# Window Functions¶

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

Each time a window function is called, it is passed a row (the current row in the window) and the window of rows that contain the current row. The window function returns one output row for each input row. The output depends on the individual row passed to the function and the values of the other rows in the window passed to the function.

Some window functions are order-sensitive. There are two main types of order-sensitive window functions:

• Rank-related functions.

• Window frame functions.

Rank-related functions list information based on the “rank” of a row. For example, if you rank stores in descending order by profit per year, the store with the most profit will be ranked 1; the second-most profitable store will be ranked 2, etc.

Window frame functions allow you to perform rolling operations, such as calculating a running total or a moving average, on a subset of the rows in the window.

Users who are not familiar with window functions, rank-related functions, or window frame functions might want to read the conceptual material in Using Window Functions.

In this Topic:

## Overview¶

### What is a Window?¶

A window is a group of related rows. For example, a window might be defined based on timestamps, with all rows in the same month grouped in the same window. Or a window might be defined based on location, with all rows from a particular city grouped in the same window.

A window can consist of zero, one, or multiple rows. For simplicity, Snowflake documentation usually says that a window contains multiple rows.

### What is a Window Function?¶

A window function is any function that operates over a window of rows.

A window function is generally passed two parameters:

• A row. More precisely, a window function is passed 0 or more expressions. In almost all cases, at least one of those expressions references a column in that row. (Most window functions require at least one column or expression, but a few window functions, such as some rank-related functions, do not required an explicit column or expression.)

• A window of related rows that includes that row. The window can be the entire table, or a subset of the rows in the table.

For non-window functions, all arguments are usually passed explicitly to the function, for example:

MY_FUNCTION(argument1, argument2, …)

Window functions behave differently; although the current row is passed as an argument the normal way, the window is passed through a separate clause, called an OVER clause. The syntax of the OVER clause is documented later.

#### List of Functions that Support Windows¶

The list below shows all the window functions.

Note that some functions listed as window frame functions do not support all possible types of window frames.

Function Name

Window

Window Frame

Rank-related

Notes

General Window

Does not use standard window syntax.

Uses different syntax than the other window functions.

Uses different syntax than the other window functions.

Alias for STDDEV_SAMP.

Alias for VAR_POP.

Alias for VAR_SAMP.

Rank-related

Supports range-based cumulative window frames, but not other types of window frames.

Not an actual window function (i.e. does not utilize a window in its syntax), but performs window-like operations on a range of rows.

Bitwise Aggregation

Boolean Aggregation

Hash

Semi-structured Data Aggregation

Linear Regression

Statistics and Probability

Cardinality Estimation . (using HyperLogLog)

Alias for HLL.

Not an aggregate function; uses scalar input from HLL_ACCUMULATE or HLL_COMBINE.

Similarity Estimation . (using MinHash)

Alias for APPROXIMATE_SIMILARITY.

Frequency Estimation . (using Space-Saving)

Not an aggregate function; uses scalar input from APPROX_TOP_K_ACCUMULATE or APPROX_TOP_K_COMBINE.

Percentile Estimation . (using t-Digest)

Not an aggregate function; uses scalar input from APPROX_PERCENTILE_ACCUMULATE or APPROX_PERCENTILE_COMBINE.

### Window Functions and NULL Values¶

Some functions ignore NULL values. For example, AVG calculates the average of values 1, 5, and NULL to be 3, based on the following formula:

(1 + 5) / 2 = 3

In both the numerator and the denominator, only the non-NULL values are used.

If all of the values passed to the function are NULL, then the function returns NULL.

Some window functions can be passed more than one column. For example:

SELECT COUNT(col1, col2) FROM table1;


In these instances, the function ignores a row if any individual column is NULL.

For example, in the following query, COUNT returns 1, not 4, because three of the four rows contain at least one NULL value in the selected columns:

Create a table and populate it with values:

CREATE TABLE t (x INT, y INT);
INSERT INTO t (x, y) VALUES
(1, 2),         -- No NULLs.
(3, NULL),      -- One but not all columns are NULL.
(NULL, 6),      -- One but not all columns are NULL.
(NULL, NULL);   -- All columns are NULL.


