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new bdeae870674 [SPARK-44341][SQL][PYTHON][FOLLOWUP] Move the base trait
WindowEvaluatorFactoryBase to a single file
bdeae870674 is described below
commit bdeae87067452bb41f4776c4ab444a9d9645fdfc
Author: Jiaan Geng <belie...@163.com>
AuthorDate: Fri Jul 21 21:21:15 2023 +0800
[SPARK-44341][SQL][PYTHON][FOLLOWUP] Move the base trait
WindowEvaluatorFactoryBase to a single file
### What changes were proposed in this pull request?
https://github.com/apache/spark/pull/41939 defined the computing logic
through PartitionEvaluator API and use it in `WindowExec` and
`WindowInPandasExec`.
According to the comment
https://github.com/apache/spark/pull/41939#discussion_r1270194752, this PR want
move the base trait `WindowEvaluatorFactoryBase` to a single file.
### Why are the changes needed?
Improve the code.
### Does this PR introduce _any_ user-facing change?
'No'.
Just update inner implementation.
### How was this patch tested?
N/A
Closes #42106 from beliefer/SPARK-44341_followup.
Authored-by: Jiaan Geng <belie...@163.com>
Signed-off-by: Wenchen Fan <wenc...@databricks.com>
---
.../execution/window/WindowEvaluatorFactory.scala | 268 +--------------------
...tory.scala => WindowEvaluatorFactoryBase.scala} | 130 +---------
2 files changed, 2 insertions(+), 396 deletions(-)
diff --git
a/sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowEvaluatorFactory.scala
b/sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowEvaluatorFactory.scala
index 913f8762c79..fb4ea7f35c0 100644
---
a/sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowEvaluatorFactory.scala
+++
b/sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowEvaluatorFactory.scala
@@ -17,278 +17,12 @@
package org.apache.spark.sql.execution.window
-import scala.collection.mutable
-import scala.collection.mutable.ArrayBuffer
-
import org.apache.spark.{PartitionEvaluator, PartitionEvaluatorFactory}
import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.{Add,
AggregateWindowFunction, Ascending, Attribute, BoundReference, CurrentRow,
DateAdd, DateAddYMInterval, DecimalAddNoOverflowCheck, Descending, Expression,
FrameLessOffsetWindowFunction, FrameType, IdentityProjection, IntegerLiteral,
JoinedRow, MutableProjection, NamedExpression, OffsetWindowFunction,
PythonFuncExpression, RangeFrame, RowFrame, RowOrdering, SortOrder,
SpecificInternalRow, SpecifiedWindowFrame, TimeAdd, TimestampAddYMIn [...]
-import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression
+import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression,
JoinedRow, NamedExpression, SortOrder, SpecificInternalRow, UnsafeProjection,
UnsafeRow}
import org.apache.spark.sql.execution.ExternalAppendOnlyUnsafeRowArray
import org.apache.spark.sql.execution.metric.SQLMetric
import org.apache.spark.sql.internal.SQLConf
-import org.apache.spark.sql.types.{CalendarIntervalType, DateType,
DayTimeIntervalType, DecimalType, IntegerType, TimestampNTZType, TimestampType,
YearMonthIntervalType}
-import org.apache.spark.util.collection.Utils
-
-trait WindowEvaluatorFactoryBase {
- def windowExpression: Seq[NamedExpression]
- def partitionSpec: Seq[Expression]
- def orderSpec: Seq[SortOrder]
- def childOutput: Seq[Attribute]
- def spillSize: SQLMetric
-
- /**
- * Create the resulting projection.
- *
- * This method uses Code Generation. It can only be used on the executor
side.
- *
- * @param expressions unbound ordered function expressions.
- * @return the final resulting projection.
- */
- protected def createResultProjection(expressions: Seq[Expression]):
UnsafeProjection = {
- val references = expressions.zipWithIndex.map { case (e, i) =>
- // Results of window expressions will be on the right side of child's
output
- BoundReference(childOutput.size + i, e.dataType, e.nullable)
- }
- val unboundToRefMap = Utils.toMap(expressions, references)
- val patchedWindowExpression =
windowExpression.map(_.transform(unboundToRefMap))
- UnsafeProjection.create(
- childOutput ++ patchedWindowExpression,
- childOutput)
- }
-
- /**
- * Create a bound ordering object for a given frame type and offset. A bound
ordering object is
- * used to determine which input row lies within the frame boundaries of an
output row.
