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https://issues.apache.org/jira/browse/SPARK-8726?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Stefano Parmesan closed SPARK-8726.
-----------------------------------
Resolution: Fixed
Fix Version/s: 1.4.0
> Wrong spark.executor.memory when using different EC2 master and worker
> machine types
> ------------------------------------------------------------------------------------
>
> Key: SPARK-8726
> URL: https://issues.apache.org/jira/browse/SPARK-8726
> Project: Spark
> Issue Type: Bug
> Components: EC2
> Affects Versions: 1.4.0
> Reporter: Stefano Parmesan
> Fix For: 1.4.0
>
>
> _(this is a mirror of
> [MESOS-2985|https://issues.apache.org/jira/browse/MESOS-2985])_
> By default, {{spark.executor.memory}} is set to the [min(slave_ram_kb,
> master_ram_kb)|https://github.com/mesos/spark-ec2/blob/e642aa362338e01efed62948ec0f063d5fce3242/deploy_templates.py#L32];
> when using the same instance type for master and workers you will not
> notice, but when using different ones (which makes sense, as the master
> cannot be a spot instance, and using a big machine for the master would be a
> waste of resources) the default amount of memory given to each worker is
> capped to the amount of RAM available on the master (ex: if you create a
> cluster with an m1.small master (1.7GB RAM) and one m1.large worker (7.5GB
> RAM), spark.executor.memory will be set to 512MB).
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