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https://issues.apache.org/jira/browse/SPARK-10000?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Andrew Or updated SPARK-10000:
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Attachment: unified-memory-management-spark-10000.pdf
> Consolidate cache memory management 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
> 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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