Query the table:

SELECT COUNT(x, y) FROM t;
+-------------+
| COUNT(X, Y) |
|-------------|
|           1 |
+-------------+


Similarly, if SUM is called with an expression that references two or more columns, and if one or more of those columns is NULL, then the expression evaluates to NULL, and the row is ignored:

SELECT SUM(x + y) FROM t;
+------------+
| SUM(X + Y) |
|------------|
|          3 |
+------------+


Note that this behavior differs from the behavior of GROUP BY, which does not discard rows when some columns are NULL:

SELECT x AS X_COL, y AS Y_COL FROM t GROUP BY x, y;
+-------+-------+
| X_COL | Y_COL |
|-------+-------|
|     1 |     2 |
|     3 |  NULL |
|  NULL |     6 |
|  NULL |  NULL |
+-------+-------+


### Introductory Example¶

Suppose that you own a chain of stores. The following query shows the percentage of the total chain’s profit generated by each store. The query uses the RATIO_TO_REPORT function, which takes a value (for example net_profit) from the current row and divides it by the sum of the corresponding values (net_profit) from all the other rows:

CREATE TRANSIENT TABLE store_sales (
branch_ID    INTEGER,
city        VARCHAR,
gross_sales NUMERIC(9, 2),
gross_costs NUMERIC(9, 2),
net_profit  NUMERIC(9, 2)
);

INSERT INTO store_sales (branch_ID, city, gross_sales, gross_costs)
VALUES
(1, 'Vancouver', 110000, 100000),
(2, 'Vancouver', 140000, 125000),
(3, 'Montreal', 150000, 140000),
(4, 'Montreal', 155000, 146000);

UPDATE store_sales SET net_profit = gross_sales - gross_costs;


Query the table:

SELECT branch_ID,
net_profit,
100 * RATIO_TO_REPORT(net_profit) OVER () AS percent_of_chain_profit
FROM store_sales AS s1
ORDER BY branch_ID;
+-----------+------------+-------------------------+
| BRANCH_ID | NET_PROFIT | PERCENT_OF_CHAIN_PROFIT |
|-----------+------------+-------------------------|
|         1 |   10000.00 |             22.72727300 |
|         2 |   15000.00 |             34.09090900 |
|         3 |   10000.00 |             22.72727300 |
|         4 |    9000.00 |             20.45454500 |
+-----------+------------+-------------------------+


### What is a Window Frame?¶

A window frame is a sub-group of the rows in a window. Creating subsets allows you to compute values over just that specified sub-group of rows. Window frames are specified as an additional subclause in the ORDER BY subclause of the OVER clause.

#### Types of Window Frames¶

Snowflake supports two types of window frames:

Cumulative

Enables computing rolling values from the beginning of the window to the current row or from the current row to the end of the window.

Sliding

Enables computing rolling values between any two rows (inclusive) in the window, relative to the current row.

For more information about window frames, including the syntax used for window frames, see Window Frame Syntax and Usage.

## Window Syntax and Usage¶

### Window Syntax¶

<function> ( [ <arguments> ] ) OVER ( [ PARTITION BY <expr1> ] [ ORDER BY <expr2> ] )


The OVER clause specifies that the function is being used as a window function.

The PARTITION BY sub-clause allows rows to be grouped into sub-groups, for example by city, by year, etc. The PARTITION BY clause is optional. You can analyze an entire group of rows without breaking it into sub-groups.

The ORDER BY clause orders rows within the window. (This is different from ordering the output of a query. A query might have one ORDER BY clause that controls the order of rows within a window, and a separate ORDER BY clause, outside the OVER clause, that controls the output order of the entire query.) Although the ORDER BY clause is optional for some window functions, it is required for others. For example, window frame functions and rank-related functions require that the data be in a meaningful order, and therefore require an ORDER BY sub-clause.

Note

The functions that support window frames utilize a modified/enhanced syntax. For more details, see Window Frame Syntax and Usage (in this topic).

### Window Usage Notes¶

• The OVER clause specifies the window over which the function operates. The clause consists of one (or both) of the following components:

• PARTITION BY expr1: Subclause that defines the partition, if any, for the window (i.e. how the data will be grouped before applying the function).

• ORDER BY expr2: Subclause that determines the ordering of the rows in the window.

