# Window Functions¶

Window functions are functions applied over a subset (known as a window) of the rows returned by a SELECT query.

## Syntax¶

window_expr ::=
window_fn ( [ value_expr [, ...] ] )
OVER (
[ PARTITION BY value_expression [, ...] ]
[ ORDER BY value_expression [ ASC | DESC ] [ NULLS { FIRST | LAST } ] [, ...] ]
[ frame_clause ]
)

window_fn ::=
| AVG
| COUNT
| LAG
| MAX
| MIN
| RANK
| ROW_NUMBER
| SUM
| FIRST_VALUE
| LAST_VALUE
| NTH_VALUE
| DENSE_RANK
| PERCENT_RANK
| CUME_DIST
| NTILE

frame_clause ::=
{ RANGE | ROWS } frame_start [ frame_exclusion ]
| { RANGE | ROWS } BETWEEN frame_def AND frame_def [ frame_exclusion ]

frame_def ::=
UNBOUNDED PRECEDING
offset PRECEDING
CURRENT ROW
offset FOLLOWING
UNBOUNDED FOLLOWING

offset ::=
EXCLUDE CURRENT ROW
| EXCLUDE GROUP
| EXCLUDE TIES
| EXCLUDE NO OTHERS

row_offset ::= numeric literal


## Arguments¶

Parameter Description
window_fn Window function, like AVG, MIN, etc.
PARTITION BY partition_expr An expression or column reference to partition by
ORDER BY order An expression or column reference to order by

## Supported Window Functions¶

Window Aggregation Functions
Function Description
AVG Returns the average of numeric values.
COUNT Returns the count of numeric values, or only the distinct values.
MAX Returns the maximum values.
MIN Returns the minimum values.
SUM Returns the sum of numeric values, or only the distinct values.
Ranking Functions
Function Description
LAG Returns a value from a previous row within the partition of a result set.
LEAD Returns a value from a subsequent row within the partition of a result set.
ROW_NUMBER Returns the row number of each row within the partition of a result set.
RANK Returns the rank of each row within the partition of a result set.
FIRST_VALUE Returns the value in the first row of a window.
LAST_VALUE Returns the value in the last row of a window.
NTH_VALUE Returns the value in a specified (n) row of a window.
DENSE_RANK Returns the rank of the current row with no gaps.
PERCENT_RANK Returns the relative rank of the current row.
CUME_DIST Returns the cumulative distribution of rows.
NTILE Returns an integer ranging between 1 and the argument value, dividing the partitions as equally as possible.

## How Window Functions Work¶

A window function operates on a subset (“window”) of rows.

Each time a window function is called, it gets the current row for processing, as well as the window of rows that contains the current row.

The window function returns one result row for each input.

The result depends on the individual row and the order of the rows. Some window functions are order-sensitive, such as RANK.

Note

In general, a window frame will include all rows of a partition.

If an ORDER BY clause is applied, the rows will become ordered which can change the order of the function calls. The function will be applied to the subset between the first row and the current row, instead of the whole frame.

Boundaries for the frames may need to be applied to get the correct results.

Window frame functions allows a user to perform rolling operations, such as calculate moving averages, longest standing customers, identifying churn, find movers and shakers, etc.

### PARTITION BY¶

The PARTITION BY clause groups the rows of the query into partitions, which are processed separately by the window function.

PARTITION BY works similarly to a query-level GROUP BY clause, but expressions are always just expressions and cannot be output-column names or numbers.

Without PARTITION BY, all rows produced by the query are treated as a single partition.

### ORDER BY¶

The ORDER BY clause determines the order in which the rows of a partition are processed by the window function. It works similarly to a query-level ORDER BY clause, but cannot use output-column names or numbers.

Without ORDER BY, rows are processed in an unspecified order.

### Frames¶

Note

Frames and frame exclusions have been tested extensively, but are a complex feature. They are released as a preview in v2020.1 pending longer-term testing.

The frame_clause specifies the set of rows constituting the window frame, which is a subset of the current partition, for those window functions that act on the frame instead of the whole partition.

The set of rows in the frame can vary depending on which row is the current row. The frame can be specified in RANGE or ROWS mode; in each case, it runs from the frame_start to the frame_end. If frame_end is omitted, the end defaults to CURRENT ROW.

A frame_start of UNBOUNDED PRECEDING means that the frame starts with the first row of the partition, and similarly a frame_end of UNBOUNDED FOLLOWING means that the frame ends with the last row of the partition.

In RANGE mode, a frame_start of CURRENT ROW means the frame starts with the current row’s first peer row (a row that the window’s ORDER BY clause sorts as equivalent to the current row), while a frame_end of CURRENT ROW means the frame ends with the current row’s last peer row. In ROWS mode, CURRENT ROW simply means the current row.

In the offset PRECEDING and offset FOLLOWING frame options, the offset must be an expression not containing any variables, aggregate functions, or window functions. The meaning of the offset depends on the frame mode:

• In ROWS mode, the offset must yield a non-null, non-negative integer, and the option means that the frame starts or ends the specified number of rows before or after the current row.
• In RANGE mode, these options require that the ORDER BY clause specify exactly one column. The offset specifies the maximum difference between the value of that column in the current row and its value in preceding or following rows of the frame. This option is restricted to integer types, date and datetime. The offset is required to be a non-null non-negative integer value.
• With a DATE or DATETIME column, the offset indicates a number of days.

In any case, the distance to the end of the frame is limited by the distance to the end of the partition, so that for rows near the partition ends the frame might contain fewer rows than elsewhere.

The default framing option is RANGE UNBOUNDED PRECEDING, which is the same as RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW. With ORDER BY, this sets the frame to be all rows from the partition start up through the current row’s last ORDER BY peer. Without ORDER BY, this means all rows of the partition are included in the window frame, since all rows become peers of the current row.

