infoankitp commented on code in PR #38865:
URL: https://github.com/apache/spark/pull/38865#discussion_r1037736311
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala:
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@@ -4600,3 +4600,69 @@ case class ArrayExcept(left: Expression, right:
Expression) extends ArrayBinaryL
override protected def withNewChildrenInternal(
newLeft: Expression, newRight: Expression): ArrayExcept = copy(left =
newLeft, right = newRight)
}
+
+/**
+ * Given an array, and another element append the element at the end of the
array.
+ */
+@ExpressionDescription(
+ usage = "_FUNC_(expr, expr) - Prepend the element",
+ examples = """
+ Examples:
+ > SELECT _FUNC_(array('b', 'd', 'c', 'a'), array(1, 2, 3, 4));
+
+ """,
+ since = "3.4.0",
+ group = "collection_funcs")
+case class ArrayAppend(left: Expression, right: Expression)
+ extends BinaryExpression
+ with ImplicitCastInputTypes {
+ override def prettyName: String = "array_append"
+ override def inputTypes: Seq[AbstractDataType] = Seq(ArrayType, AnyDataType)
+
+ override def checkInputDataTypes(): TypeCheckResult = {
+ (left.dataType, right.dataType) match {
+ case (ArrayType(e1, _), (e2)) if e1.sameType(e2) =>
+ TypeCheckResult.TypeCheckSuccess
+ case _ =>
Review Comment:
@LuciferYang Thanks for the review! Arrays are more strongly typed in Spark
! In case when we are getting mixed types of elements to append in the array,
should we change the type of the array itself ?
Also, we cannot create a column with Array[AnyDataType] like below, we will
eventually get an error
`scala> val df3 = Seq((Array("a", "b", 2, 5d), 3)).toDF("a", "b")
org.apache.spark.SparkUnsupportedOperationException: No Encoder found for Any
- array element class: "java.lang.Object"
- field (class: "scala.Array", name: "_1")
- root class: "scala.Tuple2"
`
Which is why I thought that its better to analyze the types of the array at
the start itself and raise if the types do not match.
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