Github user EntilZha commented on a diff in the pull request:
https://github.com/apache/spark/pull/7580#discussion_r35293144
--- Diff:
sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala ---
@@ -298,4 +298,28 @@ class DataFrameFunctionsSuite extends QueryTest {
Seq(Row(2), Row(0), Row(3))
)
}
+
+ test("array contains function") {
+ val df = Seq(
+ (Seq[Int](1, 2), "x"),
+ (Seq[Int](), "y"),
+ (null, "z")
+ ).toDF("a", "b")
+ checkAnswer(
+ df.select(array_contains("a", 1)),
+ Seq(Row(true), Row(false), Row(false))
+ )
+ checkAnswer(
+ df.selectExpr("array_contains(a, 1)"),
+ Seq(Row(true), Row(false), Row(false))
+ )
+ // checkAnswer(
+ // df.select(array_contains("a", null)),
--- End diff --
Unfortunately that did not do the trick. The issue is that this still
returns an error:
```scala
val elementType = left.dataType.asInstanceOf[ArrayType].elementType
if (!elementType.acceptsType(right.dataType)) {
TypeCheckResult.TypeCheckFailure(
s"type of value must match array type " +
s"${elementType.simpleString}, not " +
s"${right.dataType.simpleString}")
} else {
TypeCheckResult.TypeCheckSuccess
}
```
I think we should leave it `checkInputTypes` alone and only use
`ExpectsInputTypes` to enforce the argument types to be `Seq(ArrayType,
AnyDataType)`.
This incurs a performance penalty when based on type information alone the
value cannot exist in the array (eg testing if a string is in an integer
array). Since `acceptsTypes` doesn't seem to be loose enough though, this seems
like the only option.
Based on looking at `ImplicitTypeCasts` and `implicitCast` in
`HiveTypeCoercion.scala`, it is fairly liberal with casts. Since hive does
enforce type quality, I think it would be a bad idea to do implicit type
casting, so only use `ExpectsInputTypes`.
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