diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 3441ccf53b45b7ced8f1d2ada057b7f05d2f80f9..7cfae5ce283bfa7e589fd0310b4b9ac44b62f33f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -20,7 +20,6 @@ package org.apache.spark.sql.execution
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{execution, SaveMode, Strategy}
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.catalog.CatalogTableType
 import org.apache.spark.sql.catalyst.encoders.RowEncoder
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning._
@@ -387,7 +386,7 @@ abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       case e @ logical.Expand(_, _, child) =>
         execution.ExpandExec(e.projections, e.output, planLater(child)) :: Nil
       case logical.Window(windowExprs, partitionSpec, orderSpec, child) =>
-        execution.WindowExec(windowExprs, partitionSpec, orderSpec, planLater(child)) :: Nil
+        execution.window.WindowExec(windowExprs, partitionSpec, orderSpec, planLater(child)) :: Nil
       case logical.Sample(lb, ub, withReplacement, seed, child) =>
         execution.SampleExec(lb, ub, withReplacement, seed, planLater(child)) :: Nil
       case logical.LocalRelation(output, data) =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/WindowExec.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/WindowExec.scala
deleted file mode 100644
index 9d006d21d9440a54ece6b63f698abab5c99690c9..0000000000000000000000000000000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/WindowExec.scala
+++ /dev/null
@@ -1,1013 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.execution
-
-import java.util
-
-import scala.collection.mutable
-import scala.collection.mutable.ArrayBuffer
-
-import org.apache.spark.{SparkEnv, TaskContext}
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.expressions.aggregate._
-import org.apache.spark.sql.catalyst.plans.physical._
-import org.apache.spark.sql.types.IntegerType
-import org.apache.spark.util.collection.unsafe.sort.{UnsafeExternalSorter, UnsafeSorterIterator}
-
-/**
- * This class calculates and outputs (windowed) aggregates over the rows in a single (sorted)
- * partition. The aggregates are calculated for each row in the group. Special processing
- * instructions, frames, are used to calculate these aggregates. Frames are processed in the order
- * specified in the window specification (the ORDER BY ... clause). There are four different frame
- * types:
- * - Entire partition: The frame is the entire partition, i.e.
- *   UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING. For this case, window function will take all
- *   rows as inputs and be evaluated once.
- * - Growing frame: We only add new rows into the frame, i.e. UNBOUNDED PRECEDING AND ....
- *   Every time we move to a new row to process, we add some rows to the frame. We do not remove
- *   rows from this frame.
- * - Shrinking frame: We only remove rows from the frame, i.e. ... AND UNBOUNDED FOLLOWING.
- *   Every time we move to a new row to process, we remove some rows from the frame. We do not add
- *   rows to this frame.
- * - Moving frame: Every time we move to a new row to process, we remove some rows from the frame
- *   and we add some rows to the frame. Examples are:
- *     1 PRECEDING AND CURRENT ROW and 1 FOLLOWING AND 2 FOLLOWING.
- * - Offset frame: The frame consist of one row, which is an offset number of rows away from the
- *   current row. Only [[OffsetWindowFunction]]s can be processed in an offset frame.
- *
- * Different frame boundaries can be used in Growing, Shrinking and Moving frames. A frame
- * boundary can be either Row or Range based:
- * - Row Based: A row based boundary is based on the position of the row within the partition.
- *   An offset indicates the number of rows above or below the current row, the frame for the
- *   current row starts or ends. For instance, given a row based sliding frame with a lower bound
- *   offset of -1 and a upper bound offset of +2. The frame for row with index 5 would range from
- *   index 4 to index 6.
- * - Range based: A range based boundary is based on the actual value of the ORDER BY
- *   expression(s). An offset is used to alter the value of the ORDER BY expression, for
- *   instance if the current order by expression has a value of 10 and the lower bound offset
- *   is -3, the resulting lower bound for the current row will be 10 - 3 = 7. This however puts a
- *   number of constraints on the ORDER BY expressions: there can be only one expression and this
- *   expression must have a numerical data type. An exception can be made when the offset is 0,
- *   because no value modification is needed, in this case multiple and non-numeric ORDER BY
- *   expression are allowed.
- *
- * This is quite an expensive operator because every row for a single group must be in the same
- * partition and partitions must be sorted according to the grouping and sort order. The operator
- * requires the planner to take care of the partitioning and sorting.
- *
- * The operator is semi-blocking. The window functions and aggregates are calculated one group at
- * a time, the result will only be made available after the processing for the entire group has
- * finished. The operator is able to process different frame configurations at the same time. This
- * is done by delegating the actual frame processing (i.e. calculation of the window functions) to
- * specialized classes, see [[WindowFunctionFrame]], which take care of their own frame type:
- * Entire Partition, Sliding, Growing & Shrinking. Boundary evaluation is also delegated to a pair
- * of specialized classes: [[RowBoundOrdering]] & [[RangeBoundOrdering]].
- */
-case class WindowExec(
-    windowExpression: Seq[NamedExpression],
-    partitionSpec: Seq[Expression],
-    orderSpec: Seq[SortOrder],
-    child: SparkPlan)
-  extends UnaryExecNode {
-
-  override def output: Seq[Attribute] =
-    child.output ++ windowExpression.map(_.toAttribute)
-
-  override def requiredChildDistribution: Seq[Distribution] = {
-    if (partitionSpec.isEmpty) {
-      // Only show warning when the number of bytes is larger than 100 MB?
-      logWarning("No Partition Defined for Window operation! Moving all data to a single "
-        + "partition, this can cause serious performance degradation.")
-      AllTuples :: Nil
-    } else ClusteredDistribution(partitionSpec) :: Nil
-  }
-
-  override def requiredChildOrdering: Seq[Seq[SortOrder]] =
-    Seq(partitionSpec.map(SortOrder(_, Ascending)) ++ orderSpec)
-
-  override def outputOrdering: Seq[SortOrder] = child.outputOrdering
-
-  /**
-   * 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 frameType to evaluate. This can either be Row or Range based.
-   * @param offset with respect to the row.
-   * @return a bound ordering object.
-   */
-  private[this] def createBoundOrdering(frameType: FrameType, offset: Int): BoundOrdering = {
-    frameType match {
-      case RangeFrame =>
-        val (exprs, current, bound) = if (offset == 0) {
-          // Use the entire order expression when the offset is 0.
-          val exprs = orderSpec.map(_.child)
-          val buildProjection = () => newMutableProjection(exprs, child.output)
-          (orderSpec, buildProjection(), buildProjection())
-        } else 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 = newMutableProjection(expr :: Nil, child.output)
-          // Flip the sign of the offset when processing the order is descending
-          val boundOffset = sortExpr.direction match {
-            case Descending => -offset
-            case Ascending => offset
-          }
-          // Create the projection which returns the current 'value' modified by adding the offset.
-          val boundExpr = Add(expr, Cast(Literal.create(boundOffset, IntegerType), expr.dataType))
-          val bound = newMutableProjection(boundExpr :: Nil, child.output)
-          (sortExpr :: Nil, current, bound)
-        } else {
-          sys.error("Non-Zero range offsets are not supported for windows " +
-            "with multiple order expressions.")
-        }
-        // 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 sortExprs = exprs.zipWithIndex.map { case (e, i) =>
-          SortOrder(BoundReference(i, e.dataType, e.nullable), e.direction)
-        }
-        val ordering = newOrdering(sortExprs, Nil)
-        RangeBoundOrdering(ordering, current, bound)
-      case RowFrame => RowBoundOrdering(offset)
-    }
-  }
-
-  /**
-   * Collection containing an entry for each window frame to process. Each entry contains a frames'
-   * WindowExpressions and factory function for the WindowFrameFunction.
-   */
-  private[this] lazy val windowFrameExpressionFactoryPairs = {
-    type FrameKey = (String, FrameType, Option[Int], Option[Int])
-    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 = (tpe, fr.frameType, FrameBoundary(fr.frameStart), FrameBoundary(fr.frameEnd))
-      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: AggregateWindowFunction => collect("AGGREGATE", frame, e, f)
-            case f: OffsetWindowFunction => collect("OFFSET", frame, e, f)
-            case f => sys.error(s"Unsupported window function: $f")
-          }
-        case _ =>
-      }
-    }
-
-    // Map the groups to a (unbound) expression and frame factory pair.
-    var numExpressions = 0
-    framedFunctions.toSeq.map {
-      case (key, (expressions, functionSeq)) =>
-        val ordinal = numExpressions
-        val functions = functionSeq.toArray
-
-        // Construct an aggregate processor if we need one.
-        def processor = AggregateProcessor(
-          functions,
-          ordinal,
-          child.output,
-          (expressions, schema) =>
-            newMutableProjection(expressions, schema, subexpressionEliminationEnabled))
-
-        // Create the factory
-        val factory = key match {
-          // Offset Frame
-          case ("OFFSET", RowFrame, Some(offset), Some(h)) if offset == h =>
-            target: MutableRow =>
-              new OffsetWindowFunctionFrame(
-                target,
-                ordinal,
-                // OFFSET frame functions are guaranteed be OffsetWindowFunctions.
