beliefer commented on code in PR #38874:
URL: https://github.com/apache/spark/pull/38874#discussion_r1047981989
##########
sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala:
##########
@@ -5237,6 +5237,53 @@ class DataFrameFunctionsSuite extends QueryTest with
SharedSparkSession {
)
)
}
+
+ test("test array_compact") {
+ val df = Seq(
+ (Array[Integer](null, 1, 2, null, 3, 4),
+ Array("a", null, "b", null, "c", "d"), Array("", "")),
+ (Array.empty[Integer], Array("1.0", "2.2", "3.0"), Array.empty[String]),
+ (Array[Integer](null, null, null), null, null)
+ ).toDF("a", "b", "c")
+
+ checkAnswer(
+ df.select(array_compact($"a"),
+ array_compact($"b"), array_compact($"c")),
+ Seq(Row(Seq(1, 2, 3, 4), Seq("a", "b", "c", "d"), Seq("", "")),
+ Row(Seq.empty[Integer], Seq("1.0", "2.2", "3.0"), Seq.empty[String]),
+ Row(Seq.empty[Integer], null, null))
+ )
+
+ checkAnswer(
+ OneRowRelation().selectExpr("array_compact(array(1.0D, 2.0D, null))"),
+ Seq(
+ Row(Seq(1.0, 2.0))
+ )
+ )
+
+ // complex data type
+ checkAnswer(
+ OneRowRelation().
+ selectExpr("array_compact(array(array(1, null,3), null, array(null, 2,
3)))"),
+ Seq(
+ Row(Seq(Seq(1, null, 3), Seq(null, 2, 3))))
+ )
+
+ // unsupported data type
+ val invalid_Datatype_df = Seq(1, 2, 3).toDF("a")
+ checkErrorMatchPVals(
+ exception = intercept[AnalysisException] {
+ invalid_Datatype_df.select(array_compact($"a"))
+ },
+ errorClass = "DATATYPE_MISMATCH.UNEXPECTED_INPUT_TYPE",
+ parameters = Map(
+ "sqlExpr" -> """"filter\(a, lambdafunction\(\(x_\d+ IS NOT NULL\),
x_\d+\)\)"""",
Review Comment:
You can still implement `def checkInputDataTypes(): TypeCheckResult`.
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