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Posted to issues@ignite.apache.org by "ASF GitHub Bot (JIRA)" <ji...@apache.org> on 2018/08/13 15:13:00 UTC

[jira] [Commented] (IGNITE-9180) IgniteSparkSession Should Copy State on cloneSession()

    [ https://issues.apache.org/jira/browse/IGNITE-9180?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16578417#comment-16578417 ] 

ASF GitHub Bot commented on IGNITE-9180:
----------------------------------------

GitHub user stuartmacd opened a pull request:

    https://github.com/apache/ignite/pull/4531

    IGNITE-9180 IgniteSparkSession Should Copy State on cloneSession()

    

You can merge this pull request into a Git repository by running:

    $ git pull https://github.com/stuartmacd/ignite ignite-9180

Alternatively you can review and apply these changes as the patch at:

    https://github.com/apache/ignite/pull/4531.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

    This closes #4531
    
----
commit 9123bfc059dc83d038f1b4a4d06cef735833389b
Author: Stuart Macdonald <29...@...>
Date:   2018-08-13T15:05:53Z

    Fix cloneSession on IgniteSparkSession

----


> IgniteSparkSession Should Copy State on cloneSession()
> ------------------------------------------------------
>
>                 Key: IGNITE-9180
>                 URL: https://issues.apache.org/jira/browse/IGNITE-9180
>             Project: Ignite
>          Issue Type: Bug
>          Components: spark
>            Reporter: Stuart Macdonald
>            Priority: Major
>
> The IgniteSparkSession class extends SparkSession and overrides the cloneSession() method. The contract for cloneSession() explicitly states that it should clone all state (ie. the sharedState and sessionState fields), however the IgniteSparkSession implementation doesn't clone its state fields.
> This has the side-effect of breaking stateful operations for anything which uses cloneSession(), for example a Spark streaming job will not be able to use cached data across streaming microbatches, which is a significant issue for such applications.



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