-                functions.map(_.asInstanceOf[OffsetWindowFunction]),
-                child.output,
-                (expressions, schema) =>
-                  newMutableProjection(expressions, schema, subexpressionEliminationEnabled),
-                offset)
-
-          // Growing Frame.
-          case ("AGGREGATE", frameType, None, Some(high)) =>
-            target: MutableRow => {
-              new UnboundedPrecedingWindowFunctionFrame(
-                target,
-                processor,
-                createBoundOrdering(frameType, high))
-            }
-
-          // Shrinking Frame.
-          case ("AGGREGATE", frameType, Some(low), None) =>
-            target: MutableRow => {
-              new UnboundedFollowingWindowFunctionFrame(
-                target,
-                processor,
-                createBoundOrdering(frameType, low))
-            }
-
-          // Moving Frame.
-          case ("AGGREGATE", frameType, Some(low), Some(high)) =>
-            target: MutableRow => {
-              new SlidingWindowFunctionFrame(
-                target,
-                processor,
-                createBoundOrdering(frameType, low),
-                createBoundOrdering(frameType, high))
-            }
-
-          // Entire Partition Frame.
-          case ("AGGREGATE", frameType, None, None) =>
-            target: MutableRow => {
-              new UnboundedWindowFunctionFrame(target, processor)
-            }
-        }
-
-        // Keep track of the number of expressions. This is a side-effect in a map...
-        numExpressions += expressions.size
-
-        // Create the Frame Expression - Factory pair.
-        (expressions, factory)
-    }
-  }
-
-  /**
-   * 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.
-   */
-  private[this] 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(child.output.size + i, e.dataType, e.nullable)
-    }
-    val unboundToRefMap = expressions.zip(references).toMap
-    val patchedWindowExpression = windowExpression.map(_.transform(unboundToRefMap))
-    UnsafeProjection.create(
-      child.output ++ patchedWindowExpression,
-      child.output)
-  }
-
-  protected override def doExecute(): RDD[InternalRow] = {
-    // Unwrap the expressions and factories from the map.
-    val expressions = windowFrameExpressionFactoryPairs.flatMap(_._1)
-    val factories = windowFrameExpressionFactoryPairs.map(_._2).toArray
-
-    // Start processing.
-    child.execute().mapPartitions { stream =>
-      new Iterator[InternalRow] {
-
-        // Get all relevant projections.
-        val result = createResultProjection(expressions)
-        val grouping = UnsafeProjection.create(partitionSpec, child.output)
-
-        // Manage the stream and the grouping.
-        var nextRow: UnsafeRow = null
-        var nextGroup: UnsafeRow = null
-        var nextRowAvailable: Boolean = false
-        private[this] def fetchNextRow() {
-          nextRowAvailable = stream.hasNext
-          if (nextRowAvailable) {
-            nextRow = stream.next().asInstanceOf[UnsafeRow]
-            nextGroup = grouping(nextRow)
-          } else {
-            nextRow = null
-            nextGroup = null
-          }
-        }
-        fetchNextRow()
-
-        // Manage the current partition.
-        val rows = ArrayBuffer.empty[UnsafeRow]
-        val inputFields = child.output.length
-        var sorter: UnsafeExternalSorter = null
-        var rowBuffer: RowBuffer = null
-        val windowFunctionResult = new SpecificMutableRow(expressions.map(_.dataType))
-        val frames = factories.map(_(windowFunctionResult))
-        val numFrames = frames.length
-        private[this] def fetchNextPartition() {
-          // 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
-          if (sorter != null) {
-            // the last sorter of this task will be cleaned up via task completion listener
-            sorter.cleanupResources()
-            sorter = null
-          } else {
-            rows.clear()
-          }
-
-          while (nextRowAvailable && nextGroup == currentGroup) {
-            if (sorter == null) {
-              rows += nextRow.copy()
-
-              if (rows.length >= 4096) {
-                // We will not sort the rows, so prefixComparator and recordComparator are null.
-                sorter = UnsafeExternalSorter.create(
-                  TaskContext.get().taskMemoryManager(),
-                  SparkEnv.get.blockManager,
-                  SparkEnv.get.serializerManager,
-                  TaskContext.get(),
-                  null,
-                  null,
-                  1024,
-                  SparkEnv.get.memoryManager.pageSizeBytes,
-                  SparkEnv.get.conf.getLong("spark.shuffle.spill.numElementsForceSpillThreshold",
-                    UnsafeExternalSorter.DEFAULT_NUM_ELEMENTS_FOR_SPILL_THRESHOLD),
-                  false)
-                rows.foreach { r =>
-                  sorter.insertRecord(r.getBaseObject, r.getBaseOffset, r.getSizeInBytes, 0, false)
-                }
-                rows.clear()
-              }
-            } else {
-              sorter.insertRecord(nextRow.getBaseObject, nextRow.getBaseOffset,
-                nextRow.getSizeInBytes, 0, false)
-            }
-            fetchNextRow()
-          }
-          if (sorter != null) {
-            rowBuffer = new ExternalRowBuffer(sorter, inputFields)
-          } else {
-            rowBuffer = new ArrayRowBuffer(rows)
-          }
-
-          // Setup the frames.
-          var i = 0
-          while (i < numFrames) {
-            frames(i).prepare(rowBuffer.copy())
-            i += 1
-          }
-
-          // Setup iteration
-          rowIndex = 0
-          rowsSize = rowBuffer.size()
-        }
-
-        // Iteration
-        var rowIndex = 0
-        var rowsSize = 0L
-
-        override final def hasNext: Boolean = rowIndex < rowsSize || nextRowAvailable
-
-        val join = new JoinedRow
-        override final def next(): InternalRow = {
-          // Load the next partition if we need to.
-          if (rowIndex >= rowsSize && nextRowAvailable) {
-            fetchNextPartition()
-          }
-
-          if (rowIndex < rowsSize) {
-            // Get the results for the window frames.
-            var i = 0
-            val current = rowBuffer.next()
-            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
-        }
-      }
-    }
-  }
-}
-
-/**
- * Function for comparing boundary values.
- */
-private[execution] abstract class BoundOrdering {
-  def compare(inputRow: InternalRow, inputIndex: Int, outputRow: InternalRow, outputIndex: Int): Int
-}
-
-/**
- * Compare the input index to the bound of the output index.
- */
-private[execution] final case class RowBoundOrdering(offset: Int) extends BoundOrdering {
-  override def compare(
-      inputRow: InternalRow,
-      inputIndex: Int,
-      outputRow: InternalRow,
-      outputIndex: Int): Int =
-    inputIndex - (outputIndex + offset)
-}
-
-/**
- * Compare the value of the input index to the value bound of the output index.
- */
-private[execution] final case class RangeBoundOrdering(
-    ordering: Ordering[InternalRow],
-    current: Projection,
-    bound: Projection) extends BoundOrdering {
-  override def compare(
-      inputRow: InternalRow,
-      inputIndex: Int,
-      outputRow: InternalRow,
-      outputIndex: Int): Int =
-    ordering.compare(current(inputRow), bound(outputRow))
-}
-
-/**
- * The interface of row buffer for a partition
- */
-private[execution] abstract class RowBuffer {
-
-  /** Number of rows. */
-  def size(): Int
-
-  /** Return next row in the buffer, null if no more left. */
-  def next(): InternalRow
-
-  /** Skip the next `n` rows. */
-  def skip(n: Int): Unit
-
-  /** Return a new RowBuffer that has the same rows. */
-  def copy(): RowBuffer
-}
-
-/**
- * A row buffer based on ArrayBuffer (the number of rows is limited)
- */
-private[execution] class ArrayRowBuffer(buffer: ArrayBuffer[UnsafeRow]) extends RowBuffer {
-
-  private[this] var cursor: Int = -1
-
-  /** Number of rows. */
-  def size(): Int = buffer.length
-
-  /** Return next row in the buffer, null if no more left. */
-  def next(): InternalRow = {
-    cursor += 1
-    if (cursor < buffer.length) {
-      buffer(cursor)
-    } else {
-      null
-    }
-  }
-
-  /** Skip the next `n` rows. */
-  def skip(n: Int): Unit = {
-    cursor += n
-  }
-
-  /** Return a new RowBuffer that has the same rows. */
-  def copy(): RowBuffer = {
-    new ArrayRowBuffer(buffer)
-  }
-}
-
-/**
- * An external buffer of rows based on UnsafeExternalSorter
- */
-private[execution] class ExternalRowBuffer(sorter: UnsafeExternalSorter, numFields: Int)
-  extends RowBuffer {
-
-  private[this] val iter: UnsafeSorterIterator = sorter.getIterator
-
-  private[this] val currentRow = new UnsafeRow(numFields)
-
-  /** Number of rows. */
-  def size(): Int = iter.getNumRecords()
-
-  /** Return next row in the buffer, null if no more left. */
-  def next(): InternalRow = {
-    if (iter.hasNext) {
-      iter.loadNext()
-      currentRow.pointTo(iter.getBaseObject, iter.getBaseOffset, iter.getRecordLength)
-      currentRow
-    } else {
-      null
-    }
-  }
-
-  /** Skip the next `n` rows. */
-  def skip(n: Int): Unit = {
-    var i = 0
-    while (i < n && iter.hasNext) {
-      iter.loadNext()
-      i += 1
-    }
-  }
-
-  /** Return a new RowBuffer that has the same rows. */
-  def copy(): RowBuffer = {
-    new ExternalRowBuffer(sorter, numFields)
-  }
-}
-
-/**
- * A window function calculates the results of a number of window functions for a window frame.
