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https://issues.apache.org/jira/browse/SPARK-24934?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16562126#comment-16562126
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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<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}
> 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<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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