Github user chenghao-intel commented on a diff in the pull request:
https://github.com/apache/spark/pull/6938#discussion_r33032169
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
---
@@ -312,3 +315,90 @@ case class Logarithm(left: Expression, right:
Expression)
"""
}
}
+
+case class Round(child: Expression, scale: Expression) extends Expression {
+
+ def this(child: Expression) = {
+ this(child, Literal(0))
+ }
+
+ def children: Seq[Expression] = Seq(child, scale)
+
+ def nullable: Boolean = true
+
+ private lazy val scaleV = scale.asInstanceOf[Literal].value
+ private lazy val _scale = if (scaleV != null) scaleV.asInstanceOf[Int]
else 0
+
+ override lazy val dataType: DataType = {
+ child.dataType match {
+ case StringType | BinaryType => DoubleType
+ case DecimalType.Fixed(p, s) => DecimalType(p, _scale)
+ case t => t
+ }
+ }
+
+ override def checkInputDataTypes(): TypeCheckResult = {
+ child.dataType match {
+ case _: NumericType | NullType | BinaryType | StringType => //
satisfy requirement
+ case dt =>
+ return TypeCheckFailure(s"Only numeric, string or binary data
types" +
+ s" are allowed for ROUND function, got $dt")
+ }
+ scale match {
+ case Literal(value, LongType) =>
+ if (value.asInstanceOf[Long] < Int.MinValue ||
value.asInstanceOf[Long] > Int.MaxValue) {
+ return TypeCheckFailure("ROUND scale argument out of allowed
range")
+ }
+ case Literal(_, _: IntegralType) | Literal(_, NullType) => //
satisfy requirement
+ case child =>
+ if (child.find { case _: AttributeReference => true; case _ =>
false } != None) {
+ return TypeCheckFailure("Only Integral Literal or Null Literal "
+
+ s"are allowed for ROUND scale arguments, got
${child.dataType}")
+ }
+ }
+ TypeCheckSuccess
+ }
+
+ def eval(input: InternalRow): Any = {
+ val evalE = child.eval(input)
+
+ if (evalE == null || scaleV == null) return null
+
+ children(0).dataType match {
--- End diff --
An optimal way to this move the data type pattern matching out of `eval`,
and create the function for the `round`, so we can avoid pattern matching for
every row.
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