- * Before use a frame must be prepared by passing it all the rows in the current partition. After
- * preparation the update method can be called to fill the output rows.
- */
-private[execution] abstract class WindowFunctionFrame {
-  /**
-   * Prepare the frame for calculating the results for a partition.
-   *
-   * @param rows to calculate the frame results for.
-   */
-  def prepare(rows: RowBuffer): Unit
-
-  /**
-   * Write the current results to the target row.
-   */
-  def write(index: Int, current: InternalRow): Unit
-}
-
-/**
- * The offset window frame calculates frames containing LEAD/LAG statements.
- *
- * @param target to write results to.
- * @param ordinal the ordinal is the starting offset at which the results of the window frame get
- *                written into the (shared) target row. The result of the frame expression with
- *                index 'i' will be written to the 'ordinal' + 'i' position in the target row.
- * @param expressions to shift a number of rows.
- * @param inputSchema required for creating a projection.
- * @param newMutableProjection function used to create the projection.
- * @param offset by which rows get moved within a partition.
- */
-private[execution] final class OffsetWindowFunctionFrame(
-    target: MutableRow,
-    ordinal: Int,
-    expressions: Array[OffsetWindowFunction],
-    inputSchema: Seq[Attribute],
-    newMutableProjection: (Seq[Expression], Seq[Attribute]) => MutableProjection,
-    offset: Int) extends WindowFunctionFrame {
-
-  /** Rows of the partition currently being processed. */
-  private[this] var input: RowBuffer = null
-
-  /** Index of the input row currently used for output. */
-  private[this] var inputIndex = 0
-
-  /**
-   * Create the projection used when the offset row exists.
-   * Please note that this project always respect null input values (like PostgreSQL).
-   */
-  private[this] val projection = {
-    // Collect the expressions and bind them.
-    val inputAttrs = inputSchema.map(_.withNullability(true))
-    val boundExpressions = Seq.fill(ordinal)(NoOp) ++ expressions.toSeq.map { e =>
-      BindReferences.bindReference(e.input, inputAttrs)
-    }
-
-    // Create the projection.
-    newMutableProjection(boundExpressions, Nil).target(target)
-  }
-
-  /** Create the projection used when the offset row DOES NOT exists. */
-  private[this] val fillDefaultValue = {
-    // Collect the expressions and bind them.
-    val inputAttrs = inputSchema.map(_.withNullability(true))
-    val boundExpressions = Seq.fill(ordinal)(NoOp) ++ expressions.toSeq.map { e =>
-      if (e.default == null || e.default.foldable && e.default.eval() == null) {
-        // The default value is null.
-        Literal.create(null, e.dataType)
-      } else {
-        // The default value is an expression.
-        BindReferences.bindReference(e.default, inputAttrs)
-      }
-    }
-
-    // Create the projection.
-    newMutableProjection(boundExpressions, Nil).target(target)
-  }
-
-  override def prepare(rows: RowBuffer): Unit = {
-    input = rows
-    // drain the first few rows if offset is larger than zero
-    inputIndex = 0
-    while (inputIndex < offset) {
-      input.next()
-      inputIndex += 1
-    }
-    inputIndex = offset
-  }
-
-  override def write(index: Int, current: InternalRow): Unit = {
-    if (inputIndex >= 0 && inputIndex < input.size) {
-      val r = input.next()
-      projection(r)
-    } else {
-      // Use default values since the offset row does not exist.
-      fillDefaultValue(current)
-    }
-    inputIndex += 1
-  }
-}
-
-/**
- * The sliding window frame calculates frames with the following SQL form:
- * ... BETWEEN 1 PRECEDING AND 1 FOLLOWING
- *
- * @param target to write results to.
- * @param processor to calculate the row values with.
- * @param lbound comparator used to identify the lower bound of an output row.
- * @param ubound comparator used to identify the upper bound of an output row.
- */
-private[execution] final class SlidingWindowFunctionFrame(
-    target: MutableRow,
-    processor: AggregateProcessor,
-    lbound: BoundOrdering,
-    ubound: BoundOrdering) extends WindowFunctionFrame {
-
-  /** Rows of the partition currently being processed. */
-  private[this] var input: RowBuffer = null
-
-  /** The next row from `input`. */
-  private[this] var nextRow: InternalRow = null
-
-  /** The rows within current sliding window. */
-  private[this] val buffer = new util.ArrayDeque[InternalRow]()
-
-  /**
-   * Index of the first input row with a value greater than the upper bound of the current
-   * output row.
-   */
-  private[this] var inputHighIndex = 0
-
-  /**
-   * Index of the first input row with a value equal to or greater than the lower bound of the
-   * current output row.
-   */
-  private[this] var inputLowIndex = 0
-
-  /** Prepare the frame for calculating a new partition. Reset all variables. */
-  override def prepare(rows: RowBuffer): Unit = {
-    input = rows
-    nextRow = rows.next()
-    inputHighIndex = 0
-    inputLowIndex = 0
-    buffer.clear()
-  }
-
-  /** Write the frame columns for the current row to the given target row. */
-  override def write(index: Int, current: InternalRow): Unit = {
-    var bufferUpdated = index == 0
-
-    // Add all rows to the buffer for which the input row value is equal to or less than
-    // the output row upper bound.
-    while (nextRow != null && ubound.compare(nextRow, inputHighIndex, current, index) <= 0) {
-      buffer.add(nextRow.copy())
-      nextRow = input.next()
-      inputHighIndex += 1
-      bufferUpdated = true
-    }
-
-    // Drop all rows from the buffer for which the input row value is smaller than
-    // the output row lower bound.
-    while (!buffer.isEmpty && lbound.compare(buffer.peek(), inputLowIndex, current, index) < 0) {
-      buffer.remove()
-      inputLowIndex += 1
-      bufferUpdated = true
-    }
-
-    // Only recalculate and update when the buffer changes.
-    if (bufferUpdated) {
-      processor.initialize(input.size)
-      val iter = buffer.iterator()
-      while (iter.hasNext) {
-        processor.update(iter.next())
-      }
-      processor.evaluate(target)
-    }
-  }
-}
-
-/**
- * The unbounded window frame calculates frames with the following SQL forms:
- * ... (No Frame Definition)
- * ... BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
- *
- * Its results are  the same for each and every row in the partition. This class can be seen as a
- * special case of a sliding window, but is optimized for the unbound case.
- *
- * @param target to write results to.
- * @param processor to calculate the row values with.
- */
-private[execution] final class UnboundedWindowFunctionFrame(
-    target: MutableRow,
-    processor: AggregateProcessor) extends WindowFunctionFrame {
-
-  /** Prepare the frame for calculating a new partition. Process all rows eagerly. */
-  override def prepare(rows: RowBuffer): Unit = {
-    val size = rows.size()
-    processor.initialize(size)
-    var i = 0
-    while (i < size) {
-      processor.update(rows.next())
-      i += 1
-    }
-  }
-
-  /** Write the frame columns for the current row to the given target row. */
-  override def write(index: Int, current: InternalRow): Unit = {
-    // Unfortunately we cannot assume that evaluation is deterministic. So we need to re-evaluate
-    // for each row.
-    processor.evaluate(target)
-  }
-}
-
-/**
- * The UnboundPreceding window frame calculates frames with the following SQL form:
- * ... BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
- *
- * There is only an upper bound. Very common use cases are for instance running sums or counts
- * (row_number). Technically this is a special case of a sliding window. However a sliding window
- * has to maintain a buffer, and it must do a full evaluation everytime the buffer changes. This
- * is not the case when there is no lower bound, given the additive nature of most aggregates
- * streaming updates and partial evaluation suffice and no buffering is needed.
- *
- * @param target to write results to.
- * @param processor to calculate the row values with.
- * @param ubound comparator used to identify the upper bound of an output row.
- */
-private[execution] final class UnboundedPrecedingWindowFunctionFrame(
-    target: MutableRow,
-    processor: AggregateProcessor,
-    ubound: BoundOrdering) extends WindowFunctionFrame {
-
-  /** Rows of the partition currently being processed. */
-  private[this] var input: RowBuffer = null
-
-  /** The next row from `input`. */
-  private[this] var nextRow: InternalRow = null
-
-  /**
-   * Index of the first input row with a value greater than the upper bound of the current
-   * output row.