- *
- * This method uses Code Generation. It can only be used on the executor
side.
- *
- * @param frame to evaluate. This can either be a Row or Range frame.
- * @param bound with respect to the row.
- * @param timeZone the session local timezone for time related calculations.
- * @return a bound ordering object.
- */
- private def createBoundOrdering(
- frame: FrameType, bound: Expression, timeZone: String): BoundOrdering = {
- (frame, bound) match {
- case (RowFrame, CurrentRow) =>
- RowBoundOrdering(0)
-
- case (RowFrame, IntegerLiteral(offset)) =>
- RowBoundOrdering(offset)
-
- case (RowFrame, _) =>
- throw new IllegalStateException(s"Unhandled bound in windows
expressions: $bound")
-
- case (RangeFrame, CurrentRow) =>
- val ordering = RowOrdering.create(orderSpec, childOutput)
- RangeBoundOrdering(ordering, IdentityProjection, IdentityProjection)
-
- case (RangeFrame, offset: Expression) if orderSpec.size == 1 =>
- // Use only the first order expression when the offset is non-null.
- val sortExpr = orderSpec.head
- val expr = sortExpr.child
-
- // Create the projection which returns the current 'value'.
- val current = MutableProjection.create(expr :: Nil, childOutput)
-
- // Flip the sign of the offset when processing the order is descending
- val boundOffset = sortExpr.direction match {
- case Descending => UnaryMinus(offset)
- case Ascending => offset
- }
-
- // Create the projection which returns the current 'value' modified by
adding the offset.
- val boundExpr = (expr.dataType, boundOffset.dataType) match {
- case (DateType, IntegerType) => DateAdd(expr, boundOffset)
- case (DateType, _: YearMonthIntervalType) => DateAddYMInterval(expr,
boundOffset)
- case (TimestampType | TimestampNTZType, CalendarIntervalType) =>
- TimeAdd(expr, boundOffset, Some(timeZone))
- case (TimestampType | TimestampNTZType, _: YearMonthIntervalType) =>
- TimestampAddYMInterval(expr, boundOffset, Some(timeZone))
- case (TimestampType | TimestampNTZType, _: DayTimeIntervalType) =>
- TimeAdd(expr, boundOffset, Some(timeZone))
- case (d: DecimalType, _: DecimalType) =>
DecimalAddNoOverflowCheck(expr, boundOffset, d)
- case (a, b) if a == b => Add(expr, boundOffset)
- }
- val bound = MutableProjection.create(boundExpr :: Nil, childOutput)
-
- // Construct the ordering. This is used to compare the result of
current value projection
- // to the result of bound value projection. This is done manually
because we want to use
- // Code Generation (if it is enabled).
- val boundSortExprs = sortExpr.copy(BoundReference(0, expr.dataType,
expr.nullable)) :: Nil
- val ordering = RowOrdering.create(boundSortExprs, Nil)
- RangeBoundOrdering(ordering, current, bound)
-
- case (RangeFrame, _) =>
- throw new IllegalStateException("Non-Zero range offsets are not
supported for windows " +
- "with multiple order expressions.")
- }
- }
-
- /**
- * Collection containing an entry for each window frame to process. Each
entry contains a frame's
- * [[WindowExpression]]s and factory function for the
[[WindowFunctionFrame]].
- */
- protected lazy val windowFrameExpressionFactoryPairs = {
- type FrameKey = (String, FrameType, Expression, Expression,
Seq[Expression])
- type ExpressionBuffer = mutable.Buffer[Expression]
- val framedFunctions = mutable.Map.empty[FrameKey, (ExpressionBuffer,
ExpressionBuffer)]
-
- // Add a function and its function to the map for a given frame.
- def collect(tpe: String, fr: SpecifiedWindowFrame, e: Expression, fn:
Expression): Unit = {
- val key = fn match {
- // This branch is used for Lead/Lag to support ignoring null and
optimize the performance
- // for NthValue ignoring null.
