Window Functions

Window functions in Snowflake are a way to compute values over a group of rows. They return a single value for each row, in contrast to aggregate functions which return a single value for a group of rows.

Aggregate vs window/analytic functions

SELECT
  letter,
  SUM(number) AS aggregate
FROM
  (
    SELECT
      'A' AS letter,
      2 AS number
    UNION ALL
(    SELECT
      'A' AS letter,
      3 AS number)
    UNION ALL
(    SELECT
      'C' AS letter,
      6 AS number)
  ) AS table_3
GROUP BY
  letter

LETTER	AGGREGATE
A	5
C	6

SELECT
  letter,
  SUM(number) OVER (PARTITION BY letter) AS "WINDOW"
FROM
  (
    SELECT
      'A' AS letter,
      2 AS number
    UNION ALL
(    SELECT
      'A' AS letter,
      3 AS number)
    UNION ALL
(    SELECT
      'C' AS letter,
      6 AS number)
  ) AS table_3

LETTER	WINDOW
A	5
A	5
C	6

In the first aggregate example, the resulting data were grouped by letter, but in the second window example, we preserved our rows.

Why use window functions?

Window functions are very powerful, and once you've gotten your head around how to use them, you'll be surprised at what they allow you to do. Some common use cases are:

Running/Cumulative Total

Moving Average

Rank rows by custom criteria and groupings

Finding the year-over-year % Change

Syntax

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

where the PARTITION BY denotes how to GROUP rows into partitions,ORDER BY how to order the rows in those partitions, and FRAME which determines which rows to consider in those ordered partitions.

In general, window functions can be grouped into 3 types:

Navigation functions: Return the value given a specific location criteria (e.g. first_value)

Numbering functions: Assign a number (e.g. rank) to each row based on their position in the specified window

Analytic functions: Perform a calculation on a set of values (e.g. sum)

Navigation functions

FIRST_VALUE: Returns the value_expression for the first row in the current window frame.

FIRST_VALUE( <expr> ) [ { IGNORE | RESPECT } NULLS ]
                      OVER ( [ PARTITION BY <expr1> ] ORDER BY <expr2>  [ { ASC | DESC } ] [ <window_frame> ] )

LAST_VALUE: Returns the value_expression for the last row in the current window frame.

LAST_VALUE( <expr> ) [ { IGNORE | RESPECT } NULLS ]
                     OVER ( [ PARTITION BY <expr1> ] ORDER BY <expr2> [ { ASC | DESC } ] [ <window_frame> ] )

NTH_VALUE: Returns the value_expression for the Nth row of the current window frame.

NTH_VALUE( <expr> , n ) [ FROM { FIRST | LAST } ] [ { IGNORE | RESPECT } NULLS ]
                        OVER ( [ PARTITION BY <expr1> ] ORDER BY <expr2> [ { ASC | DESC } ] [ <window_frame> ] )

LEAD: Returns the value_expression on the subsequent row.

LEAD ( <expr> [ , <offset> , <default> ] ) [ { IGNORE | RESPECT } NULLS ] OVER ( [ PARTITION BY <expr1> ] ORDER BY <expr2> [ { ASC | DESC } ] )

LAG: Returns the value_expression on the preceding row.‍

MODE: Returns the most frequent value for the values within the expression

MODE( <expr1> ) OVER ( [ PARTITION BY <expr2> ] )

For these functions we'll use the following demo data:

SELECT
  'James Harden' AS player,
  2335 AS points,
  2020 AS season
UNION ALL
(SELECT
  'Damian Lillard' AS player,
  1978 AS points,
  2020 AS season)
UNION ALL
(SELECT
  'Devin Booker' AS player,
  1863 AS points,
  2020 AS season)
UNION ALL
(SELECT
  'James Harden' AS player,
  2818 AS points,
  2019 AS season)
UNION ALL
(SELECT
  'Paul George' AS player,
  1978 AS points,
  2019 AS season)
UNION ALL
(SELECT
  'Kemba Walker' AS player,
  2102 AS points,
  2019 AS season)
UNION ALL
(SELECT
  'Damian Lillard' AS player,
  2067 AS points,
  2019 AS season)
UNION ALL
(SELECT
  'Devin Booker' AS player,
  1700 AS points,
  2019 AS season)
UNION ALL
(SELECT
  'Paul George' AS player,
  1033 AS points,
  2020 AS season)
UNION ALL
(SELECT
  'Kemba Walker' AS player,
  1145 AS points,
  2020 AS season)