-   */
-  private[this] var inputIndex = 0
-
-  /** Prepare the frame for calculating a new partition. */
-  override def prepare(rows: RowBuffer): Unit = {
-    input = rows
-    nextRow = rows.next()
-    inputIndex = 0
-    processor.initialize(input.size)
-  }
-
-  /** Write the frame columns for the current row to the given target row. */
-  override def write(index: Int, current: InternalRow): Unit = {
-    var bufferUpdated = index == 0
-
-    // Add all rows to the aggregates for which the input row value is equal to or less than
-    // the output row upper bound.
-    while (nextRow != null && ubound.compare(nextRow, inputIndex, current, index) <= 0) {
-      processor.update(nextRow)
-      nextRow = input.next()
-      inputIndex += 1
-      bufferUpdated = true
-    }
-
-    // Only recalculate and update when the buffer changes.
-    if (bufferUpdated) {
-      processor.evaluate(target)
-    }
-  }
-}
-
-/**
- * The UnboundFollowing window frame calculates frames with the following SQL form:
- * ... BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING
- *
- * There is only an upper bound. This is a slightly modified version of the sliding window. The
- * sliding window operator has to check if both upper and the lower bound change when a new row
- * gets processed, where as the unbounded following only has to check the lower bound.
- *
- * This is a very expensive operator to use, O(n * (n - 1) /2), because we need to maintain a
- * buffer and must do full recalculation after each row. Reverse iteration would be possible, if
- * the commutativity of the used window functions can be guaranteed.
- *
- * @param target to write results to.
- * @param processor to calculate the row values with.
- * @param lbound comparator used to identify the lower bound of an output row.
- */
-private[execution] final class UnboundedFollowingWindowFunctionFrame(
-    target: MutableRow,
-    processor: AggregateProcessor,
-    lbound: BoundOrdering) extends WindowFunctionFrame {
-
-  /** Rows of the partition currently being processed. */
-  private[this] var input: RowBuffer = null
-
-  /**
-   * Index of the first input row with a value equal to or greater than the lower bound of the
-   * current output row.
-   */
-  private[this] var inputIndex = 0
-
-  /** Prepare the frame for calculating a new partition. */
-  override def prepare(rows: RowBuffer): Unit = {
-    input = rows
-    inputIndex = 0
-  }
-
-  /** Write the frame columns for the current row to the given target row. */
-  override def write(index: Int, current: InternalRow): Unit = {
-    var bufferUpdated = index == 0
-
-    // Duplicate the input to have a new iterator
-    val tmp = input.copy()
-
-    // Drop all rows from the buffer for which the input row value is smaller than
-    // the output row lower bound.
-    tmp.skip(inputIndex)
-    var nextRow = tmp.next()
-    while (nextRow != null && lbound.compare(nextRow, inputIndex, current, index) < 0) {
-      nextRow = tmp.next()
-      inputIndex += 1
-      bufferUpdated = true
-    }
-
-    // Only recalculate and update when the buffer changes.
-    if (bufferUpdated) {
-      processor.initialize(input.size)
-      while (nextRow != null) {
-        processor.update(nextRow)
-        nextRow = tmp.next()
-      }
-      processor.evaluate(target)
-    }
-  }
-}
-
-/**
- * This class prepares and manages the processing of a number of [[AggregateFunction]]s within a
- * single frame. The [[WindowFunctionFrame]] takes care of processing the frame in the correct way,
- * this reduces the processing of a [[AggregateWindowFunction]] to processing the underlying
- * [[AggregateFunction]]. All [[AggregateFunction]]s are processed in [[Complete]] mode.
- *
- * [[SizeBasedWindowFunction]]s are initialized in a slightly different way. These functions
- * require the size of the partition processed, this value is exposed to them when the processor is
- * constructed.
- *
- * Processing of distinct aggregates is currently not supported.
- *
- * The implementation is split into an object which takes care of construction, and a the actual
- * processor class.
- */
-private[execution] object AggregateProcessor {
-  def apply(
-      functions: Array[Expression],
-      ordinal: Int,
-      inputAttributes: Seq[Attribute],
-      newMutableProjection: (Seq[Expression], Seq[Attribute]) => MutableProjection):
-      AggregateProcessor = {
-    val aggBufferAttributes = mutable.Buffer.empty[AttributeReference]
-    val initialValues = mutable.Buffer.empty[Expression]
-    val updateExpressions = mutable.Buffer.empty[Expression]
-    val evaluateExpressions = mutable.Buffer.fill[Expression](ordinal)(NoOp)
-    val imperatives = mutable.Buffer.empty[ImperativeAggregate]
-
-    // SPARK-14244: `SizeBasedWindowFunction`s are firstly created on driver side and then
-    // serialized to executor side. These functions all reference a global singleton window
-    // partition size attribute reference, i.e., `SizeBasedWindowFunction.n`. Here we must collect
-    // the singleton instance created on driver side instead of using executor side
-    // `SizeBasedWindowFunction.n` to avoid binding failure caused by mismatching expression ID.
-    val partitionSize: Option[AttributeReference] = {
-      val aggs = functions.flatMap(_.collectFirst { case f: SizeBasedWindowFunction => f })
-      aggs.headOption.map(_.n)
-    }
-
-    // Check if there are any SizeBasedWindowFunctions. If there are, we add the partition size to
-    // the aggregation buffer. Note that the ordinal of the partition size value will always be 0.
-    partitionSize.foreach { n =>
-      aggBufferAttributes += n
-      initialValues += NoOp
-      updateExpressions += NoOp
-    }
-
-    // Add an AggregateFunction to the AggregateProcessor.
-    functions.foreach {
-      case agg: DeclarativeAggregate =>
-        aggBufferAttributes ++= agg.aggBufferAttributes
-        initialValues ++= agg.initialValues
-        updateExpressions ++= agg.updateExpressions
-        evaluateExpressions += agg.evaluateExpression
-      case agg: ImperativeAggregate =>
-        val offset = aggBufferAttributes.size
-        val imperative = BindReferences.bindReference(agg
-          .withNewInputAggBufferOffset(offset)
-          .withNewMutableAggBufferOffset(offset),
-          inputAttributes)
-        imperatives += imperative
-        aggBufferAttributes ++= imperative.aggBufferAttributes
-        val noOps = Seq.fill(imperative.aggBufferAttributes.size)(NoOp)
-        initialValues ++= noOps
-        updateExpressions ++= noOps
-        evaluateExpressions += imperative
-      case other =>
-        sys.error(s"Unsupported Aggregate Function: $other")
-    }
-
-    // Create the projections.
-    val initialProjection = newMutableProjection(
-      initialValues,
-      partitionSize.toSeq)
-    val updateProjection = newMutableProjection(
-      updateExpressions,
-      aggBufferAttributes ++ inputAttributes)
-    val evaluateProjection = newMutableProjection(
-      evaluateExpressions,
-      aggBufferAttributes)
-
-    // Create the processor
-    new AggregateProcessor(
-      aggBufferAttributes.toArray,
-      initialProjection,
-      updateProjection,
-      evaluateProjection,
-      imperatives.toArray,
-      partitionSize.isDefined)
-  }
-}
-
-/**
- * This class manages the processing of a number of aggregate functions. See the documentation of
- * the object for more information.
- */
-private[execution] final class AggregateProcessor(
-    private[this] val bufferSchema: Array[AttributeReference],
-    private[this] val initialProjection: MutableProjection,
-    private[this] val updateProjection: MutableProjection,
-    private[this] val evaluateProjection: MutableProjection,
-    private[this] val imperatives: Array[ImperativeAggregate],
-    private[this] val trackPartitionSize: Boolean) {
-
-  private[this] val join = new JoinedRow
-  private[this] val numImperatives = imperatives.length
-  private[this] val buffer = new SpecificMutableRow(bufferSchema.toSeq.map(_.dataType))
-  initialProjection.target(buffer)
-  updateProjection.target(buffer)
-
-  /** Create the initial state. */
-  def initialize(size: Int): Unit = {
-    // Some initialization expressions are dependent on the partition size so we have to
-    // initialize the size before initializing all other fields, and we have to pass the buffer to
-    // the initialization projection.
-    if (trackPartitionSize) {
-      buffer.setInt(0, size)
-    }
-    initialProjection(buffer)
-    var i = 0
-    while (i < numImperatives) {
-      imperatives(i).initialize(buffer)
-      i += 1
-    }
-  }
-
-  /** Update the buffer. */
-  def update(input: InternalRow): Unit = {
-    updateProjection(join(buffer, input))
-    var i = 0
-    while (i < numImperatives) {
-      imperatives(i).update(buffer, input)
-      i += 1
-    }
-  }
-
-  /** Evaluate buffer. */
-  def evaluate(target: MutableRow): Unit =
-    evaluateProjection.target(target)(buffer)
-}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/window/AggregateProcessor.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/window/AggregateProcessor.scala
new file mode 100644
index 0000000000000000000000000000000000000000..d3a46d020dbbf1a3b8a49a60d42bf26613ae970a
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/window/AggregateProcessor.scala
@@ -0,0 +1,159 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.window
+
+import scala.collection.mutable
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate._
+
+
+/**
+ * This class prepares and manages the processing of a number of [[AggregateFunction]]s within a
+ * single frame. The [[WindowFunctionFrame]] takes care of processing the frame in the correct way,
+ * this reduces the processing of a [[AggregateWindowFunction]] to processing the underlying
+ * [[AggregateFunction]]. All [[AggregateFunction]]s are processed in [[Complete]] mode.
