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https://issues.apache.org/jira/browse/SPARK-10000?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17572486#comment-17572486
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sam commented on SPARK-10000:
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Is there any way to disable Spark from evicting rdd partitions from memory and
instead just blow up with an OOM (like the good old days)?
Because this makes the Accumulators highly unstable, in fact it practically
just invalidates the Accumulator feature altogether.
> Consolidate storage and execution memory management
> ---------------------------------------------------
>
> Key: SPARK-10000
> URL: https://issues.apache.org/jira/browse/SPARK-10000
> Project: Spark
> Issue Type: Story
> Components: Block Manager, Spark Core
> Reporter: Reynold Xin
> Assignee: Andrew Or
> Priority: Major
> Fix For: 1.6.0
>
> Attachments: unified-memory-management-spark-10000.pdf
>
>
> Memory management in Spark is currently broken down into two disjoint
> regions: one for execution and one for storage. The sizes of these regions
> are statically configured and fixed for the duration of the application.
> There are several limitations to this approach. It requires user expertise to
> avoid unnecessary spilling, and there are no sensible defaults that will work
> for all workloads. As a Spark user, I want Spark to manage the memory more
> intelligently so I do not need to worry about how to statically partition the
> execution (shuffle) memory fraction and cache memory fraction. More
> importantly, applications that do not use caching use only a small fraction
> of the heap space, resulting in suboptimal performance.
> Instead, we should unify these two regions and let one borrow from another if
> possible.
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