re: Spark job resource allocation best practices
Date Mon, 03 Nov 2014 16:03:38 GMT
So, as said there, static partitioning is used in "Spark’s standalone and
YARN modes, as well as the coarse-grained Mesos mode".
That leaves us only with Mesos, where there is *dynamic sharing* of CPU

It says "when the application is not running tasks on a machine, other
applications may run tasks on those cores".
But my applications are short lived (seconds to minutes), and they read a
large dataset, process it, and write the results. They are also IO-bound,
meaning most of the time is spent reading input data (from S3) and writing
the results back.

Is it possible to divide the resources between them, according to how many
are trying to run at the same time?
So for example if I have 12 cores - if one job is scheduled, it will get 12
cores, but if 3 are scheduled, then each one will get 4 cores and then will
all start.


*Romi Kuntsman*, *Big Data Engineer*

On Mon, Nov 3, 2014 at 5:46 PM, Akhil Das <>

> Have a look at scheduling pools
> <>. If you want
> more sophisticated resource allocation, then you are better of to use
> cluster managers like mesos or yarn
> Thanks
> Best Regards
> On Mon, Nov 3, 2014 at 9:10 PM, Romi Kuntsman <> wrote:
>> Hello,
>> I have a Spark 1.1.0 standalone cluster, with several nodes, and several
>> jobs (applications) being scheduled at the same time.
>> By default, each Spark job takes up all available CPUs.
>> This way, when more than one job is scheduled, all but the first are
>> stuck in "WAITING".
>> On the other hand, if I tell each job to initially limit itself to a
>> fixed number of CPUs, and that job runs by itself, the cluster is
>> under-utilized and the job runs longer than it could have if it took all
>> the available resources.
>> - How to give the tasks a more fair resource division, which lets many
>> jobs run together, and together lets them use all the available resources?
>> - How do you divide resources between applications on your usecase?
>> P.S. I started reading about Mesos but couldn't figure out if/how it
>> could solve the described issue.
>> Thanks!
>> *Romi Kuntsman*, *Big Data Engineer*

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