infoankitp commented on code in PR #38865:
URL: https://github.com/apache/spark/pull/38865#discussion_r1043043495
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sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala:
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@@ -4600,3 +4600,133 @@ 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_(array, element) - Append the element",
+ examples =
+ """
+ Examples:
+ > SELECT _FUNC_(array('b', 'd', 'c', 'a'), 'd');
+ ["b","d","c","a","d"]
+
+ """,
+ since = "3.4.0",
+ group = "array_funcs")
+case class ArrayAppend(left: Expression, right: Expression)
+ extends BinaryExpression
+ with ImplicitCastInputTypes
+ with ComplexTypeMergingExpression
+ with QueryErrorsBase {
+ override def prettyName: String = "array_append"
+
+ override def inputTypes: Seq[AbstractDataType] = {
+ (left.dataType, right.dataType) match {
+ case (ArrayType(e1, hasNull), e2) =>
+ TypeCoercion.findTightestCommonType(e1, e2) match {
+ case Some(dt) => Seq(ArrayType(dt, hasNull), dt)
+ case _ => Seq.empty
+ }
+ case _ => Seq.empty
+ }
+ }
+
+ override def eval(input: InternalRow): Any = {
+ val value1 = left.eval(input)
+ val value2 = right.eval(input)
+ if (value1 == null) {
+ null
+ } else {
+ nullSafeEval(value1, value2)
+ }
+ }
+
+ override def checkInputDataTypes(): TypeCheckResult = {
+ (left.dataType, right.dataType) match {
+ case (ArrayType(e1, _), e2) => if (e1.sameType(e2)) {
+ TypeCheckResult.TypeCheckSuccess
+ }
+ else {
+ DataTypeMismatch(
+ errorSubClass = "ARRAY_FUNCTION_DIFF_TYPES",
+ messageParameters = Map(
+ "functionName" -> toSQLId(prettyName),
+ "leftType" -> toSQLType(left.dataType),
+ "rightType" -> toSQLType(right.dataType),
+ "dataType" -> toSQLType(ArrayType)
+ ))
+ }
+ case _ =>
+ DataTypeMismatch(
+ errorSubClass = "UNEXPECTED_INPUT_TYPE",
+ messageParameters = Map(
+ "paramIndex" -> "0",
+ "requiredType" -> toSQLType(ArrayType),
+ "inputSql" -> toSQLExpr(left),
+ "inputType" -> toSQLType(left.dataType)
+ )
+ )
+ }
+ }
+
+ protected def withNewChildrenInternal(newLeft: Expression, newRight:
Expression): ArrayAppend =
+ copy(left = newLeft, right = newRight)
+
+ override protected def nullSafeEval(input1: Any, input2: Any): Any = {
+ val arrayData = input1.asInstanceOf[ArrayData]
+ val arrayElementType = dataType.asInstanceOf[ArrayType].elementType
+ val elementData = input2
+ val numberOfElements = arrayData.numElements() + 1
+ if (numberOfElements > ByteArrayMethods.MAX_ROUNDED_ARRAY_LENGTH) {
+ throw
QueryExecutionErrors.concatArraysWithElementsExceedLimitError(numberOfElements)
+ }
+ val finalData = new Array[Any](numberOfElements)
+ arrayData.foreach(arrayElementType, finalData.update)
+ finalData.update(numberOfElements - 1, elementData)
+ new GenericArrayData(finalData)
+ }
+
+ override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
+
+ val f = (left: String, right: String) => {
+ val expr = ctx.addReferenceObj("arraysAppendExpr", this)
+ s"${ev.value} = (ArrayData)$expr.nullSafeEval($left, $right);"
+ }
+
+ val leftGen = left.genCode(ctx)
+ val rightGen = right.genCode(ctx)
Review Comment:
I think its okay if we are not consistent with the behaviour we have in
snowflake and are more consistent to the existing high order functions in spark
itself. In that we won't need to override the eval function also, we won't need
to write the codeGen function completely. We will just be able to create
nullSafeCodeGen function and call the function we need to call.
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