+ *
+ * [[SizeBasedWindowFunction]]s are initialized in a slightly different way. These functions
+ * require the size of the partition processed, this value is exposed to them when the processor is
+ * constructed.
+ *
+ * Processing of distinct aggregates is currently not supported.
+ *
+ * The implementation is split into an object which takes care of construction, and a the actual
+ * processor class.
+ */
+private[window] object AggregateProcessor {
+  def apply(
+      functions: Array[Expression],
+      ordinal: Int,
+      inputAttributes: Seq[Attribute],
+      newMutableProjection: (Seq[Expression], Seq[Attribute]) => MutableProjection)
+    : AggregateProcessor = {
+    val aggBufferAttributes = mutable.Buffer.empty[AttributeReference]
+    val initialValues = mutable.Buffer.empty[Expression]
+    val updateExpressions = mutable.Buffer.empty[Expression]
+    val evaluateExpressions = mutable.Buffer.fill[Expression](ordinal)(NoOp)
+    val imperatives = mutable.Buffer.empty[ImperativeAggregate]
+
+    // SPARK-14244: `SizeBasedWindowFunction`s are firstly created on driver side and then
+    // serialized to executor side. These functions all reference a global singleton window
+    // partition size attribute reference, i.e., `SizeBasedWindowFunction.n`. Here we must collect
+    // the singleton instance created on driver side instead of using executor side
+    // `SizeBasedWindowFunction.n` to avoid binding failure caused by mismatching expression ID.
+    val partitionSize: Option[AttributeReference] = {
+      val aggs = functions.flatMap(_.collectFirst { case f: SizeBasedWindowFunction => f })
+      aggs.headOption.map(_.n)
+    }
+
+    // Check if there are any SizeBasedWindowFunctions. If there are, we add the partition size to
+    // the aggregation buffer. Note that the ordinal of the partition size value will always be 0.
+    partitionSize.foreach { n =>
+      aggBufferAttributes += n
+      initialValues += NoOp
+      updateExpressions += NoOp
+    }
+
+    // Add an AggregateFunction to the AggregateProcessor.
+    functions.foreach {
+      case agg: DeclarativeAggregate =>
+        aggBufferAttributes ++= agg.aggBufferAttributes
+        initialValues ++= agg.initialValues
+        updateExpressions ++= agg.updateExpressions
+        evaluateExpressions += agg.evaluateExpression
+      case agg: ImperativeAggregate =>
+        val offset = aggBufferAttributes.size
+        val imperative = BindReferences.bindReference(agg
+          .withNewInputAggBufferOffset(offset)
+          .withNewMutableAggBufferOffset(offset),
+          inputAttributes)
+        imperatives += imperative
+        aggBufferAttributes ++= imperative.aggBufferAttributes
+        val noOps = Seq.fill(imperative.aggBufferAttributes.size)(NoOp)
+        initialValues ++= noOps
+        updateExpressions ++= noOps
+        evaluateExpressions += imperative
+      case other =>
+        sys.error(s"Unsupported Aggregate Function: $other")
+    }
+
+    // Create the projections.
+    val initialProj = newMutableProjection(initialValues, partitionSize.toSeq)
+    val updateProj = newMutableProjection(updateExpressions, aggBufferAttributes ++ inputAttributes)
+    val evalProj = newMutableProjection(evaluateExpressions, aggBufferAttributes)
+
+    // Create the processor
+    new AggregateProcessor(
+      aggBufferAttributes.toArray,
+      initialProj,
+      updateProj,
+      evalProj,
+      imperatives.toArray,
+      partitionSize.isDefined)
+  }
+}
+
+/**
+ * This class manages the processing of a number of aggregate functions. See the documentation of
+ * the object for more information.
+ */
+private[window] final class AggregateProcessor(
+    private[this] val bufferSchema: Array[AttributeReference],
+    private[this] val initialProjection: MutableProjection,
+    private[this] val updateProjection: MutableProjection,
+    private[this] val evaluateProjection: MutableProjection,
+    private[this] val imperatives: Array[ImperativeAggregate],
+    private[this] val trackPartitionSize: Boolean) {
+
+  private[this] val join = new JoinedRow
+  private[this] val numImperatives = imperatives.length
+  private[this] val buffer = new SpecificMutableRow(bufferSchema.toSeq.map(_.dataType))
+  initialProjection.target(buffer)
+  updateProjection.target(buffer)
+
+  /** Create the initial state. */
+  def initialize(size: Int): Unit = {
+    // Some initialization expressions are dependent on the partition size so we have to
+    // initialize the size before initializing all other fields, and we have to pass the buffer to
+    // the initialization projection.
+    if (trackPartitionSize) {
+      buffer.setInt(0, size)
+    }
+    initialProjection(buffer)
+    var i = 0
+    while (i < numImperatives) {
+      imperatives(i).initialize(buffer)
+      i += 1
+    }
+  }
+
+  /** Update the buffer. */
+  def update(input: InternalRow): Unit = {
+    updateProjection(join(buffer, input))
+    var i = 0
+    while (i < numImperatives) {
+      imperatives(i).update(buffer, input)
+      i += 1
+    }
+  }
+
+  /** Evaluate buffer. */
+  def evaluate(target: MutableRow): Unit =
+  evaluateProjection.target(target)(buffer)
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/window/BoundOrdering.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/window/BoundOrdering.scala
new file mode 100644
index 0000000000000000000000000000000000000000..d6a801954c1ac2a5283870d3b6e908cb9093888b
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/window/BoundOrdering.scala
@@ -0,0 +1,58 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.window
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Projection
+
+
+/**
+ * Function for comparing boundary values.
+ */
+private[window] abstract class BoundOrdering {
+  def compare(inputRow: InternalRow, inputIndex: Int, outputRow: InternalRow, outputIndex: Int): Int
+}
+
+/**
+ * Compare the input index to the bound of the output index.
+ */
+private[window] final case class RowBoundOrdering(offset: Int) extends BoundOrdering {
+  override def compare(
+      inputRow: InternalRow,
+      inputIndex: Int,
+      outputRow: InternalRow,
+      outputIndex: Int): Int =
+    inputIndex - (outputIndex + offset)
+}
+
+/**
+ * Compare the value of the input index to the value bound of the output index.
+ */
+private[window] final case class RangeBoundOrdering(
+    ordering: Ordering[InternalRow],
+    current: Projection,
+    bound: Projection)
+  extends BoundOrdering {
+
+  override def compare(
+      inputRow: InternalRow,
+      inputIndex: Int,
+      outputRow: InternalRow,
+      outputIndex: Int): Int =
+    ordering.compare(current(inputRow), bound(outputRow))
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/window/RowBuffer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/window/RowBuffer.scala
new file mode 100644
index 0000000000000000000000000000000000000000..ee36c8425151979b874c9427c1c584082c9da7e2
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/window/RowBuffer.scala
@@ -0,0 +1,115 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.window
+
+import scala.collection.mutable.ArrayBuffer
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.UnsafeRow
+import org.apache.spark.util.collection.unsafe.sort.{UnsafeExternalSorter, UnsafeSorterIterator}
+
+
+/**
+ * The interface of row buffer for a partition. In absence of a buffer pool (with locking), the
+ * row buffer is used to materialize a partition of rows since we need to repeatedly scan these
+ * rows in window function processing.
+ */
+private[window] abstract class RowBuffer {
+
+  /** Number of rows. */
+  def size: Int
+
+  /** Return next row in the buffer, null if no more left. */
+  def next(): InternalRow
+
+  /** Skip the next `n` rows. */
+  def skip(n: Int): Unit
+
+  /** Return a new RowBuffer that has the same rows. */
+  def copy(): RowBuffer
+}
+
+/**
+ * A row buffer based on ArrayBuffer (the number of rows is limited).
+ */
+private[window] class ArrayRowBuffer(buffer: ArrayBuffer[UnsafeRow]) extends RowBuffer {
+
+  private[this] var cursor: Int = -1
+
+  /** Number of rows. */
+  override def size: Int = buffer.length
+
+  /** Return next row in the buffer, null if no more left. */
+  override def next(): InternalRow = {
+    cursor += 1
+    if (cursor < buffer.length) {
+      buffer(cursor)
+    } else {
+      null
+    }
+  }
+
+  /** Skip the next `n` rows. */
+  override def skip(n: Int): Unit = {
+    cursor += n
+  }
+
+  /** Return a new RowBuffer that has the same rows. */
+  override def copy(): RowBuffer = {
+    new ArrayRowBuffer(buffer)
+  }
+}
+
+/**
+ * An external buffer of rows based on UnsafeExternalSorter.
