[
https://issues.apache.org/jira/browse/SPARK-15917?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-15917:
------------------------------------
Assignee: (was: Apache Spark)
> Define the number of executors in standalone mode with an easy-to-use property
> ------------------------------------------------------------------------------
>
> Key: SPARK-15917
> URL: https://issues.apache.org/jira/browse/SPARK-15917
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core, Spark Shell, Spark Submit
> Affects Versions: 1.6.1
> Reporter: Jonathan Taws
> Priority: Minor
>
> After stumbling across a few StackOverflow posts around the issue of using a
> fixed number of executors in standalone mode (non-YARN), I was wondering if
> we could not add an easier way to set this parameter than having to resort to
> some calculations based on the number of cores and the memory you have
> available on your worker.
> For example, let's say I have 8 cores and 30GB of memory available :
> - If no option is passed, one executor will be spawned with 8 cores and 1GB
> of memory allocated.
> - However, if I want to have only *2* executors, and to use 2 cores and 10GB
> of memory per executor, I will end up with *3* executors (as the available
> memory will limit the number of executors) instead of the 2 I was hoping for.
> Sure, I can set {{spark.cores.max}} as a workaround to get exactly what I
> want, but would it not be easier to add a {{--num-executors}}-like option to
> standalone mode to be able to really fine-tune the configuration ? This
> option is already available in YARN mode.
> From my understanding, I don't see any other option lying around that can
> help achieve this.
> This seems to be slightly disturbing for newcomers, and standalone mode is
> probably the first thing anyone will use to just try out Spark or test some
> configuration.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