The ORDER BY sub-clause follows rules similar to those of the query ORDER BY clause, for example with respect to ASC/DESC (ascending/descending) and NULL handling. For more details about additional supported options see the ORDER BY query construct.

Different functions handle the ORDER BY clause different ways:

• Some window functions require an ORDER BY clause.

• Some window functions prohibit an ORDER BY clause.

• Some window functions use an ORDER BY clause if one is present, but do not require it.

• Some window functions treat an ORDER BY clause as an implicit cumulative window frame clause. (More information about implied window frames is at Window Frame Usage Notes.)

Note

For clarity, Snowflake recommends avoiding implicit window frames. If your query uses a window frame, make it an explicit window frame.

## Window Frame Syntax and Usage¶

### Window Frame Syntax¶

<function> ( <arguments> ) OVER ( [ PARTITION BY <expr1> ] ORDER BY <expr2> [ cumulativeFrame | slidingFrame ] )


Where:

cumulativeFrame ::=
{
{ ROWS | RANGE } BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
| { ROWS | RANGE } BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING
}

slidingFrame ::=
{
ROWS BETWEEN <N> { PRECEDING | FOLLOWING } AND <N> { PRECEDING | FOLLOWING }
| ROWS BETWEEN UNBOUNDED PRECEDING AND <N> { PRECEDING | FOLLOWING }
| ROWS BETWEEN <N> { PRECEDING | FOLLOWING } AND UNBOUNDED FOLLOWING
}


Note

Window frames require that the data in the window be in a known order. Therefore, the ORDER BY inside the OVER clause is required for window frame syntax, even though that ORDER BY clause is optional in general window function syntax.

### Window Frame Usage Notes¶

• For cumulative window frames:

• ROWS computes the result for the current row using all rows from the beginning or end of the partition to the current row (according to the specified ORDER BY subclause).

• RANGE is similar to ROWS, except it only computes the result for rows that have the same value as the current row (according to the specified ORDER BY subclause).

• For sliding window frames:

• ROWS is inclusive and is always relative to the current row.

• RANGE is not supported.

• If the specified number of preceding or following ROWS extends beyond the window limits, Snowflake treats the value as NULL.

• If no window frame is specified, the default depends on the function:

• For non-rank-related functions (COUNT, MIN / MAX, SUM), the default is the following cumulative window frame (in accordance with the ANSI standard):

RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW

• For rank-related functions (FIRST_VALUE, LAST_VALUE, NTH_VALUE), the default is the entire window:

ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING

Note that this deviates from the ANSI standard.

## General Tips¶

• The syntax shows all subclauses of the OVER clause as optional for window functions. This is by design (i.e. you can use OVER without any subclauses inside the parentheses). This can be useful in specific scenarios (e.g. turning off parallel processing).

• PARTITION BY is not always compatible with GROUP BY.

• If a query uses more than one window function, it typically should partition each function’s input data set the same way. For example, the first statement below is more likely to be correct than the second statement below:

SELECT SUM(a) OVER (PARTITION BY x), SUM(b) OVER (PARTITION BY x) ... ;

SELECT SUM(a)                      , SUM(b) OVER (PARTITION BY x) ... ;


The error message SQL compilation error: ... is not a valid group by expression is often a sign that different columns in the SELECT statement’s “project” clauses are not partitioned the same way and therefore might produce different numbers of rows.

## Examples¶

Additional examples can be found in Using Window Functions.

### Cumulative Window Frame Examples¶

Create and populate a table with values:

CREATE OR REPLACE TABLE example_cumulative (p INT, o INT, i INT);

INSERT INTO example_cumulative VALUES
(  0, 1, 10), (0, 2, 20), (0, 3, 30),
(100, 1, 10),(100, 2, 30),(100, 2, 5),(100, 3, 11),(100, 3, 120),
(200, 1, 10000),(200, 1, 200),(200, 1, 808080),(200, 2, 33333),(200, 3, null), (200, 3, 4),
(300, 1, null), (300, 1, null);


Run a query that uses a cumulative window frame and show the output. Return a cumulative count, sum, min, and max, for rows in the specified window for the table:

