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From Thodoris Zois <z...@ics.forth.gr>
Subject Re: Spark on Mesos - Weird behavior
Date Mon, 23 Jul 2018 21:31:58 GMT
Hi Susan,

This is exactly what we have used. Thank you for your interest!

- Thodoris 

> On 23 Jul 2018, at 20:55, Susan X. Huynh <xhuynh@mesosphere.io> wrote:
> 
> Hi Thodoris,
> 
> Maybe setting "spark.scheduler.minRegisteredResourcesRatio" to > 0 would help? Default
value is 0 with Mesos.
> 
> "The minimum ratio of registered resources (registered resources / total expected resources)
(resources are executors in yarn mode and Kubernetes mode, CPU cores in standalone mode and
Mesos coarsed-grained mode ['spark.cores.max' value is total expected resources for Mesos
coarse-grained mode] ) to wait for before scheduling begins. Specified as a double between
0.0 and 1.0. Regardless of whether the minimum ratio of resources has been reached, the maximum
amount of time it will wait before scheduling begins is controlled by configspark.scheduler.maxRegisteredResourcesWaitingTime."
- https://spark.apache.org/docs/latest/configuration.html
> 
> Susan
> 
>> On Wed, Jul 11, 2018 at 7:22 AM, Pavel Plotnikov <pavel.plotnikov@team.wrike.com>
wrote:
>> Oh, sorry, i missed that you use spark without dynamic allocation. Anyway, i don't
know does this parameters works without dynamic allocation. 
>> 
>>> On Wed, Jul 11, 2018 at 5:11 PM Thodoris Zois <zois@ics.forth.gr> wrote:
>>> Hello,
>>> 
>>> Yeah you are right, but I think that works only if you use Spark dynamic allocation.
Am I wrong?
>>> 
>>> -Thodoris
>>> 
>>>> On 11 Jul 2018, at 17:09, Pavel Plotnikov <pavel.plotnikov@team.wrike.com>
wrote:
>>>> 
>>>> 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 
>>>>>>> 
>>>>>>> ---------------------------------------------------------------------
>>>>>>> To unsubscribe e-mail: user-unsubscribe@spark.apache.org
>>>>>>> 
>>> 
> 
> 
> 
> -- 
> Susan X. Huynh
> Software engineer, Data Agility
> xhuynh@mesosphere.com

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