Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/9819#discussion_r47035762
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala
---
@@ -328,3 +281,222 @@ object FrameBoundaryExtractor {
case _ => None
}
}
+
+/**
+ * A window function is a function that can only be evaluated in the
context of a window operator.
+ */
+trait WindowFunction extends Expression {
+ /** Frame in which the window operator must be executed. */
+ def frame: WindowFrame = UnspecifiedFrame
+}
+
+abstract class OffsetWindowFunction
+ extends Expression with WindowFunction with Unevaluable with
ImplicitCastInputTypes {
+ val input: Expression
+ val default: Expression
+ val offset: Expression
+ val offsetSign: Int
+
+ override def children: Seq[Expression] = Seq(input, offset, default)
+
+ override def foldable: Boolean = input.foldable && (default == null ||
default.foldable)
+
+ override def nullable: Boolean = input.nullable && (default == null ||
default.nullable)
+
+ override lazy val frame = {
+ // This will be triggered by the Analyzer.
+ val offsetValue = offset.eval() match {
+ case o: Int => o
+ case x => throw new AnalysisException(
+ s"Offset expression must be a foldable integer expression: $x")
+ }
+ val boundary = ValueFollowing(offsetSign * offsetValue)
+ SpecifiedWindowFrame(RowFrame, boundary, boundary)
+ }
+
+ override def dataType: DataType = input.dataType
+
+ override def inputTypes: Seq[AbstractDataType] =
+ Seq(AnyDataType, IntegerType, TypeCollection(input.dataType, NullType))
--- End diff --
Should default have the same type as input?
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