SELECT
p, o, i,
COUNT(i) OVER (PARTITION BY p ORDER BY o ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) count_i_Rows_Pre,
SUM(i)   OVER (PARTITION BY p ORDER BY o ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) sum_i_Rows_Pre,
AVG(i)   OVER (PARTITION BY p ORDER BY o ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) avg_i_Rows_Pre,
MIN(i)   OVER (PARTITION BY p ORDER BY o ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) min_i_Rows_Pre,
MAX(i)   OVER (PARTITION BY p ORDER BY o ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) max_i_Rows_Pre
FROM example_cumulative
ORDER BY p,o;
+-----+---+--------+------------------+----------------+----------------+----------------+----------------+
|   P | O |      I | COUNT_I_ROWS_PRE | SUM_I_ROWS_PRE | AVG_I_ROWS_PRE | MIN_I_ROWS_PRE | MAX_I_ROWS_PRE |
|-----+---+--------+------------------+----------------+----------------+----------------+----------------|
|   0 | 1 |     10 |                1 |             10 |         10.000 |             10 |             10 |
|   0 | 2 |     20 |                2 |             30 |         15.000 |             10 |             20 |
|   0 | 3 |     30 |                3 |             60 |         20.000 |             10 |             30 |
| 100 | 1 |     10 |                1 |             10 |         10.000 |             10 |             10 |
| 100 | 2 |     30 |                2 |             40 |         20.000 |             10 |             30 |
| 100 | 2 |      5 |                3 |             45 |         15.000 |              5 |             30 |
| 100 | 3 |     11 |                4 |             56 |         14.000 |              5 |             30 |
| 100 | 3 |    120 |                5 |            176 |         35.200 |              5 |            120 |
| 200 | 1 |  10000 |                1 |          10000 |      10000.000 |          10000 |          10000 |
| 200 | 1 |    200 |                2 |          10200 |       5100.000 |            200 |          10000 |
| 200 | 1 | 808080 |                3 |         818280 |     272760.000 |            200 |         808080 |
| 200 | 2 |  33333 |                4 |         851613 |     212903.250 |            200 |         808080 |
| 200 | 3 |   NULL |                4 |         851613 |     212903.250 |            200 |         808080 |
| 200 | 3 |      4 |                5 |         851617 |     170323.400 |              4 |         808080 |
| 300 | 1 |   NULL |                0 |           NULL |           NULL |           NULL |           NULL |
| 300 | 1 |   NULL |                0 |           NULL |           NULL |           NULL |           NULL |
+-----+---+--------+------------------+----------------+----------------+----------------+----------------+


Return a cumulative count, sum, min, and max by range for rows in the specified window for the table:

SELECT
p, o, i,
COUNT(i) OVER (PARTITION BY p ORDER BY o RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) count_i_Range_Pre,
SUM(i)   OVER (PARTITION BY p ORDER BY o RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) sum_i_Range_Pre,
AVG(i)   OVER (PARTITION BY p ORDER BY o RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) avg_i_Range_Pre,
MIN(i)   OVER (PARTITION BY p ORDER BY o RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) min_i_Range_Pre,
MAX(i)   OVER (PARTITION BY p ORDER BY o RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) max_i_Range_Pre
FROM example_cumulative
ORDER BY p,o;
+-----+---+--------+-------------------+-----------------+-----------------+-----------------+-----------------+
|   P | O |      I | COUNT_I_RANGE_PRE | SUM_I_RANGE_PRE | AVG_I_RANGE_PRE | MIN_I_RANGE_PRE | MAX_I_RANGE_PRE |
|-----+---+--------+-------------------+-----------------+-----------------+-----------------+-----------------|
|   0 | 1 |     10 |                 1 |              10 |       10.000000 |              10 |              10 |
|   0 | 2 |     20 |                 2 |              30 |       15.000000 |              10 |              20 |
|   0 | 3 |     30 |                 3 |              60 |       20.000000 |              10 |              30 |
| 100 | 1 |     10 |                 1 |              10 |       10.000000 |              10 |              10 |
| 100 | 2 |     30 |                 3 |              45 |       15.000000 |               5 |              30 |
| 100 | 2 |      5 |                 3 |              45 |       15.000000 |               5 |              30 |
| 100 | 3 |     11 |                 5 |             176 |       35.200000 |               5 |             120 |
| 100 | 3 |    120 |                 5 |             176 |       35.200000 |               5 |             120 |
| 200 | 1 |  10000 |                 3 |          818280 |   272760.000000 |             200 |          808080 |
| 200 | 1 |    200 |                 3 |          818280 |   272760.000000 |             200 |          808080 |
| 200 | 1 | 808080 |                 3 |          818280 |   272760.000000 |             200 |          808080 |
| 200 | 2 |  33333 |                 4 |          851613 |   212903.250000 |             200 |          808080 |
| 200 | 3 |   NULL |                 5 |          851617 |   170323.400000 |               4 |          808080 |
| 200 | 3 |      4 |                 5 |          851617 |   170323.400000 |               4 |          808080 |
| 300 | 1 |   NULL |                 0 |            NULL |            NULL |            NULL |            NULL |
| 300 | 1 |   NULL |                 0 |            NULL |            NULL |            NULL |            NULL |
+-----+---+--------+-------------------+-----------------+-----------------+-----------------+-----------------+


