Value representing the current row. This can be used to specify the frame boundaries:

Window.rowsBetween(Window.unboundedPreceding, Window.currentRow)

Creates a WindowSpec with the ordering defined.

Creates a WindowSpec with the ordering defined.

Creates a WindowSpec with the partitioning defined.

Creates a WindowSpec with the partitioning defined.

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|
+---+--------+---+

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 7.

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|
+---+--------+---+

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)

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)