We/i were discussing STANDALONE mode, besides maxdml had already summarized 
what is available and possible under yarn

So let me recap - for standalone mode if you need more than 1 executor per 
physical host e.g. to partition its sys resources more finley (especialy RAM 
per jvm instance) you need to got for what is essentialy a bit of a hack ie 
runn8ng more than 1 workers per machine


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<div>-------- Original message --------</div><div>From: Sandy Ryza 
<sandy.r...@cloudera.com> </div><div>Date:2015/06/10  21:31  (GMT+00:00) 
</div><div>To: Evo Eftimov <evo.efti...@isecc.com> </div><div>Cc: maxdml 
<max...@cs.duke.edu>,user@spark.apache.org </div><div>Subject: Re: Determining 
number of executors within RDD </div><div>
</div>On YARN, there is no concept of a Spark Worker.  Multiple executors will 
be run per node without any effort required by the user, as long as all the 
executors fit within each node's resource limits.

-Sandy

On Wed, Jun 10, 2015 at 3:24 PM, Evo Eftimov <evo.efti...@isecc.com> wrote:
Yes  i think it is ONE worker ONE executor as executor is nothing but jvm 
instance spawned by the worker 

To run more executors ie jvm instances on the same physical cluster node you 
need to run more than one worker on that node and then allocate only part of 
the sys resourced to that worker/executot


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-------- Original message --------
From: maxdml
Date:2015/06/10 19:56 (GMT+00:00)
To: user@spark.apache.org
Subject: Re: Determining number of executors within RDD

Actually this is somehow confusing for two reasons:

- First, the option 'spark.executor.instances', which seems to be only dealt
with in the case of YARN in the source code of SparkSubmit.scala, is also
present in the conf/spark-env.sh file under the standalone section, which
would indicate that it is also available for this mode

- Second, a post from Andrew Or states that this properties define the
number of workers in the cluster, not the number of executors on a given
worker.
(http://apache-spark-user-list.1001560.n3.nabble.com/clarification-for-some-spark-on-yarn-configuration-options-td13692.html)

Could anyone clarify this? :-)

Thanks.



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