Return the same results as the above query by using the default window frame semantics (i.e. RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW):

SELECT
p, o, i,
COUNT(i) OVER (PARTITION BY p ORDER BY o) count_i_Range_Pre,
SUM(i)   OVER (PARTITION BY p ORDER BY o) sum_i_Range_Pre,
AVG(i)   OVER (PARTITION BY p ORDER BY o) avg_i_Range_Pre,
MIN(i)   OVER (PARTITION BY p ORDER BY o) min_i_Range_Pre,
MAX(i)   OVER (PARTITION BY p ORDER BY o) max_i_Range_Pre
FROM example_cumulative
ORDER BY p,o;
+-----+---+--------+-------------------+-----------------+-----------------+-----------------+-----------------+
|   P | O |      I | COUNT_I_RANGE_PRE | SUM_I_RANGE_PRE | AVG_I_RANGE_PRE | MIN_I_RANGE_PRE | MAX_I_RANGE_PRE |
|-----+---+--------+-------------------+-----------------+-----------------+-----------------+-----------------|
|   0 | 1 |     10 |                 1 |              10 |       10.000000 |              10 |              10 |
|   0 | 2 |     20 |                 2 |              30 |       15.000000 |              10 |              20 |
|   0 | 3 |     30 |                 3 |              60 |       20.000000 |              10 |              30 |
| 100 | 1 |     10 |                 1 |              10 |       10.000000 |              10 |              10 |
| 100 | 2 |     30 |                 3 |              45 |       15.000000 |               5 |              30 |
| 100 | 2 |      5 |                 3 |              45 |       15.000000 |               5 |              30 |
| 100 | 3 |     11 |                 5 |             176 |       35.200000 |               5 |             120 |
| 100 | 3 |    120 |                 5 |             176 |       35.200000 |               5 |             120 |
| 200 | 1 |  10000 |                 3 |          818280 |   272760.000000 |             200 |          808080 |
| 200 | 1 |    200 |                 3 |          818280 |   272760.000000 |             200 |          808080 |
| 200 | 1 | 808080 |                 3 |          818280 |   272760.000000 |             200 |          808080 |
| 200 | 2 |  33333 |                 4 |          851613 |   212903.250000 |             200 |          808080 |
| 200 | 3 |   NULL |                 5 |          851617 |   170323.400000 |               4 |          808080 |
| 200 | 3 |      4 |                 5 |          851617 |   170323.400000 |               4 |          808080 |
| 300 | 1 |   NULL |                 0 |            NULL |            NULL |            NULL |            NULL |
| 300 | 1 |   NULL |                 0 |            NULL |            NULL |            NULL |            NULL |
+-----+---+--------+-------------------+-----------------+-----------------+-----------------+-----------------+


### Sliding Window Frame Examples¶

Create and populate a table with values:

CREATE TABLE example_sliding
(p INT, o INT, i INT, r INT, s VARCHAR(100));

INSERT INTO example_sliding VALUES
(100,1,1,70,'seventy'),(100,2,2,30, 'thirty'),(100,3,3,40,'forty'),(100,4,NULL,90,'ninety'),
(100,5,5,50,'fifty'),(100,6,6,30,'thirty'),
(200,7,7,40,'forty'),(200,8,NULL,NULL,'n_u_l_l'),(200,9,NULL,NULL,'n_u_l_l'),(200,10,10,20,'twenty'),
(200,11,NULL,90,'ninety'),
(300,12,12,30,'thirty'),
(400,13,NULL,20,'twenty');