- // All window frames move in rows. If there are multiple Leads, Lags
or NthValues acting on
- // a row and operating on different input expressions, they should not
be moved uniformly
- // by row. Therefore, we put these functions in different window
frames.
- case f: OffsetWindowFunction if f.ignoreNulls =>
- (tpe, fr.frameType, fr.lower, fr.upper,
f.children.map(_.canonicalized))
- case _ => (tpe, fr.frameType, fr.lower, fr.upper, Nil)
- }
- val (es, fns) = framedFunctions.getOrElseUpdate(
- key, (ArrayBuffer.empty[Expression], ArrayBuffer.empty[Expression]))
- es += e
- fns += fn
- }
-
- // Collect all valid window functions and group them by their frame.
- windowExpression.foreach { x =>
- x.foreach {
- case e@WindowExpression(function, spec) =>
- val frame =
spec.frameSpecification.asInstanceOf[SpecifiedWindowFrame]
- function match {
- case AggregateExpression(f, _, _, _, _) => collect("AGGREGATE",
frame, e, f)
- case f: FrameLessOffsetWindowFunction =>
- collect("FRAME_LESS_OFFSET", f.fakeFrame, e, f)
- case f: OffsetWindowFunction if frame.frameType == RowFrame &&
- frame.lower == UnboundedPreceding =>
- frame.upper match {
- case UnboundedFollowing => collect("UNBOUNDED_OFFSET",
f.fakeFrame, e, f)
- case CurrentRow => collect("UNBOUNDED_PRECEDING_OFFSET",
f.fakeFrame, e, f)
- case _ => collect("AGGREGATE", frame, e, f)
- }
- case f: AggregateWindowFunction => collect("AGGREGATE", frame, e,
f)
- case f => throw new IllegalStateException(s"Unsupported window
function: $f")
- }
- case _ =>
- }
- }
-
- // Map the groups to a (unbound) expression and frame factory pair.
- var numExpressions = 0
- val timeZone = SQLConf.get.sessionLocalTimeZone
- framedFunctions.toSeq.map {
- case (key, (expressions, functionSeq)) =>
- val ordinal = numExpressions
- val functions = functionSeq.toArray
-
- // Construct an aggregate processor if we need one.
- // Currently we don't allow mixing of Pandas UDF and SQL aggregation
functions
- // in a single Window physical node. Therefore, we can assume no SQL
aggregation
- // functions if Pandas UDF exists. In the future, we might mix Pandas
UDF and SQL
- // aggregation function in a single physical node.
- def processor = if
(functions.exists(_.isInstanceOf[PythonFuncExpression])) {
- null
- } else {
- AggregateProcessor(
- functions,
- ordinal,
- childOutput,
- (expressions, schema) =>
- MutableProjection.create(expressions, schema))
- }
-
- // Create the factory to produce WindowFunctionFrame.
- val factory = key match {
- // Frameless offset Frame
- case ("FRAME_LESS_OFFSET", _, IntegerLiteral(offset), _, expr) =>
- target: InternalRow =>
- new FrameLessOffsetWindowFunctionFrame(
- target,
- ordinal,
- // OFFSET frame functions are guaranteed be
OffsetWindowFunction.
- functions.map(_.asInstanceOf[OffsetWindowFunction]),
- childOutput,
- (expressions, schema) =>
- MutableProjection.create(expressions, schema),
- offset,
- expr.nonEmpty)
- case ("UNBOUNDED_OFFSET", _, IntegerLiteral(offset), _, expr) =>
- target: InternalRow => {
- new UnboundedOffsetWindowFunctionFrame(
- target,
- ordinal,
- // OFFSET frame functions are guaranteed be
OffsetWindowFunction.
- functions.map(_.asInstanceOf[OffsetWindowFunction]),
- childOutput,
- (expressions, schema) =>
- MutableProjection.create(expressions, schema),
- offset,
- expr.nonEmpty)
- }
- case ("UNBOUNDED_PRECEDING_OFFSET", _, IntegerLiteral(offset), _,
expr) =>
- target: InternalRow => {
- new UnboundedPrecedingOffsetWindowFunctionFrame(
- target,
- ordinal,
- // OFFSET frame functions are guaranteed be
OffsetWindowFunction.