+ */
+private[window] class ExternalRowBuffer(sorter: UnsafeExternalSorter, numFields: Int)
+  extends RowBuffer {
+
+  private[this] val iter: UnsafeSorterIterator = sorter.getIterator
+
+  private[this] val currentRow = new UnsafeRow(numFields)
+
+  /** Number of rows. */
+  override def size: Int = iter.getNumRecords()
+
+  /** Return next row in the buffer, null if no more left. */
+  override def next(): InternalRow = {
+    if (iter.hasNext) {
+      iter.loadNext()
+      currentRow.pointTo(iter.getBaseObject, iter.getBaseOffset, iter.getRecordLength)
+      currentRow
+    } else {
+      null
+    }
+  }
+
+  /** Skip the next `n` rows. */
+  override def skip(n: Int): Unit = {
+    var i = 0
+    while (i < n && iter.hasNext) {
+      iter.loadNext()
+      i += 1
+    }
+  }
+
+  /** Return a new RowBuffer that has the same rows. */
+  override def copy(): RowBuffer = {
+    new ExternalRowBuffer(sorter, numFields)
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowExec.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowExec.scala
new file mode 100644
index 0000000000000000000000000000000000000000..7a6a30f120386ec6ecd1260ce773eeae92947fc3
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowExec.scala
@@ -0,0 +1,412 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.window
+
+import scala.collection.mutable
+import scala.collection.mutable.ArrayBuffer
+
+import org.apache.spark.{SparkEnv, TaskContext}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate._
+import org.apache.spark.sql.catalyst.plans.physical._
+import org.apache.spark.sql.execution.{SparkPlan, UnaryExecNode}
+import org.apache.spark.sql.types.IntegerType
+import org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter
+
+/**
+ * This class calculates and outputs (windowed) aggregates over the rows in a single (sorted)
+ * partition. The aggregates are calculated for each row in the group. Special processing
+ * instructions, frames, are used to calculate these aggregates. Frames are processed in the order
+ * specified in the window specification (the ORDER BY ... clause). There are four different frame
+ * types:
+ * - Entire partition: The frame is the entire partition, i.e.
+ *   UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING. For this case, window function will take all
+ *   rows as inputs and be evaluated once.
+ * - Growing frame: We only add new rows into the frame, i.e. UNBOUNDED PRECEDING AND ....
+ *   Every time we move to a new row to process, we add some rows to the frame. We do not remove
+ *   rows from this frame.
+ * - Shrinking frame: We only remove rows from the frame, i.e. ... AND UNBOUNDED FOLLOWING.
+ *   Every time we move to a new row to process, we remove some rows from the frame. We do not add
+ *   rows to this frame.
+ * - Moving frame: Every time we move to a new row to process, we remove some rows from the frame
+ *   and we add some rows to the frame. Examples are:
+ *     1 PRECEDING AND CURRENT ROW and 1 FOLLOWING AND 2 FOLLOWING.
+ * - Offset frame: The frame consist of one row, which is an offset number of rows away from the
+ *   current row. Only [[OffsetWindowFunction]]s can be processed in an offset frame.
+ *
+ * Different frame boundaries can be used in Growing, Shrinking and Moving frames. A frame
+ * boundary can be either Row or Range based:
+ * - Row Based: A row based boundary is based on the position of the row within the partition.
+ *   An offset indicates the number of rows above or below the current row, the frame for the
+ *   current row starts or ends. For instance, given a row based sliding frame with a lower bound
+ *   offset of -1 and a upper bound offset of +2. The frame for row with index 5 would range from
+ *   index 4 to index 6.
+ * - Range based: A range based boundary is based on the actual value of the ORDER BY
+ *   expression(s). An offset is used to alter the value of the ORDER BY expression, for
+ *   instance if the current order by expression has a value of 10 and the lower bound offset
+ *   is -3, the resulting lower bound for the current row will be 10 - 3 = 7. This however puts a
+ *   number of constraints on the ORDER BY expressions: there can be only one expression and this
+ *   expression must have a numerical data type. An exception can be made when the offset is 0,
+ *   because no value modification is needed, in this case multiple and non-numeric ORDER BY
+ *   expression are allowed.
+ *
+ * This is quite an expensive operator because every row for a single group must be in the same
+ * partition and partitions must be sorted according to the grouping and sort order. The operator
+ * requires the planner to take care of the partitioning and sorting.
+ *
+ * The operator is semi-blocking. The window functions and aggregates are calculated one group at
+ * a time, the result will only be made available after the processing for the entire group has
+ * finished. The operator is able to process different frame configurations at the same time. This
+ * is done by delegating the actual frame processing (i.e. calculation of the window functions) to
+ * specialized classes, see [[WindowFunctionFrame]], which take care of their own frame type:
+ * Entire Partition, Sliding, Growing & Shrinking. Boundary evaluation is also delegated to a pair
+ * of specialized classes: [[RowBoundOrdering]] & [[RangeBoundOrdering]].
+ */
+case class WindowExec(
+    windowExpression: Seq[NamedExpression],
+    partitionSpec: Seq[Expression],
+    orderSpec: Seq[SortOrder],
+    child: SparkPlan)
+  extends UnaryExecNode {
+
+  override def output: Seq[Attribute] =
+    child.output ++ windowExpression.map(_.toAttribute)
+
+  override def requiredChildDistribution: Seq[Distribution] = {
+    if (partitionSpec.isEmpty) {
+      // Only show warning when the number of bytes is larger than 100 MB?
+      logWarning("No Partition Defined for Window operation! Moving all data to a single "
+        + "partition, this can cause serious performance degradation.")
+      AllTuples :: Nil
+    } else ClusteredDistribution(partitionSpec) :: Nil
+  }
+
+  override def requiredChildOrdering: Seq[Seq[SortOrder]] =
+    Seq(partitionSpec.map(SortOrder(_, Ascending)) ++ orderSpec)
+
+  override def outputOrdering: Seq[SortOrder] = child.outputOrdering
+
+  /**
+   * 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 frameType to evaluate. This can either be Row or Range based.
+   * @param offset with respect to the row.
+   * @return a bound ordering object.
+   */
+  private[this] def createBoundOrdering(frameType: FrameType, offset: Int): BoundOrdering = {
+    frameType match {
+      case RangeFrame =>
+        val (exprs, current, bound) = if (offset == 0) {
+          // Use the entire order expression when the offset is 0.
+          val exprs = orderSpec.map(_.child)
+          val buildProjection = () => newMutableProjection(exprs, child.output)
+          (orderSpec, buildProjection(), buildProjection())
+        } else 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 = newMutableProjection(expr :: Nil, child.output)
+          // Flip the sign of the offset when processing the order is descending
+          val boundOffset = sortExpr.direction match {
+            case Descending => -offset
+            case Ascending => offset
+          }
+          // Create the projection which returns the current 'value' modified by adding the offset.
+          val boundExpr = Add(expr, Cast(Literal.create(boundOffset, IntegerType), expr.dataType))
+          val bound = newMutableProjection(boundExpr :: Nil, child.output)
+          (sortExpr :: Nil, current, bound)
+        } else {
+          sys.error("Non-Zero range offsets are not supported for windows " +
+            "with multiple order expressions.")
+        }
+        // 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 sortExprs = exprs.zipWithIndex.map { case (e, i) =>
+          SortOrder(BoundReference(i, e.dataType, e.nullable), e.direction)
+        }
+        val ordering = newOrdering(sortExprs, Nil)
+        RangeBoundOrdering(ordering, current, bound)
+      case RowFrame => RowBoundOrdering(offset)
+    }
+  }
+
+  /**
+   * Collection containing an entry for each window frame to process. Each entry contains a frames'
+   * WindowExpressions and factory function for the WindowFrameFunction.
+   */
+  private[this] lazy val windowFrameExpressionFactoryPairs = {
+    type FrameKey = (String, FrameType, Option[Int], Option[Int])
+    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 = (tpe, fr.frameType, FrameBoundary(fr.frameStart), FrameBoundary(fr.frameEnd))
+      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: AggregateWindowFunction => collect("AGGREGATE", frame, e, f)
+            case f: OffsetWindowFunction => collect("OFFSET", frame, e, f)
+            case f => sys.error(s"Unsupported window function: $f")
+          }
+        case _ =>
+      }
+    }
+
+    // Map the groups to a (unbound) expression and frame factory pair.
+    var numExpressions = 0
+    framedFunctions.toSeq.map {
+      case (key, (expressions, functionSeq)) =>
+        val ordinal = numExpressions
+        val functions = functionSeq.toArray
+
+        // Construct an aggregate processor if we need one.
+        def processor = AggregateProcessor(
+          functions,
+          ordinal,
+          child.output,
+          (expressions, schema) =>
+            newMutableProjection(expressions, schema, subexpressionEliminationEnabled))
+
+        // Create the factory
+        val factory = key match {
+          // Offset Frame
+          case ("OFFSET", RowFrame, Some(offset), Some(h)) if offset == h =>
+            target: MutableRow =>
+              new OffsetWindowFunctionFrame(
+                target,
+                ordinal,
+                // OFFSET frame functions are guaranteed be OffsetWindowFunctions.
