[jira] [Commented] (SPARK-16042) Eliminate nullcheck code at projection for an array type

2017-02-03 Thread Hyukjin Kwon (JIRA)

[ 
https://issues.apache.org/jira/browse/SPARK-16042?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15851469#comment-15851469
 ] 

Hyukjin Kwon commented on SPARK-16042:
--

[~kiszk], would this JIRA maybe be resolvable per 
https://github.com/apache/spark/pull/13757#issuecomment-270453328?

> Eliminate nullcheck code at projection for an array type
> 
>
> Key: SPARK-16042
> URL: https://issues.apache.org/jira/browse/SPARK-16042
> Project: Spark
>  Issue Type: Improvement
>  Components: SQL
>Reporter: Kazuaki Ishizaki
>
> When we run a spark program with a projection for a array type, nullcheck at 
> a call to write each element of an array is generated. If we know all of the 
> elements do not have {{null}} at compilation time, we can eliminate code for 
> nullcheck.
> {code}
> val df = sparkContext.parallelize(Seq(1.0, 2.0), 1).toDF("v")
> df.selectExpr("Array(v + 2.2, v + 3.3)").collect
> {code}



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[jira] [Commented] (SPARK-16042) Eliminate nullcheck code at projection for an array type

2016-06-18 Thread Apache Spark (JIRA)

[ 
https://issues.apache.org/jira/browse/SPARK-16042?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15337594#comment-15337594
 ] 

Apache Spark commented on SPARK-16042:
--

User 'kiszk' has created a pull request for this issue:
https://github.com/apache/spark/pull/13757

> Eliminate nullcheck code at projection for an array type
> 
>
> Key: SPARK-16042
> URL: https://issues.apache.org/jira/browse/SPARK-16042
> Project: Spark
>  Issue Type: Improvement
>  Components: SQL
>Reporter: Kazuaki Ishizaki
>
> When we run a spark program with a projection for a array type, nullcheck at 
> a call to write each element of an array is generated. If we know all of the 
> elements do not have {{null}} at compilation time, we can eliminate code for 
> nullcheck.
> {code}
> val df = sparkContext.parallelize(Seq(1.0, 2.0), 1).toDF("v")
> df.selectExpr("Array(v + 2.2, v + 3.3)").collect
> {code}



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