Return the min values for two columns (numeric and string) across sliding windows before, after, and encompassing the current row:

select p, o, i AS i_col,
MIN(i) OVER (PARTITION BY p ORDER BY o ROWS BETWEEN 3 PRECEDING AND 1 PRECEDING) min_i_3P_1P,
MIN(i) OVER (PARTITION BY p ORDER BY o ROWS BETWEEN 1 FOLLOWING AND 3 FOLLOWING) min_i_1F_3F,
MIN(i) OVER (PARTITION BY p ORDER BY o ROWS BETWEEN 1 PRECEDING AND 3 FOLLOWING) min_i_1P_3F,
s,
MIN(s) OVER (PARTITION BY p ORDER BY o ROWS BETWEEN 3 PRECEDING AND 1 PRECEDING) min_s_3P_1P,
MIN(s) OVER (PARTITION BY p ORDER BY o ROWS BETWEEN 1 FOLLOWING AND 3 FOLLOWING) min_s_1F_3F,
MIN(s) OVER (PARTITION BY p ORDER BY o ROWS BETWEEN 1 PRECEDING AND 3 FOLLOWING) min_s_1P_3F
FROM example_sliding
ORDER BY p, o;
+-----+----+-------+-------------+-------------+-------------+---------+-------------+-------------+-------------+
|   P |  O | I_COL | MIN_I_3P_1P | MIN_I_1F_3F | MIN_I_1P_3F | S       | MIN_S_3P_1P | MIN_S_1F_3F | MIN_S_1P_3F |
|-----+----+-------+-------------+-------------+-------------+---------+-------------+-------------+-------------|
| 100 |  1 |     1 |        NULL |           2 |           1 | seventy | NULL        | forty       | forty       |
| 100 |  2 |     2 |           1 |           3 |           1 | thirty  | seventy     | fifty       | fifty       |
| 100 |  3 |     3 |           1 |           5 |           2 | forty   | seventy     | fifty       | fifty       |
| 100 |  4 |  NULL |           1 |           5 |           3 | ninety  | forty       | fifty       | fifty       |
| 100 |  5 |     5 |           2 |           6 |           5 | fifty   | forty       | thirty      | fifty       |
| 100 |  6 |     6 |           3 |        NULL |           5 | thirty  | fifty       | NULL        | fifty       |
| 200 |  7 |     7 |        NULL |          10 |           7 | forty   | NULL        | n_u_l_l     | forty       |
| 200 |  8 |  NULL |           7 |          10 |           7 | n_u_l_l | forty       | n_u_l_l     | forty       |
| 200 |  9 |  NULL |           7 |          10 |          10 | n_u_l_l | forty       | ninety      | n_u_l_l     |
| 200 | 10 |    10 |           7 |        NULL |          10 | twenty  | forty       | ninety      | n_u_l_l     |
| 200 | 11 |  NULL |          10 |        NULL |          10 | ninety  | n_u_l_l     | NULL        | ninety      |
| 300 | 12 |    12 |        NULL |        NULL |          12 | thirty  | NULL        | NULL        | thirty      |
| 400 | 13 |  NULL |        NULL |        NULL |        NULL | twenty  | NULL        | NULL        | twenty      |
+-----+----+-------+-------------+-------------+-------------+---------+-------------+-------------+-------------+


Return the max values for two columns (numeric and string) across sliding windows before, after, and encompassing the current row:

