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

["Apache Spark (JIRA)" <ji...@apache.org>]

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

Apache Spark reassigned SPARK-24934:
------------------------------------

    Assignee:     (was: Apache Spark)

> 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<string>]
> 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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