[jira] [Commented] (SPARK-24934) Complex type and binary type in in-memory partition pruning does not work due to missing upper/lower bounds cases

2018-07-30 Thread Hyukjin Kwon (JIRA) 


[ 
https://issues.apache.org/jira/browse/SPARK-24934?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16562126#comment-16562126
 ] 

Hyukjin Kwon commented on SPARK-24934:
--

I think this has been a bug from the first place. It at least affects 2.3.1. I 
manually tested:

{code}
Welcome to
    __
 / __/__  ___ _/ /__
_\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 2.3.1
  /_/

Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_162)
Type in expressions to have them evaluated.
Type :help for more information.

scala> import org.apache.spark.sql.functions
import org.apache.spark.sql.functions

scala>

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}

> Complex type and binary type in in-memory partition pruning does not work due 
> to missing upper/lower bounds cases
> -
>
> Key: SPARK-24934
> URL: https://issues.apache.org/jira/browse/SPARK-24934
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 2.3.1, 2.4.0
>Reporter: Hyukjin Kwon
>Assignee: Hyukjin Kwon
>Priority: Critical
>  Labels: correctness
> Fix For: 2.3.2, 2.4.0
>
>
> 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) Complex type and binary type in in-memory partition pruning does not work due to missing upper/lower bounds cases

2018-07-30 Thread Thomas Graves (JIRA) 


[ 
https://issues.apache.org/jira/browse/SPARK-24934?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16561944#comment-16561944
 ] 

Thomas Graves commented on SPARK-24934:
---

what is the real affected versions here?  Since it went into spark 2.3.2 does 
it affect 2.3.1?

> Complex type and binary type in in-memory partition pruning does not work due 
> to missing upper/lower bounds cases
> -
>
> 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
>Assignee: Hyukjin Kwon
>Priority: Critical
>  Labels: correctness
> Fix For: 2.3.2, 2.4.0
>
>
> 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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