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https://issues.apache.org/jira/browse/SPARK-1777?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14092826#comment-14092826
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Apache Spark commented on SPARK-1777:
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User 'liyezhang556520' has created a pull request for this issue:
https://github.com/apache/spark/pull/1892
> Pass "cached" blocks directly to disk if memory is not large enough
> -------------------------------------------------------------------
>
> Key: SPARK-1777
> URL: https://issues.apache.org/jira/browse/SPARK-1777
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Reporter: Patrick Wendell
> Assignee: Andrew Or
> Priority: Critical
> Fix For: 1.1.0
>
> Attachments: spark-1777-design-doc.pdf
>
>
> Currently in Spark we entirely unroll a partition and then check whether it
> will cause us to exceed the storage limit. This has an obvious problem - if
> the partition itself is enough to push us over the storage limit (and
> eventually over the JVM heap), it will cause an OOM.
> This can happen in cases where a single partition is very large or when
> someone is running examples locally with a small heap.
> https://github.com/apache/spark/blob/f6ff2a61d00d12481bfb211ae13d6992daacdcc2/core/src/main/scala/org/apache/spark/CacheManager.scala#L148
> We should think a bit about the most elegant way to fix this - it shares some
> similarities with the external aggregation code.
> A simple idea is to periodically check the size of the buffer as we are
> unrolling and see if we are over the memory limit. If we are we could prepend
> the existing buffer to the iterator and write that entire thing out to disk.
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