- functions.map(_.asInstanceOf[OffsetWindowFunction]),
- childOutput,
- (expressions, schema) =>
- MutableProjection.create(expressions, schema),
- offset,
- expr.nonEmpty)
- }
-
- // Entire Partition Frame.
- case ("AGGREGATE", _, UnboundedPreceding, UnboundedFollowing, _) =>
- target: InternalRow => {
- new UnboundedWindowFunctionFrame(target, processor)
- }
-
- // Growing Frame.
- case ("AGGREGATE", frameType, UnboundedPreceding, upper, _) =>
- target: InternalRow => {
- new UnboundedPrecedingWindowFunctionFrame(
- target,
- processor,
- createBoundOrdering(frameType, upper, timeZone))
- }
-
- // Shrinking Frame.
- case ("AGGREGATE", frameType, lower, UnboundedFollowing, _) =>
- target: InternalRow => {
- new UnboundedFollowingWindowFunctionFrame(
- target,
- processor,
- createBoundOrdering(frameType, lower, timeZone))
- }
-
- // Moving Frame.
- case ("AGGREGATE", frameType, lower, upper, _) =>
- target: InternalRow => {
- new SlidingWindowFunctionFrame(
- target,
- processor,
- createBoundOrdering(frameType, lower, timeZone),
- createBoundOrdering(frameType, upper, timeZone))
- }
-
- case _ =>
- throw new IllegalStateException(s"Unsupported factory: $key")
- }
-
- // Keep track of the number of expressions. This is a side-effect in a
map...
- numExpressions += expressions.size
-
- // Create the Window Expression - Frame Factory pair.
- (expressions, factory)
- }
- }
-
-}
class WindowEvaluatorFactory(
val windowExpression: Seq[NamedExpression],
diff --git
a/sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowEvaluatorFactory.scala
b/sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowEvaluatorFactoryBase.scala
similarity index 73%
copy from
sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowEvaluatorFactory.scala
copy to
sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowEvaluatorFactoryBase.scala
index 913f8762c79..4491861dd9d 100644
---
a/sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowEvaluatorFactory.scala
+++
b/sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowEvaluatorFactoryBase.scala
@@ -20,11 +20,9 @@ package org.apache.spark.sql.execution.window
import scala.collection.mutable
import scala.collection.mutable.ArrayBuffer
-import org.apache.spark.{PartitionEvaluator, PartitionEvaluatorFactory}
import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.{Add,
AggregateWindowFunction, Ascending, Attribute, BoundReference, CurrentRow,
DateAdd, DateAddYMInterval, DecimalAddNoOverflowCheck, Descending, Expression,
FrameLessOffsetWindowFunction, FrameType, IdentityProjection, IntegerLiteral,
JoinedRow, MutableProjection, NamedExpression, OffsetWindowFunction,
PythonFuncExpression, RangeFrame, RowFrame, RowOrdering, SortOrder,
SpecificInternalRow, SpecifiedWindowFrame, TimeAdd, TimestampAddYMIn [...]
+import org.apache.spark.sql.catalyst.expressions.{Add,
AggregateWindowFunction, Ascending, Attribute, BoundReference, CurrentRow,
DateAdd, DateAddYMInterval, DecimalAddNoOverflowCheck, Descending, Expression,
FrameLessOffsetWindowFunction, FrameType, IdentityProjection, IntegerLiteral,
MutableProjection, NamedExpression, OffsetWindowFunction, PythonFuncExpression,
RangeFrame, RowFrame, RowOrdering, SortOrder, SpecifiedWindowFrame, TimeAdd,
TimestampAddYMInterval, UnaryMinus, UnboundedFol [...]
