Window Functions#
Window functions perform calculations across rows of the query result.
They run after the HAVING
clause but before the ORDER BY
clause.
Invoking a window function requires special syntax using the OVER
clause to specify the window as follows:
function(args) OVER (
[PARTITION BY expression]
[ORDER BY expression [ASC|DESC]]
[frame]
)
A frame
is one of:
{RANGE|ROWS|GROUPS} frame_start
{RANGE|ROWS|GROUPS} BETWEEN frame_start AND frame_end
frame_start
and frame_end
can be any of:
UNBOUNDED PRECEDING
expression PRECEDING
CURRENT ROW
expression FOLLOWING
UNBOUNDED FOLLOWING
The window definition has 3 components:
The
PARTITION BY
clause separates the input rows into different partitions. This is analogous to how theGROUP BY
clause separates rows into different groups for aggregate functions. IfPARTITION BY
is not specified, the entire input is treated as a single partition.The
ORDER BY
clause determines the order in which input rows will be processed by the window function. IfORDER BY
is not specified, the ordering is undefined. Note that the ORDER BY clause within window functions does not support ordinals. You need to use actual expressionsThe
frame
clause specifies the sliding window of rows to be processed by the function for a given input row. A frame can beROWS
type,RANGE
type orGROUPS
type, and it runs fromframe_start
toframe_end
. Ifframe_end
is not specified, a default value ofCURRENT ROW
is used.In
ROWS
mode,CURRENT ROW
refers specifically to the current row. InRANGE
andGROUPS
mode,CURRENT ROW
refers to any peer row of the current row for the purpose of theORDER BY
. If noORDER BY
is specified, all rows are considered peers of the current row. InRANGE
andGROUPS
mode a frame start ofCURRENT ROW
refers to the first peer row of the current row, while a frame end ofCURRENT ROW
refers to the last peer row of the current row.In
ROWS
mode, frame starts and ends ofexpression PRECEDING
orexpression FOLLOWING
define the start or end of the frame as the specified number of rows before or after the current row. Theexpression
must be of typeINTEGER
.In
RANGE
mode, frame starts and ends ofexpression PRECEDING
orexpression FOLLOWING
define the start or end of the frame as the value difference of the sort key from the current row. The sort key must either be the same type ofexpression
or can be coerced to the same type asexpression
.In
GROUPS
mode, frame starts and ends ofexpression PRECEDING
orexpression FOLLOWING
define the start or end of the frame as the number of groups from the current row. A group includes all rows with the same value on the sort key. The type ofexpression
must be INTEGER or BIGINT.If no frame is specified, a default frame of
RANGE UNBOUNDED PRECEDING
is used.
Examples#
The following query ranks orders for each clerk by price:
SELECT orderkey, clerk, totalprice,
rank() OVER (PARTITION BY clerk
ORDER BY totalprice DESC) AS rnk
FROM orders
ORDER BY clerk, rnk
The following queries demonstrate the difference between ROWS
, RANGE
and GROUPS
in frame definition:
SELECT
ARRAY_AGG(v) OVER (
ORDER BY k ASC ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING
)
FROM (
VALUES (1, 'a'), (1, 'b'), (3, 'c'), (3, 'd'), (5, 'e')
) t(k, v); -- ['a', 'b'], ['a', 'b', 'c'], ['b', 'c', 'd'], ['c', 'd', 'e'], ['d', 'e']
SELECT
ARRAY_AGG(v) OVER (
ORDER BY k ASC RANGE BETWEEN 1 PRECEDING AND 1 FOLLOWING
)
FROM (
VALUES (1, 'a'), (1, 'b'), (3, 'c'), (3, 'd'), (5, 'e')
) t(k, v); -- ['a', 'b'], ['a', 'b'], ['c', 'd'], ['c', 'd'], ['e']
SELECT
ARRAY_AGG(v) OVER (
ORDER BY k ASC GROUPS BETWEEN 1 PRECEDING AND 1 FOLLOWING
)
FROM (
VALUES (1, 'a'), (1, 'b'), (3, 'c'), (3, 'd'), (5, 'e')
) t(k, v); -- ['a', 'b', 'c', 'd'], ['a', 'b', 'c', 'd'], ['a', 'b', 'c', 'd', 'e'], ['a', 'b', 'c', 'd', 'e'], ['c', 'd', 'e']
Aggregate Functions#
All Aggregate Functions can be used as window functions by adding the OVER
clause. The aggregate function is computed for each row over the rows within
the current row’s window frame.
For example, the following query produces a rolling sum of order prices by day for each clerk:
SELECT clerk, orderdate, orderkey, totalprice,
sum(totalprice) OVER (PARTITION BY clerk
ORDER BY orderdate) AS rolling_sum
FROM orders
ORDER BY clerk, orderdate, orderkey
Ranking Functions#
- cume_dist() bigint #
Returns the cumulative distribution of a value in a group of values. The result is the number of rows preceding or peer with the row in the window ordering of the window partition divided by the total number of rows in the window partition. Thus, any tie values in the ordering will evaluate to the same distribution value.
- dense_rank() bigint #
Returns the rank of a value in a group of values. This is similar to
rank()
, except that tie values do not produce gaps in the sequence.
- ntile(n) bigint #
Divides the rows for each window partition into
n
buckets ranging from1
to at mostn
. Bucket values will differ by at most1
. If the number of rows in the partition does not divide evenly into the number of buckets, then the remainder values are distributed one per bucket, starting with the first bucket.For example, with
6
rows and4
buckets, the bucket values would be as follows:1
1
2
2
3
4
- percent_rank() double #
Returns the percentage ranking of a value in group of values. The result is
(r - 1) / (n - 1)
wherer
is therank()
of the row andn
is the total number of rows in the window partition.
- rank() bigint #
Returns the rank of a value in a group of values. The rank is one plus the number of rows preceding the row that are not peer with the row. Thus, tie values in the ordering will produce gaps in the sequence. The ranking is performed for each window partition.
- row_number() bigint #
Returns a unique, sequential number for each row, starting with one, according to the ordering of rows within the window partition.
Value Functions#
Value functions provide an option to specify how null values should be treated when evaluating the
function. Nulls can either be ignored (IGNORE NULLS
) or respected (RESPECT NULLS
). By default,
null values are respected. If IGNORE NULLS
is specified, all rows where the value expression is
null are excluded from the calculation. If IGNORE NULLS
is specified and the value expression is
null for all rows, the default_value
is returned, or if it is not specified, null
is returned.
- first_value(x) [same as input] #
Returns the first value of the window.
- last_value(x) [same as input] #
Returns the last value of the window.
- nth_value(x, offset) [same as input] #
Returns the value at the specified offset from beginning the window. Offsets start at
1
. The offset can be any scalar expression. If the offset is null or greater than the number of values in the window, null is returned. It is an error for the offset to be zero or negative.
- lead(x[, offset[, default_value]]) [same as input] #
Returns the value at
offset
rows after the current row in the window. Offsets start at0
, which is the current row. The offset can be any scalar expression. The defaultoffset
is1
. If the offset is null or larger than the window, thedefault_value
is returned, or if it is not specifiednull
is returned.
- lag(x[, offset[, default_value]]) [same as input] #
Returns the value at
offset
rows before the current row in the window Offsets start at0
, which is the current row. The offset can be any scalar expression. The defaultoffset
is1
. If the offset is null or larger than the window, thedefault_value
is returned, or if it is not specifiednull
is returned.