PLAYER	POINTS	SEASON
James Harden	2335	2020
Damian Lillard	1978	2020
Devin Booker	1863	2020
James Harden	2818	2019
Paul George	1978	2019
Kemba Walker	2102	2019
Damian Lillard	2067	2019
Devin Booker	1700	2019
Paul George	1033	2020
Kemba Walker	1145	2020

How to find a year-over-year change:

SELECT DISTINCT
  player,
  FIRST_VALUE(POINTS) OVER (PARTITION BY PLAYER ORDER BY SEASON ASC) AS first_season,
  LAST_VALUE(POINTS) OVER (PARTITION BY PLAYER ORDER BY SEASON ASC) AS last_season,
  (100 * ((LAST_VALUE(points) OVER (PARTITION BY player ORDER BY season ASC) - FIRST_VALUE(points) OVER (PARTITION BY player ORDER BY season ASC)) / FIRST_VALUE(points) OVER (PARTITION BY player ORDER BY season ASC))) AS per_change  
FROM
  TOP_SCORERS
ORDER BY PLAYER

PLAYER	FIRST_SEASON	LAST_SEASON	PER_CHANGE
Damian Lillard	2067	1978	-4.3058
Devin Booker	1700	1863	9.5882
James Harden	2818	2335	-17.1398
Kemba Walker	2102	1145	-45.5281
Paul George	1978	1033	-47.7755

We used FIRST_VALUE and LAST_VALUE to find the scores for each player in the earliest and most recent seasons of data. Then we computed the percent difference using:

100 * ((new value - old value) / old value) per_difference

Numbering functions

RANK: Returns the rank of each row in the ordered partition (starts at 1).

RANK() OVER ( [ PARTITION BY <expr1> ] ORDER BY <expr2> [ { ASC | DESC } ] [ <window_frame> ] )

DENSE_RANK : Returns the rank, but values of the same value get the same rank (starts at 1).

PERCENT_RANK() OVER ( [ PARTITION BY <expr1> ] ORDER BY <expr2> [ { ASC | DESC } ] [ <cumulativeRangeFrame> ] )

PERCENT_RANK : Returns the percentile rank of a row.

PERCENT_RANK() OVER ( [ PARTITION BY <expr1> ] ORDER BY <expr2> [ { ASC | DESC } ] [ <cumulativeRangeFrame> ] )

CUME_DIST : Returns the relative rank of a row.

CUME_DIST() OVER ( [ PARTITION BY <partition_expr> ] ORDER BY <order_expr>  [ ASC | DESC ] )

NTILE: Returns the bucket number after dividing each partition into constant_integer_expression buckets.

NTILE( <constant_value> ) OVER ( [ PARTITION BY <expr1> ] ORDER BY <expr2> [ { ASC | DESC } ] )

ROW_NUMBER : Returns the sequential row number for each ordered partition.

ROW_NUMBER() OVER (
  [ PARTITION BY <expr1> [, <expr2> ... ] ]
  ORDER BY <expr3> [ , <expr4> ... ] [ { ASC | DESC } ]
  )

WIDTH_BUCKET : Constructs equi-width histograms, in which the histogram range is divided into intervals of identical size, and returns the bucket number into which the value of an expression falls after it has been evaluated.

WIDTH_BUCKET( <expr> , <min_value> , <max_value> , <num_buckets> )

CONDITIONAL_CHANGE_EVENT: Returns a window event number for each row when the value of the expression is different from the value in the previous row.

CONDITIONAL_CHANGE_EVENT( <expr1> ) OVER ( [ PARTITION BY <expr2> ] ORDER BY <expr3> )

CONDITIONAL_TRUE_EVENT: Returns a window event number for each row based on the result of the boolean expression.

CONDITIONAL_TRUE_EVENT( <expr1> ) OVER ( [ PARTITION BY <expr2> ] ORDER BY <expr3> )

How to get top 3 results for each group?