SELECT p, o, i AS i_col,
MAX(i) OVER (PARTITION BY p ORDER BY o ROWS BETWEEN 3 PRECEDING AND 1 PRECEDING) max_i_3P_1P,
MAX(i) OVER (PARTITION BY p ORDER BY o ROWS BETWEEN 1 FOLLOWING AND 3 FOLLOWING) max_i_1F_3F,
MAX(i) OVER (PARTITION BY p ORDER BY o ROWS BETWEEN 1 PRECEDING AND 3 FOLLOWING) max_i_1P_3F,
s,
MAX(s) OVER (PARTITION BY p ORDER BY o ROWS BETWEEN 3 PRECEDING AND 1 PRECEDING) max_s_3P_1P,
MAX(s) OVER (PARTITION BY p ORDER BY o ROWS BETWEEN 1 FOLLOWING AND 3 FOLLOWING) max_s_1F_3F,
MAX(s) OVER (PARTITION BY p ORDER BY o ROWS BETWEEN 1 PRECEDING AND 3 FOLLOWING) max_s_1P_3F
FROM example_sliding
ORDER BY p, o;
+-----+----+-------+-------------+-------------+-------------+---------+-------------+-------------+-------------+
|   P |  O | I_COL | MAX_I_3P_1P | MAX_I_1F_3F | MAX_I_1P_3F | S       | MAX_S_3P_1P | MAX_S_1F_3F | MAX_S_1P_3F |
|-----+----+-------+-------------+-------------+-------------+---------+-------------+-------------+-------------|
| 100 |  1 |     1 |        NULL |           3 |           3 | seventy | NULL        | thirty      | thirty      |
| 100 |  2 |     2 |           1 |           5 |           5 | thirty  | seventy     | ninety      | thirty      |
| 100 |  3 |     3 |           2 |           6 |           6 | forty   | thirty      | thirty      | thirty      |
| 100 |  4 |  NULL |           3 |           6 |           6 | ninety  | thirty      | thirty      | thirty      |
| 100 |  5 |     5 |           3 |           6 |           6 | fifty   | thirty      | thirty      | thirty      |
| 100 |  6 |     6 |           5 |        NULL |           6 | thirty  | ninety      | NULL        | thirty      |
| 200 |  7 |     7 |        NULL |          10 |          10 | forty   | NULL        | twenty      | twenty      |
| 200 |  8 |  NULL |           7 |          10 |          10 | n_u_l_l | forty       | twenty      | twenty      |
| 200 |  9 |  NULL |           7 |          10 |          10 | n_u_l_l | n_u_l_l     | twenty      | twenty      |
| 200 | 10 |    10 |           7 |        NULL |          10 | twenty  | n_u_l_l     | ninety      | twenty      |
| 200 | 11 |  NULL |          10 |        NULL |          10 | ninety  | twenty      | NULL        | twenty      |
| 300 | 12 |    12 |        NULL |        NULL |          12 | thirty  | NULL        | NULL        | thirty      |
| 400 | 13 |  NULL |        NULL |        NULL |        NULL | twenty  | NULL        | NULL        | twenty      |
+-----+----+-------+-------------+-------------+-------------+---------+-------------+-------------+-------------+


Return the sum of a number column across sliding windows before, after, and encompassing the current row:

SELECT p, o, r AS r_col,
SUM(r) OVER (PARTITION BY p ORDER BY o ROWS BETWEEN 4 PRECEDING AND 2 PRECEDING) sum_r_4P_2P,
sum(r) over (partition by p ORDER BY o ROWS BETWEEN 2 FOLLOWING AND 4 FOLLOWING) sum_r_2F_4F,
sum(r) over (partition by p ORDER BY o ROWS BETWEEN 2 PRECEDING AND 4 FOLLOWING) sum_r_2P_4F
FROM example_sliding
ORDER BY p, o;
+-----+----+-------+-------------+-------------+-------------+
|   P |  O | R_COL | SUM_R_4P_2P | SUM_R_2F_4F | SUM_R_2P_4F |
|-----+----+-------+-------------+-------------+-------------|
| 100 |  1 |    70 |        NULL |         180 |         280 |
| 100 |  2 |    30 |        NULL |         170 |         310 |
| 100 |  3 |    40 |          70 |          80 |         310 |
| 100 |  4 |    90 |         100 |          30 |         240 |
| 100 |  5 |    50 |         140 |        NULL |         210 |
| 100 |  6 |    30 |         160 |        NULL |         170 |
| 200 |  7 |    40 |        NULL |         110 |         150 |
| 200 |  8 |  NULL |        NULL |         110 |         150 |
| 200 |  9 |  NULL |          40 |          90 |         150 |
| 200 | 10 |    20 |          40 |        NULL |         110 |
| 200 | 11 |    90 |          40 |        NULL |         110 |
| 300 | 12 |    30 |        NULL |        NULL |          30 |
| 400 | 13 |    20 |        NULL |        NULL |          20 |
+-----+----+-------+-------------+-------------+-------------+