+                functions.map(_.asInstanceOf[OffsetWindowFunction]),
+                child.output,
+                (expressions, schema) =>
+                  newMutableProjection(expressions, schema, subexpressionEliminationEnabled),
+                offset)
+
+          // Growing Frame.
+          case ("AGGREGATE", frameType, None, Some(high)) =>
+            target: MutableRow => {
+              new UnboundedPrecedingWindowFunctionFrame(
+                target,
+                processor,
+                createBoundOrdering(frameType, high))
+            }
+
+          // Shrinking Frame.
+          case ("AGGREGATE", frameType, Some(low), None) =>
+            target: MutableRow => {
+              new UnboundedFollowingWindowFunctionFrame(
+                target,
+                processor,
+                createBoundOrdering(frameType, low))
+            }
+
+          // Moving Frame.
+          case ("AGGREGATE", frameType, Some(low), Some(high)) =>
+            target: MutableRow => {
+              new SlidingWindowFunctionFrame(
+                target,
+                processor,
+                createBoundOrdering(frameType, low),
+                createBoundOrdering(frameType, high))
+            }
+
+          // Entire Partition Frame.
+          case ("AGGREGATE", frameType, None, None) =>
+            target: MutableRow => {
+              new UnboundedWindowFunctionFrame(target, processor)
+            }
+        }
+
+        // Keep track of the number of expressions. This is a side-effect in a map...
+        numExpressions += expressions.size
+
+        // Create the Frame Expression - Factory pair.
+        (expressions, factory)
+    }
+  }
+
+  /**
+   * 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.
+   */
+  private[this] 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(child.output.size + i, e.dataType, e.nullable)
+    }
+    val unboundToRefMap = expressions.zip(references).toMap
+    val patchedWindowExpression = windowExpression.map(_.transform(unboundToRefMap))
+    UnsafeProjection.create(
+      child.output ++ patchedWindowExpression,
+      child.output)
+  }
+
+  protected override def doExecute(): RDD[InternalRow] = {
+    // Unwrap the expressions and factories from the map.
+    val expressions = windowFrameExpressionFactoryPairs.flatMap(_._1)
+    val factories = windowFrameExpressionFactoryPairs.map(_._2).toArray
+
+    // Start processing.
+    child.execute().mapPartitions { stream =>
+      new Iterator[InternalRow] {
+
+        // Get all relevant projections.
+        val result = createResultProjection(expressions)
+        val grouping = UnsafeProjection.create(partitionSpec, child.output)
+
+        // Manage the stream and the grouping.
+        var nextRow: UnsafeRow = null
+        var nextGroup: UnsafeRow = null
+        var nextRowAvailable: Boolean = false
+        private[this] def fetchNextRow() {
+          nextRowAvailable = stream.hasNext
+          if (nextRowAvailable) {
+            nextRow = stream.next().asInstanceOf[UnsafeRow]
+            nextGroup = grouping(nextRow)
+          } else {
+            nextRow = null
+            nextGroup = null
+          }
+        }
+        fetchNextRow()
+
+        // Manage the current partition.
+        val rows = ArrayBuffer.empty[UnsafeRow]
+        val inputFields = child.output.length
+        var sorter: UnsafeExternalSorter = null
+        var rowBuffer: RowBuffer = null
+        val windowFunctionResult = new SpecificMutableRow(expressions.map(_.dataType))
+        val frames = factories.map(_(windowFunctionResult))
+        val numFrames = frames.length
+        private[this] def fetchNextPartition() {
+          // 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
+          if (sorter != null) {
+            // the last sorter of this task will be cleaned up via task completion listener
+            sorter.cleanupResources()
+            sorter = null
+          } else {
+            rows.clear()
+          }
+
+          while (nextRowAvailable && nextGroup == currentGroup) {
+            if (sorter == null) {
+              rows += nextRow.copy()
+
+              if (rows.length >= 4096) {
+                // We will not sort the rows, so prefixComparator and recordComparator are null.
+                sorter = UnsafeExternalSorter.create(
+                  TaskContext.get().taskMemoryManager(),
+                  SparkEnv.get.blockManager,
+                  SparkEnv.get.serializerManager,
+                  TaskContext.get(),
+                  null,
+                  null,
+                  1024,
+                  SparkEnv.get.memoryManager.pageSizeBytes,
+                  SparkEnv.get.conf.getLong("spark.shuffle.spill.numElementsForceSpillThreshold",
+                    UnsafeExternalSorter.DEFAULT_NUM_ELEMENTS_FOR_SPILL_THRESHOLD),
+                  false)
+                rows.foreach { r =>
+                  sorter.insertRecord(r.getBaseObject, r.getBaseOffset, r.getSizeInBytes, 0, false)
+                }
+                rows.clear()
+              }
+            } else {
+              sorter.insertRecord(nextRow.getBaseObject, nextRow.getBaseOffset,
+                nextRow.getSizeInBytes, 0, false)
+            }
+            fetchNextRow()
+          }
+          if (sorter != null) {
+            rowBuffer = new ExternalRowBuffer(sorter, inputFields)
+          } else {
+            rowBuffer = new ArrayRowBuffer(rows)
+          }
+
+          // Setup the frames.
+          var i = 0
+          while (i < numFrames) {
+            frames(i).prepare(rowBuffer.copy())
+            i += 1
+          }
+
+          // Setup iteration
+          rowIndex = 0
+          rowsSize = rowBuffer.size
+        }
+
+        // Iteration
+        var rowIndex = 0
+        var rowsSize = 0L
+
+        override final def hasNext: Boolean = rowIndex < rowsSize || nextRowAvailable
+
+        val join = new JoinedRow
+        override final def next(): InternalRow = {
+          // Load the next partition if we need to.
+          if (rowIndex >= rowsSize && nextRowAvailable) {
+            fetchNextPartition()
+          }
+
+          if (rowIndex < rowsSize) {
+            // Get the results for the window frames.
+            var i = 0
+            val current = rowBuffer.next()
+            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
+        }
+      }
+    }
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowFunctionFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowFunctionFrame.scala
new file mode 100644
index 0000000000000000000000000000000000000000..2ab9faab7a59b5008eae61dc5ef57a7849af63a0
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/window/WindowFunctionFrame.scala
@@ -0,0 +1,367 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.window
+
+import java.util
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate.NoOp
+
+
+/**
+ * A window function calculates the results of a number of window functions for a window frame.
+ * Before use a frame must be prepared by passing it all the rows in the current partition. After
+ * preparation the update method can be called to fill the output rows.
+ */
+private[window] abstract class WindowFunctionFrame {
+  /**
+   * Prepare the frame for calculating the results for a partition.
+   *
+   * @param rows to calculate the frame results for.
+   */
+  def prepare(rows: RowBuffer): Unit
+
+  /**
+   * Write the current results to the target row.
+   */
+  def write(index: Int, current: InternalRow): Unit
+}
+
+/**
+ * The offset window frame calculates frames containing LEAD/LAG statements.
+ *
+ * @param target to write results to.
+ * @param ordinal the ordinal is the starting offset at which the results of the window frame get
+ *                written into the (shared) target row. The result of the frame expression with
+ *                index 'i' will be written to the 'ordinal' + 'i' position in the target row.
+ * @param expressions to shift a number of rows.
+ * @param inputSchema required for creating a projection.
+ * @param newMutableProjection function used to create the projection.
+ * @param offset by which rows get moved within a partition.
+ */
+private[window] final class OffsetWindowFunctionFrame(
+    target: MutableRow,
+    ordinal: Int,
+    expressions: Array[OffsetWindowFunction],
+    inputSchema: Seq[Attribute],
+    newMutableProjection: (Seq[Expression], Seq[Attribute]) => MutableProjection,
+    offset: Int)
+  extends WindowFunctionFrame {
+
+  /** Rows of the partition currently being processed. */
+  private[this] var input: RowBuffer = null
+
+  /** Index of the input row currently used for output. */
+  private[this] var inputIndex = 0
+
+  /**
+   * Create the projection used when the offset row exists.
+   * Please note that this project always respect null input values (like PostgreSQL).
+   */
+  private[this] val projection = {
+    // Collect the expressions and bind them.
+    val inputAttrs = inputSchema.map(_.withNullability(true))
+    val boundExpressions = Seq.fill(ordinal)(NoOp) ++ expressions.toSeq.map { e =>
+      BindReferences.bindReference(e.input, inputAttrs)
+    }
+
+    // Create the projection.
+    newMutableProjection(boundExpressions, Nil).target(target)
+  }
+
+  /** Create the projection used when the offset row DOES NOT exists. */
+  private[this] val fillDefaultValue = {
+    // Collect the expressions and bind them.
+    val inputAttrs = inputSchema.map(_.withNullability(true))
+    val boundExpressions = Seq.fill(ordinal)(NoOp) ++ expressions.toSeq.map { e =>
+      if (e.default == null || e.default.foldable && e.default.eval() == null) {
+        // The default value is null.
