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/*
* 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.expressions
import org.apache.spark.annotation.InterfaceStability
import org.apache.spark.sql.{AnalysisException, Column}
import org.apache.spark.sql.catalyst.expressions._
/**
* A window specification that defines the partitioning, ordering, and frame boundaries.
*
* Use the static methods in [[Window]] to create a [[WindowSpec]].
*
* @since 1.4.0
*/
@InterfaceStability.Stable
class WindowSpec private[sql](
partitionSpec: Seq[Expression],
orderSpec: Seq[SortOrder],
frame: WindowFrame) {
/**
* Defines the partitioning columns in a [[WindowSpec]].
* @since 1.4.0
*/
@scala.annotation.varargs
def partitionBy(colName: String, colNames: String*): WindowSpec = {
partitionBy((colName +: colNames).map(Column(_)): _*)
}
/**
* Defines the partitioning columns in a [[WindowSpec]].
* @since 1.4.0
*/
@scala.annotation.varargs
def partitionBy(cols: Column*): WindowSpec = {
new WindowSpec(cols.map(_.expr), orderSpec, frame)
}
/**
* Defines the ordering columns in a [[WindowSpec]].
* @since 1.4.0
*/
@scala.annotation.varargs
def orderBy(colName: String, colNames: String*): WindowSpec = {
orderBy((colName +: colNames).map(Column(_)): _*)
}
/**
* Defines the ordering columns in a [[WindowSpec]].
* @since 1.4.0
*/
@scala.annotation.varargs
def orderBy(cols: Column*): WindowSpec = {
val sortOrder: Seq[SortOrder] = cols.map { col =>
col.expr match {
case expr: SortOrder =>
expr
case expr: Expression =>
SortOrder(expr, Ascending)
}
}
new WindowSpec(partitionSpec, sortOrder, frame)
}
/**
* Defines the frame boundaries, from `start` (inclusive) to `end` (inclusive).
*
* Both `start` and `end` are relative positions from the current row. For example, "0" means
* "current row", while "-1" means the row before the current row, and "5" means the fifth row
* after the current row.
*
* We recommend users use `Window.unboundedPreceding`, `Window.unboundedFollowing`,
* and `Window.currentRow` to specify special boundary values, rather than using integral
* values directly.
*
* 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.
*
* {{{
* import org.apache.spark.sql.expressions.Window
* val df = Seq((1, "a"), (1, "a"), (2, "a"), (1, "b"), (2, "b"), (3, "b"))
* .toDF("id", "category")
* val byCategoryOrderedById =
* Window.partitionBy('category).orderBy('id).rowsBetween(Window.currentRow, 1)
* df.withColumn("sum", sum('id) over byCategoryOrderedById).show()
*
* +---+--------+---+
* | id|category|sum|
* +---+--------+---+
* | 1| b| 3|
* | 2| b| 5|
* | 3| b| 3|
* | 1| a| 2|
* | 1| a| 3|
* | 2| a| 2|
* +---+--------+---+
* }}}
*
* @param start boundary start, inclusive. The frame is unbounded if this is
* the minimum long value (`Window.unboundedPreceding`).
* @param end boundary end, inclusive. The frame is unbounded if this is the
* maximum long value (`Window.unboundedFollowing`).
* @since 1.4.0
*/
// Note: when updating the doc for this method, also update Window.rowsBetween.
def rowsBetween(start: Long, end: Long): WindowSpec = {
val boundaryStart = start match {
case 0 => CurrentRow
case Long.MinValue => UnboundedPreceding
case x if Int.MinValue <= x && x <= Int.MaxValue => Literal(x.toInt)
case x => throw new AnalysisException(s"Boundary start is not a valid integer: $x")
}
val boundaryEnd = end match {
case 0 => CurrentRow
case Long.MaxValue => UnboundedFollowing
case x if Int.MinValue <= x && x <= Int.MaxValue => Literal(x.toInt)
case x => throw new AnalysisException(s"Boundary end is not a valid integer: $x")
}
new WindowSpec(
partitionSpec,
orderSpec,
SpecifiedWindowFrame(RowFrame, boundaryStart, boundaryEnd))
}
/**
* Defines the frame boundaries, from `start` (inclusive) to `end` (inclusive).
*
* Both `start` and `end` are relative from the current row. For example, "0" means
* "current row", while "-1" means one off before the current row, and "5" means the five off
* after the current row.
*
* We recommend users use `Window.unboundedPreceding`, `Window.unboundedFollowing`,
* and `Window.currentRow` to specify special boundary values, rather than using long values
* directly.
*
* 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
* unbounded, because no value modification is needed, in this case multiple and non-numeric
* ORDER BY expression are allowed.
*
* {{{
* import org.apache.spark.sql.expressions.Window
* val df = Seq((1, "a"), (1, "a"), (2, "a"), (1, "b"), (2, "b"), (3, "b"))
* .toDF("id", "category")
* val byCategoryOrderedById =
* Window.partitionBy('category).orderBy('id).rangeBetween(Window.currentRow, 1)
* df.withColumn("sum", sum('id) over byCategoryOrderedById).show()
*
* +---+--------+---+
* | id|category|sum|
* +---+--------+---+
* | 1| b| 3|
* | 2| b| 5|
* | 3| b| 3|
* | 1| a| 4|
* | 1| a| 4|
* | 2| a| 2|
* +---+--------+---+
* }}}
*
* @param start boundary start, inclusive. The frame is unbounded if this is
* the minimum long value (`Window.unboundedPreceding`).
* @param end boundary end, inclusive. The frame is unbounded if this is the
* maximum long value (`Window.unboundedFollowing`).
* @since 1.4.0
*/
// Note: when updating the doc for this method, also update Window.rangeBetween.
def rangeBetween(start: Long, end: Long): WindowSpec = {
val boundaryStart = start match {
case 0 => CurrentRow
case Long.MinValue => UnboundedPreceding
case x => Literal(x)
}
val boundaryEnd = end match {
case 0 => CurrentRow
case Long.MaxValue => UnboundedFollowing
case x => Literal(x)
}
new WindowSpec(
partitionSpec,
orderSpec,
SpecifiedWindowFrame(RangeFrame, boundaryStart, boundaryEnd))
}
/**
* This function has been deprecated in Spark 2.4. See SPARK-25842 for more information.
* @since 2.3.0
*/
@deprecated("Use the version with Long parameter types", "2.4.0")
def rangeBetween(start: Column, end: Column): WindowSpec = {
new WindowSpec(
partitionSpec,
orderSpec,
SpecifiedWindowFrame(RangeFrame, start.expr, end.expr))
}
/**
* Converts this [[WindowSpec]] into a [[Column]] with an aggregate expression.
*/
private[sql] def withAggregate(aggregate: Column): Column = {
val spec = WindowSpecDefinition(partitionSpec, orderSpec, frame)
new Column(WindowExpression(aggregate.expr, spec))
}
}
