beliefer commented on a change in pull request #29800:
URL: https://github.com/apache/spark/pull/29800#discussion_r513138536
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowFunctionFrame.scala
##########
@@ -151,10 +173,93 @@ final class OffsetWindowFunctionFrame(
}
inputIndex += 1
}
+}
- override def currentLowerBound(): Int = throw new
UnsupportedOperationException()
+/**
+ * The unbounded offset window frame is an internal window frame just used to
optimize the
+ * performance for the window function that returns the value of the input
column offset
+ * by a number of rows within the partition and has specified ROWS BETWEEN
UNBOUNDED PRECEDING
+ * AND UNBOUNDED FOLLOWING. The internal window frame is not a popular window
frame cannot be
+ * specified and used directly by the users.
+ * The unbounded offset window frame calculates frames containing NTH_VALUE
statements.
+ * The unbounded offset window frame return the same value for all rows in the
window partition.
+ */
+class UnboundedOffsetWindowFunctionFrame(
+ target: InternalRow,
+ ordinal: Int,
+ expressions: Array[OffsetWindowSpec],
+ inputSchema: Seq[Attribute],
+ newMutableProjection: (Seq[Expression], Seq[Attribute]) =>
MutableProjection,
+ offset: Int)
+ extends OffsetWindowFunctionFrameBase(
+ target, ordinal, expressions, inputSchema, newMutableProjection, offset) {
- override def currentUpperBound(): Int = throw new
UnsupportedOperationException()
+ override def prepare(rows: ExternalAppendOnlyUnsafeRowArray): Unit = {
+ input = rows
+ if (offset > input.length) {
+ fillDefaultValue(EmptyRow)
+ } else {
+ inputIterator = input.generateIterator()
+ // drain the first few rows if offset is larger than one
+ inputIndex = 0
+ while (inputIndex < offset - 1) {
+ if (inputIterator.hasNext) inputIterator.next()
+ inputIndex += 1
+ }
+ val r = WindowFunctionFrame.getNextOrNull(inputIterator)
+ projection(r)
+ }
+ }
+
+ override def write(index: Int, current: InternalRow): Unit = {
+ // The results are the same for each row in the partition, and have been
evaluated in prepare.
+ // Don't need to recalculate here.
+ }
+}
+
+/**
+ * The unbounded preceding offset window frame is an internal window frame
just used to optimize
+ * the performance for the window function that returns the value of the input
column offset
+ * by a number of rows within the partition and has specified ROWS BETWEEN
UNBOUNDED PRECEDING
+ * AND CURRENT ROW. The internal window frame is not a popular window frame
cannot be specified
+ * and used directly by the users.
+ * The unbounded preceding offset window frame calculates frames containing
NTH_VALUE statements.
+ * The unbounded preceding offset window frame return the same value for rows
which index
+ * (starting from 1) equal to or greater than offset in the window partition.
+ */
+class UnboundedPrecedingOffsetWindowFunctionFrame(
+ target: InternalRow,
+ ordinal: Int,
+ expressions: Array[OffsetWindowSpec],
+ inputSchema: Seq[Attribute],
+ newMutableProjection: (Seq[Expression], Seq[Attribute]) =>
MutableProjection,
+ offset: Int)
+ extends OffsetWindowFunctionFrameBase(
+ target, ordinal, expressions, inputSchema, newMutableProjection, offset) {
+
+ var selectedRow: UnsafeRow = null
+
+ override def prepare(rows: ExternalAppendOnlyUnsafeRowArray): Unit = {
+ input = rows
+ inputIterator = input.generateIterator()
+ // drain the first few rows if offset is larger than one
+ inputIndex = 0
+ while (inputIndex < offset - 1) {
+ if (inputIterator.hasNext) inputIterator.next()
+ inputIndex += 1
+ }
+ if (inputIndex >= 0 && inputIndex < input.length) {
Review comment:
Yes.
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