#### Restrictions¶

• frame_start cannot be UNBOUNDED FOLLOWING
• frame_end cannot be UNBOUNDED PRECEDING
• frame_end choice cannot appear earlier in the above list of frame_start and frame_end options than the frame_start choice does.

For example RANGE BETWEEN CURRENT ROW AND 7 PRECEDING is not allowed. However, while ROWS BETWEEN 7 PRECEDING AND 8 PRECEDING is allowed, it would never select any rows.

### Frame Exclusion¶

The frame_exclusion option allows rows around the current row to be excluded from the frame, even if they would be included according to the frame start and frame end options. EXCLUDE CURRENT ROW excludes the current row from the frame. EXCLUDE GROUP excludes the current row and its ordering peers from the frame. EXCLUDE TIES excludes any peers of the current row from the frame, but not the current row itself. EXCLUDE NO OTHERS simply specifies explicitly the default behavior of not excluding the current row or its peers.

## Limitations¶

• At this phase, text columns are not supported in window function expressions.
• Window function calls are permitted only in the SELECT list.

## Examples¶

For these examples, assume a table named nba, with the following structure:

CREATE TABLE nba
(
"Name" varchar(40),
"Team" varchar(40),
"Number" tinyint,
"Position" varchar(2),
"Age" tinyint,
"Height" varchar(4),
"Weight" real,
"College" varchar(40),
"Salary" float
);


Here’s a peek at the table contents (Download nba.csv):

nba.csv
Name Team Number Position Age Height Weight College Salary
Avery Bradley Boston Celtics 0.0 PG 25.0 6-2 180.0 Texas 7730337.0
Jae Crowder Boston Celtics 99.0 SF 25.0 6-6 235.0 Marquette 6796117.0
John Holland Boston Celtics 30.0 SG 27.0 6-5 205.0 Boston University
R.J. Hunter Boston Celtics 28.0 SG 22.0 6-5 185.0 Georgia State 1148640.0
Jonas Jerebko Boston Celtics 8.0 PF 29.0 6-10 231.0   5000000.0
Amir Johnson Boston Celtics 90.0 PF 29.0 6-9 240.0   12000000.0
Jordan Mickey Boston Celtics 55.0 PF 21.0 6-8 235.0 LSU 1170960.0
Kelly Olynyk Boston Celtics 41.0 C 25.0 7-0 238.0 Gonzaga 2165160.0
Terry Rozier Boston Celtics 12.0 PG 22.0 6-2 190.0 Louisville 1824360.0

### Window Function Application¶

t=> SELECT SUM("Salary") OVER (PARTITION BY "Team" ORDER BY "Age") FROM nba;
sum
---------
1763400
5540289
5540289
5540289
5540289
7540289
18873622
18873622
30873622
60301531
60301531
60301531
64301531
72902950
72902950
[...]


### Ranking Results¶

See RANK.

t=> SELECT n.Name, n.Age, n.Height ,RANK() OVER
.       (PARTITION BY n.Age ORDER BY n.Height DESC) AS Rank
.        FROM nba_2 n;
name                     | age | height | rank
-------------------------+-----+--------+-----
Devin Booker             |  19 | 6-6    |    1
Rashad Vaughn            |  19 | 6-6    |    1
Kristaps Porzingis       |  20 | 7-3    |    1
Karl-Anthony Towns       |  20 | 7-0    |    2
Bruno Caboclo            |  20 | 6-9    |    3
Kevon Looney             |  20 | 6-9    |    3
Aaron Gordon             |  20 | 6-9    |    3
Noah Vonleh              |  20 | 6-9    |    3
Cliff Alexander          |  20 | 6-8    |    7
Stanley Johnson          |  20 | 6-7    |    8
Justise Winslow          |  20 | 6-7    |    8
Kelly Oubre Jr.          |  20 | 6-7    |    8
James Young              |  20 | 6-6    |   11
Dante Exum               |  20 | 6-6    |   11
D'Angelo Russell         |  20 | 6-5    |   13
Emmanuel Mudiay          |  20 | 6-5    |   13
Tyus Jones               |  20 | 6-2    |   15
Jahlil Okafor            |  20 | 6-11   |   16
Christian Wood           |  20 | 6-11   |   16
Myles Turner             |  20 | 6-11   |   16
Trey Lyles               |  20 | 6-10   |   19
[...]


### Using LEAD to Access Following Rows Without a Join¶

The LEAD function is used to return data from rows further down the result set. The LAG function returns data from rows further up the result set.

This example calculates the salary between two players, starting from the highest salary.

t=> SELECT "Name",
.          "Salary",
.          LEAD("Salary", 1) OVER (ORDER BY "Salary" DESC) AS "Salary - next",
.          ABS(LEAD("Salary", 1) OVER (ORDER BY "Salary" DESC) - "Salary") AS "Salary - diff"
.          FROM nba
.          LIMIT 11 ;
Name            | Salary   | Salary - next | Salary - diff
----------------+----------+---------------+--------------
Kobe Bryant     | 25000000 |      22970500 |       2029500
LeBron James    | 22970500 |      22875000 |         95500
Carmelo Anthony | 22875000 |      22359364 |        515636
Dwight Howard   | 22359364 |      22192730 |        166634
Chris Bosh      | 22192730 |      21468695 |        724035
Chris Paul      | 21468695 |      20158622 |       1310073
Kevin Durant    | 20158622 |      20093064 |         65558
Derrick Rose    | 20093064 |      20000000 |         93064
Dwyane Wade     | 20000000 |      19689000 |        311000
Brook Lopez     | 19689000 |      19689000 |             0
DeAndre Jordan  | 19689000 |      19689000 |             0