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From Pavel Plotnikov <pavel.plotni...@team.wrike.com>
Subject Re: Spark on Mesos - Weird behavior
Date Wed, 11 Jul 2018 14:09:38 GMT
Hi, Thodoris
You can configure resources per executor and manipulate with number of
executers instead using spark.max.cores. I think
spark.dynamicAllocation.minExecutors
and spark.dynamicAllocation.maxExecutors configuration values can help you.

On Tue, Jul 10, 2018 at 5:07 PM Thodoris Zois <zois@ics.forth.gr> wrote:

> Actually after some experiments we figured out that spark.max.cores /
> spark.executor.cores is the upper bound for the executors. Spark apps will
> run even only if one executor can be launched.
>
> Is there any way to specify also the lower bound? It is a bit annoying
> that seems that we can’t control the resource usage of an application. By
> the way, we are not using dynamic allocation.
>
> - Thodoris
>
>
> On 10 Jul 2018, at 14:35, Pavel Plotnikov <pavel.plotnikov@team.wrike.com>
> wrote:
>
> Hello Thodoris!
> Have you checked this:
>  - does mesos cluster have available resources?
>   - if spark have waiting tasks in queue more than
> spark.dynamicAllocation.schedulerBacklogTimeout configuration value?
>  - And then, have you checked that mesos send offers to spark app mesos
> framework at least with 10 cores and 2GB RAM?
>
> If mesos have not available offers with 10 cores, for example, but have
> with 8 or 9, so you can use smaller executers for better fit for available
> resources on nodes for example with 4 cores and 1 GB RAM, for example
>
> Cheers,
> Pavel
>
> On Mon, Jul 9, 2018 at 9:05 PM Thodoris Zois <zois@ics.forth.gr> wrote:
>
>> Hello list,
>>
>> We are running Apache Spark on a Mesos cluster and we face a weird
>> behavior of executors. When we submit an app with e.g 10 cores and 2GB of
>> memory and max cores 30, we expect to see 3 executors running on the
>> cluster. However, sometimes there are only 2... Spark applications are not
>> the only one that run on the cluster. I guess that Spark starts executors
>> on the available offers even if it does not satisfy our needs. Is there any
>> configuration that we can use in order to prevent Spark from starting when
>> there are no resource offers for the total number of executors?
>>
>> Thank you
>> - Thodoris
>>
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