import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression
-import org.apache.spark.sql.execution.ExternalAppendOnlyUnsafeRowArray
import org.apache.spark.sql.execution.metric.SQLMetric
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.types.{CalendarIntervalType, DateType,
DayTimeIntervalType, DecimalType, IntegerType, TimestampNTZType, TimestampType,
YearMonthIntervalType}
@@ -289,129 +287,3 @@ trait WindowEvaluatorFactoryBase {
}
}
-
-class WindowEvaluatorFactory(
- val windowExpression: Seq[NamedExpression],
- val partitionSpec: Seq[Expression],
- val orderSpec: Seq[SortOrder],
- val childOutput: Seq[Attribute],
- val spillSize: SQLMetric)
- extends PartitionEvaluatorFactory[InternalRow, InternalRow] with
WindowEvaluatorFactoryBase {
-
- override def createEvaluator(): PartitionEvaluator[InternalRow, InternalRow]
= {
- new WindowPartitionEvaluator()
- }
-
- class WindowPartitionEvaluator extends PartitionEvaluator[InternalRow,
InternalRow] {
- private val conf: SQLConf = SQLConf.get
-
- // Unwrap the window expressions and window frame factories from the map.
- private val expressions = windowFrameExpressionFactoryPairs.flatMap(_._1)
- private val factories = windowFrameExpressionFactoryPairs.map(_._2).toArray
- private val inMemoryThreshold = conf.windowExecBufferInMemoryThreshold
- private val spillThreshold = conf.windowExecBufferSpillThreshold
-
- override def eval(
- partitionIndex: Int,
- inputs: Iterator[InternalRow]*): Iterator[InternalRow] = {
- val stream = inputs.head
- new Iterator[InternalRow] {
-
- // Get all relevant projections.
- val result = createResultProjection(expressions)
- val grouping = UnsafeProjection.create(partitionSpec, childOutput)
-
- // Manage the stream and the grouping.
- var nextRow: UnsafeRow = null
- var nextGroup: UnsafeRow = null
- var nextRowAvailable: Boolean = false
- private[this] def fetchNextRow(): Unit = {
- nextRowAvailable = stream.hasNext
- if (nextRowAvailable) {
- nextRow = stream.next().asInstanceOf[UnsafeRow]
- nextGroup = grouping(nextRow)
- } else {
- nextRow = null
- nextGroup = null
- }
- }
- fetchNextRow()
-
- // Manage the current partition.
- val buffer: ExternalAppendOnlyUnsafeRowArray =
- new ExternalAppendOnlyUnsafeRowArray(inMemoryThreshold,
spillThreshold)
-
- var bufferIterator: Iterator[UnsafeRow] = _
-
- val windowFunctionResult = new
SpecificInternalRow(expressions.map(_.dataType))
- val frames = factories.map(_(windowFunctionResult))
- val numFrames = frames.length
- private[this] def fetchNextPartition(): Unit = {
- // Collect all the rows in the current partition.
- // Before we start to fetch new input rows, make a copy of nextGroup.
- val currentGroup = nextGroup.copy()
-
- // clear last partition
- buffer.clear()
-
- while (nextRowAvailable && nextGroup == currentGroup) {
- buffer.add(nextRow)
- fetchNextRow()
- }
-
- // Setup the frames.
- var i = 0
- while (i < numFrames) {
- frames(i).prepare(buffer)
- i += 1
- }
-
- // Setup iteration
- rowIndex = 0
- bufferIterator = buffer.generateIterator()
- }
-
- // Iteration
- var rowIndex = 0
-
- override final def hasNext: Boolean = {
- val found = (bufferIterator != null && bufferIterator.hasNext) ||
nextRowAvailable
- if (!found) {
- // clear final partition
- buffer.clear()
- spillSize += buffer.spillSize
- }
- found
- }
-
- val join = new JoinedRow
- override final def next(): InternalRow = {
- // Load the next partition if we need to.
- if ((bufferIterator == null || !bufferIterator.hasNext) &&
nextRowAvailable) {
- fetchNextPartition()
- }
-
- if (bufferIterator.hasNext) {
- val current = bufferIterator.next()
-
- // Get the results for the window frames.
- var i = 0
- while (i < numFrames) {
- frames(i).write(rowIndex, current)
- i += 1
- }
-
- // 'Merge' the input row with the window function result
- join(current, windowFunctionResult)
- rowIndex += 1
-
- // Return the projection.
- result(join)
- } else {
- throw new NoSuchElementException
- }
- }
- }
- }
- }
-}
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