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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/02/12 08:41:00 UTC
[jira] [Updated] (SPARK-26858) Vectorized gapplyCollect, Arrow
optimization in native R function execution
[ https://issues.apache.org/jira/browse/SPARK-26858?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon updated SPARK-26858:
---------------------------------
Description:
Unlike gapply, gapplyCollect requires additional ser/de steps because it can omit the schema, and Spark SQL doesn't know the return type before actually execution happens.
In original code path, it's done via using binary schema. Once gapply is done (SPARK-26761). we can mimic this approach in vectorized gapply to support gapplyCollect.
was:
Unlike gapply, gapplyCollect requires additional ser/de steps because it can omit the schema, and Spark SQL doesn't know the return type before actually execution happens.
In original code path, it's done via using binary schema. Once gapply is done. we can mimic this approach in vectorized gapply to support gapplyCollect.
> Vectorized gapplyCollect, Arrow optimization in native R function execution
> ---------------------------------------------------------------------------
>
> Key: SPARK-26858
> URL: https://issues.apache.org/jira/browse/SPARK-26858
> Project: Spark
> Issue Type: Sub-task
> Components: SparkR, SQL
> Affects Versions: 3.0.0
> Reporter: Hyukjin Kwon
> Assignee: Hyukjin Kwon
> Priority: Major
>
> Unlike gapply, gapplyCollect requires additional ser/de steps because it can omit the schema, and Spark SQL doesn't know the return type before actually execution happens.
>
> In original code path, it's done via using binary schema. Once gapply is done (SPARK-26761). we can mimic this approach in vectorized gapply to support gapplyCollect.
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