[jira] [Resolved] (SPARK-12147) Off heap storage and dynamicAllocation operation

["Hyukjin Kwon (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-12147?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon resolved SPARK-12147.
----------------------------------
    Resolution: Incomplete

> 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
>              Labels: bulk-closed
>         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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