Hyukjin Kwon created SPARK-24934:
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Summary: 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
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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