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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2019/05/24 14:59:00 UTC
[jira] [Assigned] (SPARK-27834) Make separate PySpark/SparkR
vectorization configurations
[ https://issues.apache.org/jira/browse/SPARK-27834?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-27834:
------------------------------------
Assignee: (was: Apache Spark)
> 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
> Priority: Major
>
> {{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.
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