[jira] [Resolved] (SPARK-27834) Make separate PySpark/SparkR vectorization configurations

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

     [ https://issues.apache.org/jira/browse/SPARK-27834?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon resolved SPARK-27834.
----------------------------------
       Resolution: Fixed
    Fix Version/s: 3.0.0

Issue resolved by pull request 24700
[https://github.com/apache/spark/pull/24700]

> Make separate PySpark/SparkR vectorization configurations
> ---------------------------------------------------------
>
>                 Key: SPARK-27834
>                 URL: https://issues.apache.org/jira/browse/SPARK-27834
>             Project: Spark
>          Issue Type: Improvement
>          Components: PySpark, SparkR, SQL
>    Affects Versions: 3.0.0
>            Reporter: Hyukjin Kwon
>            Assignee: Hyukjin Kwon
>            Priority: Major
>             Fix For: 3.0.0
>
>
> {{spark.sql.execution.arrow.enabled}} was added when we add PySpark arrow optimization.
> Later, in the current master, SparkR arrow optimization was added and it's controlled by the same configuration {{spark.sql.execution.arrow.enabled}}.
> There look two issues about this:
> 1. {{spark.sql.execution.arrow.enabled}} in PySpark was added from 2.3.0 whereas SparkR optimization was added 3.0.0. The stability is different so it's problematic when we change the default value for one of both optimization first.
> 2. Suppose users want to share some JVM by PySpark and SparkR. They are currently forced to use the optimization for all or none.



--
This message was sent by Atlassian JIRA
(v7.6.3#76005)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org