+        Literal.create(null, e.dataType)
+      } else {
+        // The default value is an expression.
+        BindReferences.bindReference(e.default, inputAttrs)
+      }
+    }
+
+    // Create the projection.
+    newMutableProjection(boundExpressions, Nil).target(target)
+  }
+
+  override def prepare(rows: RowBuffer): Unit = {
+    input = rows
+    // drain the first few rows if offset is larger than zero
+    inputIndex = 0
+    while (inputIndex < offset) {
+      input.next()
+      inputIndex += 1
+    }
+    inputIndex = offset
+  }
+
+  override def write(index: Int, current: InternalRow): Unit = {
+    if (inputIndex >= 0 && inputIndex < input.size) {
+      val r = input.next()
+      projection(r)
+    } else {
+      // Use default values since the offset row does not exist.
+      fillDefaultValue(current)
+    }
+    inputIndex += 1
+  }
+}
+
+/**
+ * The sliding window frame calculates frames with the following SQL form:
+ * ... BETWEEN 1 PRECEDING AND 1 FOLLOWING
+ *
+ * @param target to write results to.
+ * @param processor to calculate the row values with.
+ * @param lbound comparator used to identify the lower bound of an output row.
+ * @param ubound comparator used to identify the upper bound of an output row.
+ */
+private[window] final class SlidingWindowFunctionFrame(
+    target: MutableRow,
+    processor: AggregateProcessor,
+    lbound: BoundOrdering,
+    ubound: BoundOrdering)
+  extends WindowFunctionFrame {
+
+  /** Rows of the partition currently being processed. */
+  private[this] var input: RowBuffer = null
+
+  /** The next row from `input`. */
+  private[this] var nextRow: InternalRow = null
+
+  /** The rows within current sliding window. */
+  private[this] val buffer = new util.ArrayDeque[InternalRow]()
+
+  /**
+   * Index of the first input row with a value greater than the upper bound of the current
+   * output row.
+   */
+  private[this] var inputHighIndex = 0
+
+  /**
+   * Index of the first input row with a value equal to or greater than the lower bound of the
+   * current output row.
+   */
+  private[this] var inputLowIndex = 0
+
+  /** Prepare the frame for calculating a new partition. Reset all variables. */
+  override def prepare(rows: RowBuffer): Unit = {
+    input = rows
+    nextRow = rows.next()
+    inputHighIndex = 0
+    inputLowIndex = 0
+    buffer.clear()
+  }
+
+  /** Write the frame columns for the current row to the given target row. */
+  override def write(index: Int, current: InternalRow): Unit = {
+    var bufferUpdated = index == 0
+
+    // Add all rows to the buffer for which the input row value is equal to or less than
+    // the output row upper bound.
+    while (nextRow != null && ubound.compare(nextRow, inputHighIndex, current, index) <= 0) {
+      buffer.add(nextRow.copy())
+      nextRow = input.next()
+      inputHighIndex += 1
+      bufferUpdated = true
+    }
+
+    // Drop all rows from the buffer for which the input row value is smaller than
+    // the output row lower bound.
+    while (!buffer.isEmpty && lbound.compare(buffer.peek(), inputLowIndex, current, index) < 0) {
+      buffer.remove()
+      inputLowIndex += 1
+      bufferUpdated = true
+    }
+
+    // Only recalculate and update when the buffer changes.
+    if (bufferUpdated) {
+      processor.initialize(input.size)
+      val iter = buffer.iterator()
+      while (iter.hasNext) {
+        processor.update(iter.next())
+      }
+      processor.evaluate(target)
+    }
+  }
+}
+
+/**
+ * The unbounded window frame calculates frames with the following SQL forms:
+ * ... (No Frame Definition)
+ * ... BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
+ *
+ * Its results are the same for each and every row in the partition. This class can be seen as a
+ * special case of a sliding window, but is optimized for the unbound case.
+ *
+ * @param target to write results to.
+ * @param processor to calculate the row values with.
+ */
+private[window] final class UnboundedWindowFunctionFrame(
+    target: MutableRow,
+    processor: AggregateProcessor)
+  extends WindowFunctionFrame {
+
+  /** Prepare the frame for calculating a new partition. Process all rows eagerly. */
+  override def prepare(rows: RowBuffer): Unit = {
+    val size = rows.size
+    processor.initialize(size)
+    var i = 0
+    while (i < size) {
+      processor.update(rows.next())
+      i += 1
+    }
+  }
+
+  /** Write the frame columns for the current row to the given target row. */
+  override def write(index: Int, current: InternalRow): Unit = {
+    // Unfortunately we cannot assume that evaluation is deterministic. So we need to re-evaluate
+    // for each row.
+    processor.evaluate(target)
+  }
+}
+
+/**
+ * The UnboundPreceding window frame calculates frames with the following SQL form:
+ * ... BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
+ *
+ * There is only an upper bound. Very common use cases are for instance running sums or counts
+ * (row_number). Technically this is a special case of a sliding window. However a sliding window
+ * has to maintain a buffer, and it must do a full evaluation everytime the buffer changes. This
+ * is not the case when there is no lower bound, given the additive nature of most aggregates
+ * streaming updates and partial evaluation suffice and no buffering is needed.
+ *
+ * @param target to write results to.
+ * @param processor to calculate the row values with.
+ * @param ubound comparator used to identify the upper bound of an output row.
+ */
+private[window] final class UnboundedPrecedingWindowFunctionFrame(
+    target: MutableRow,
+    processor: AggregateProcessor,
+    ubound: BoundOrdering)
+  extends WindowFunctionFrame {
+
+  /** Rows of the partition currently being processed. */
+  private[this] var input: RowBuffer = null
+
+  /** The next row from `input`. */
+  private[this] var nextRow: InternalRow = null
+
+  /**
+   * Index of the first input row with a value greater than the upper bound of the current
+   * output row.
+   */
+  private[this] var inputIndex = 0
+
+  /** Prepare the frame for calculating a new partition. */
+  override def prepare(rows: RowBuffer): Unit = {
+    input = rows
+    nextRow = rows.next()
+    inputIndex = 0
+    processor.initialize(input.size)
+  }
+
+  /** Write the frame columns for the current row to the given target row. */
+  override def write(index: Int, current: InternalRow): Unit = {
+    var bufferUpdated = index == 0
+
+    // Add all rows to the aggregates for which the input row value is equal to or less than
+    // the output row upper bound.
+    while (nextRow != null && ubound.compare(nextRow, inputIndex, current, index) <= 0) {
+      processor.update(nextRow)
+      nextRow = input.next()
+      inputIndex += 1
+      bufferUpdated = true
+    }
+
+    // Only recalculate and update when the buffer changes.
+    if (bufferUpdated) {
+      processor.evaluate(target)
+    }
+  }
+}
+
+/**
+ * The UnboundFollowing window frame calculates frames with the following SQL form:
+ * ... BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING
+ *
+ * There is only an upper bound. This is a slightly modified version of the sliding window. The
+ * sliding window operator has to check if both upper and the lower bound change when a new row
+ * gets processed, where as the unbounded following only has to check the lower bound.
+ *
+ * This is a very expensive operator to use, O(n * (n - 1) /2), because we need to maintain a
+ * buffer and must do full recalculation after each row. Reverse iteration would be possible, if
+ * the commutativity of the used window functions can be guaranteed.
+ *
+ * @param target to write results to.
+ * @param processor to calculate the row values with.
+ * @param lbound comparator used to identify the lower bound of an output row.
+ */
+private[window] final class UnboundedFollowingWindowFunctionFrame(
+    target: MutableRow,
+    processor: AggregateProcessor,
+    lbound: BoundOrdering)
+  extends WindowFunctionFrame {
+
+  /** Rows of the partition currently being processed. */
+  private[this] var input: RowBuffer = null
+
+  /**
+   * Index of the first input row with a value equal to or greater than the lower bound of the
+   * current output row.
+   */
+  private[this] var inputIndex = 0
+
+  /** Prepare the frame for calculating a new partition. */
+  override def prepare(rows: RowBuffer): Unit = {
+    input = rows
+    inputIndex = 0
+  }
+
+  /** Write the frame columns for the current row to the given target row. */
+  override def write(index: Int, current: InternalRow): Unit = {
+    var bufferUpdated = index == 0
+
+    // Duplicate the input to have a new iterator
+    val tmp = input.copy()
+
+    // Drop all rows from the buffer for which the input row value is smaller than
+    // the output row lower bound.
+    tmp.skip(inputIndex)
+    var nextRow = tmp.next()
+    while (nextRow != null && lbound.compare(nextRow, inputIndex, current, index) < 0) {
+      nextRow = tmp.next()
+      inputIndex += 1
+      bufferUpdated = true
+    }
+
+    // Only recalculate and update when the buffer changes.
+    if (bufferUpdated) {
+      processor.initialize(input.size)
+      while (nextRow != null) {
+        processor.update(nextRow)
+        nextRow = tmp.next()
+      }
+      processor.evaluate(target)
+    }
+  }
+}