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https://issues.apache.org/jira/browse/SPARK-12147?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15041789#comment-15041789
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Rares Mirica commented on SPARK-12147:
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Yes, I am talking about the executor stopping as part of scaling down on
dynamic allocation. I am observing this in am actual test, I was reading
the docs just to test my assumption.
> Off heap storage and dynamicAllocation operation
> ------------------------------------------------
>
> Key: SPARK-12147
> URL: https://issues.apache.org/jira/browse/SPARK-12147
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Affects Versions: 1.5.2
> Environment: Cloudera Hadoop 2.6.0-cdh5.4.8
> Tachyon 0.7.1
> Yarn
> Reporter: Rares Mirica
> Priority: Minor
> Attachments: spark-defaults.conf
>
>
> For the purpose of increasing computation density and efficiency I set up to
> test off-heap storage (using Tachyon) with dynamicAllocation enabled.
> Following the available documentation (programming-guide for Spark 1.5.2) I
> was expecting data to be cached in Tachyon for the lifetime of the
> application (driver instance) or until unpersist() is called. This belief was
> supported by the doc: "Cached data is not lost if individual executors
> crash." where with crash I also assimilate Graceful Decommission.
> Furthermore, in the GD description documented in the job-scheduling document
> cached data preservation through off-heap storage is also hinted at.
> Seeing how Tachyon is now in a state where these promises of a better future
> are well within reach, I consider it a bug that upon graceful decommission of
> an executor the off-heap data is deleted (presumably as part of the cleanup
> phase).
> Needless to say, enabling the preservation of the off-heap persisted data
> after graceful decommission for dynamic allocation would yield significant
> improvements in resource allocation, especially over yarn where executors use
> up compute "slots" even if idle. After a long, expensive, computation where
> we take advantage of the dynamically scaled executors, the rest of the spark
> jobs can use the cached data while releasing the compute resources for other
> cluster tasks.
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