[jira] [Commented] (SPARK-24934) Should handle missing upper/lower bounds cases in in-memory partition pruning

2018-07-26 Thread Hyukjin Kwon (JIRA) 


[ 
https://issues.apache.org/jira/browse/SPARK-24934?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16558364#comment-16558364
 ] 

Hyukjin Kwon commented on SPARK-24934:
--

np! BTW, the workaround will be turning off 
{{spark.sql.inMemoryColumnarStorage.partitionPruning}} although it'd be less 
performant.

> Should handle missing upper/lower bounds cases in in-memory partition pruning
> -
>
> Key: SPARK-24934
> URL: https://issues.apache.org/jira/browse/SPARK-24934
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 2.4.0
>Reporter: Hyukjin Kwon
>Priority: Major
>
> For example, if array is used (where the lower and upper bounds for its 
> column batch are {{null}})), it looks wrongly filtering all data out:
> {code}
> scala> import org.apache.spark.sql.functions
> import org.apache.spark.sql.functions
> scala> val df = Seq(Array("a", "b"), Array("c", "d")).toDF("arrayCol")
> df: org.apache.spark.sql.DataFrame = [arrayCol: array]
> scala> 
> df.filter(df.col("arrayCol").eqNullSafe(functions.array(functions.lit("a"), 
> functions.lit("b".show()
> ++
> |arrayCol|
> ++
> |  [a, b]|
> ++
> scala> 
> df.cache().filter(df.col("arrayCol").eqNullSafe(functions.array(functions.lit("a"),
>  functions.lit("b".show()
> ++
> |arrayCol|
> ++
> ++
> {code}



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[jira] [Commented] (SPARK-24934) Should handle missing upper/lower bounds cases in in-memory partition pruning

2018-07-26 Thread David Vogelbacher (JIRA) 


[ 
https://issues.apache.org/jira/browse/SPARK-24934?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16558360#comment-16558360
 ] 

David Vogelbacher commented on SPARK-24934:
---

Thanks for opening and making the pr [~hyukjin.kwon]!

> Should handle missing upper/lower bounds cases in in-memory partition pruning
> -
>
> Key: SPARK-24934
> URL: https://issues.apache.org/jira/browse/SPARK-24934
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 2.4.0
>Reporter: Hyukjin Kwon
>Priority: Major
>
> For example, if array is used (where the lower and upper bounds for its 
> column batch are {{null}})), it looks wrongly filtering all data out:
> {code}
> scala> import org.apache.spark.sql.functions
> import org.apache.spark.sql.functions
> scala> val df = Seq(Array("a", "b"), Array("c", "d")).toDF("arrayCol")
> df: org.apache.spark.sql.DataFrame = [arrayCol: array]
> scala> 
> df.filter(df.col("arrayCol").eqNullSafe(functions.array(functions.lit("a"), 
> functions.lit("b".show()
> ++
> |arrayCol|
> ++
> |  [a, b]|
> ++
> scala> 
> df.cache().filter(df.col("arrayCol").eqNullSafe(functions.array(functions.lit("a"),
>  functions.lit("b".show()
> ++
> |arrayCol|
> ++
> ++
> {code}



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[jira] [Commented] (SPARK-24934) Should handle missing upper/lower bounds cases in in-memory partition pruning

2018-07-26 Thread Apache Spark (JIRA) 


[ 
https://issues.apache.org/jira/browse/SPARK-24934?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16558295#comment-16558295
 ] 

Apache Spark commented on SPARK-24934:
--

User 'HyukjinKwon' has created a pull request for this issue:
https://github.com/apache/spark/pull/21882

> Should handle missing upper/lower bounds cases in in-memory partition pruning
> -
>
> Key: SPARK-24934
> URL: https://issues.apache.org/jira/browse/SPARK-24934
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 2.4.0
>Reporter: Hyukjin Kwon
>Priority: Major
>
> For example, if array is used (where the lower and upper bounds for its 
> column batch are {{null}})), it looks wrongly filtering all data out:
> {code}
> scala> import org.apache.spark.sql.functions
> import org.apache.spark.sql.functions
> scala> val df = Seq(Array("a", "b"), Array("c", "d")).toDF("arrayCol")
> df: org.apache.spark.sql.DataFrame = [arrayCol: array]
> scala> 
> df.filter(df.col("arrayCol").eqNullSafe(functions.array(functions.lit("a"), 
> functions.lit("b".show()
> ++
> |arrayCol|
> ++
> |  [a, b]|
> ++
> scala> 
> df.cache().filter(df.col("arrayCol").eqNullSafe(functions.array(functions.lit("a"),
>  functions.lit("b".show()
> ++
> |arrayCol|
> ++
> ++
> {code}



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