Value representing the current row.
Value representing the current row. This can be used to specify the frame boundaries:
Window.rowsBetween(Window.unboundedPreceding, Window.currentRow)
2.1.0
Creates a WindowSpec with the ordering defined.
Creates a WindowSpec with the ordering defined.
1.4.0
Creates a WindowSpec with the ordering defined.
Creates a WindowSpec with the ordering defined.
1.4.0
Creates a WindowSpec with the partitioning defined.
Creates a WindowSpec with the partitioning defined.
1.4.0
Creates a WindowSpec with the partitioning defined.
Creates a WindowSpec with the partitioning defined.
1.4.0
Creates a WindowSpec with the frame boundaries defined,
from start
(inclusive) to end
(inclusive).
Creates a WindowSpec with the frame boundaries defined,
from start
(inclusive) to end
(inclusive).
Both start
and end
are relative to the current row. For example, "lit(0)" means
"current row", while "lit(-1)" means one off before the current row, and "lit(5)" means the
five off after the current row.
Users should use unboundedPreceding()
, unboundedFollowing()
, and currentRow()
from
org.apache.spark.sql.functions to specify special boundary values, literals are not
transformed to org.apache.spark.sql.catalyst.expressions.SpecialFrameBoundarys.
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/date/timestamp data type. An exception can be made when the offset is unbounded, because no value modification is needed, in this case multiple and non-numerical/date/timestamp data type 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(currentRow(), lit(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| +---+--------+---+
boundary start, inclusive. The frame is unbounded if the expression is org.apache.spark.sql.catalyst.expressions.UnboundedPreceding.
boundary end, inclusive. The frame is unbounded if the expression is org.apache.spark.sql.catalyst.expressions.UnboundedFollowing.
2.3.0
Creates a WindowSpec with the frame boundaries defined,
from start
(inclusive) to end
(inclusive).
Creates a WindowSpec with the frame boundaries defined,
from start
(inclusive) to end
(inclusive).
Both start
and end
are relative to 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| +---+--------+---+
boundary start, inclusive. The frame is unbounded if this is
the minimum long value (Window.unboundedPreceding
).
boundary end, inclusive. The frame is unbounded if this is the
maximum long value (Window.unboundedFollowing
).
2.1.0
Creates a WindowSpec with the frame boundaries defined,
from start
(inclusive) to end
(inclusive).
Creates a WindowSpec with the frame boundaries defined,
from start
(inclusive) to end
(inclusive).
Both start
and end
are positions relative to 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| +---+--------+---+
boundary start, inclusive. The frame is unbounded if this is
the minimum long value (Window.unboundedPreceding
).
boundary end, inclusive. The frame is unbounded if this is the
maximum long value (Window.unboundedFollowing
).
2.1.0
Value representing the last row in the partition, equivalent to "UNBOUNDED FOLLOWING" in SQL.
Value representing the last row in the partition, equivalent to "UNBOUNDED FOLLOWING" in SQL. This can be used to specify the frame boundaries:
Window.rowsBetween(Window.unboundedPreceding, Window.unboundedFollowing)
2.1.0
Value representing the first row in the partition, equivalent to "UNBOUNDED PRECEDING" in SQL.
Value representing the first row in the partition, equivalent to "UNBOUNDED PRECEDING" in SQL. This can be used to specify the frame boundaries:
Window.rowsBetween(Window.unboundedPreceding, Window.currentRow)
2.1.0
Utility functions for defining window in DataFrames.
1.4.0