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https://issues.apache.org/jira/browse/SPARK-24934?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Wenchen Fan updated SPARK-24934:
--------------------------------
Priority: Critical (was: Major)
> 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
> Priority: Critical
> Labels: correctness
>
> 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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