SELECT
 *
FROM
 (
   SELECT
     season,
     RANK() OVER (PARTITION BY season ORDER BY points DESC) AS points_rank,
     player,
     points
   FROM
     TOP_SCORERS
 ) AS table_1
WHERE
 (points_rank <= 3)

SEASON	POINTS_RANK	PLAYER	POINTS
2019	1	James Harden	2818
2019	2	Kemba Walker	2102
2019	3	Damian Lillard	2067
2020	1	James Harden	2335
2020	2	Damian Lillard	1978
2020	3	Devin Booker	1863

In this example, we used RANK to rank each player by points over each season. Then we used a subquery to then return only the top 3 ranked players for each season.

Aggregate functions

Aggregate functions are available outside of window functions, but can be additionally applied over a specified window.

ANY_VALUE: Returns expression for some row chosen from the group. Basically a random selection from an expression.

ANY_VALUE( [ DISTINCT ] <expr1> ) OVER ( [ PARTITION BY <expr2> ] )

AVG: Returns the average of the non-NULL input values.

AVG( <expr1> ) OVER ( [ PARTITION BY <expr2> ] [ ORDER BY <expr3> [ ASC | DESC ] [ <window_frame> ] ] )

CORR: Returns the coefficient (COVAR_POP(y, x) / (STDDEV_POP(x) * STDDEV_POP(y))) of correlation for a set of number pairs.

CORR( y , x ) OVER ( [ PARTITION BY <expr3> ] )

COUNT: Returns the number of [distinct] elements in expression

COUNT( <expr1> [ , <expr2> ... ] )
    OVER ( [ PARTITION BY <expr3> ] [ ORDER BY <expr4> [ ASC | DESC ] [ <window_frame> ] ] )

COUNT_IF: Returns the count of True values for expression

COUNT_IF( <condition> )
    OVER ( [ PARTITION BY <expr1> ] [ ORDER BY <expr2> [ ASC | DESC ] [ <window_frame> ] ] )

COVAR_POP: Returns the population covariance of a set of numbers

COVAR_POP( y , x ) OVER ( [ PARTITION BY <expr1> ] )

COVAR_SAMP:Returns the sample covariance of a set of numbers

COVAR_SAMP( y , x ) OVER ( [ PARTITION BY <expr1> ] )

LISTAGG: Returns the concatenated input values, separated by the delimiter string.

LISTAGG( [ DISTINCT ] <expr1> [, <delimiter> ] )
    [ WITHIN GROUP ( <orderby_clause> ) ]
    OVER ( [ PARTITION BY <expr2> ] )

MAX: Returns the maximum non-NULL value of the expression.

MAX( <expr> ) [ OVER ( [ PARTITION BY <expr1> ] [ ORDER BY <expr2> [ <window_frame> ] ] ) ]

MEDIAN: Returns the median of a set of values.

MEDIAN( <expr> ) OVER ( [ PARTITION BY <expr2> ] )

MIN: Returns the minimum non-NULL value of the expression.

MIN( <expr> ) [ OVER ( [ PARTITION BY <expr1> ] [ ORDER BY <expr2> [ <window_frame> ] ] ) ]

PERCENTILE_CONT: Returns the percentile value based on a continuous distribution of the input column

PERCENTILE_CONT( <percentile> ) WITHIN GROUP (ORDER BY <order_by_expr>) OVER ( [ PARTITION BY <expr3> ] )

PERCENTILE_DISC: Returns the percentile value based on a discrete distribution of the input column

PERCENTILE_DISC( <percentile> ) WITHIN GROUP (ORDER BY <order_by_expr> ) OVER ( [ PARTITION BY <expr3> ] )

RATIO_TO_REPORT: Returns the ratio of a value within a group to the sum of the values within a group.

RATIO_TO_REPORT( <expr1> ) [ OVER ( [ PARTITION BY <expr2> ] [ ORDER BY <expr3> ] ) ]

STDEV: Returns the sample standard deviation (square root of sample variance) of non-NULL values.

STDDEV( <expr1> ) OVER (
                       [ PARTITION BY <expr2> ]
                       [ ORDER BY <expr3> [ ASC | DESC ] [ <window_frame> ] ]
                       )

STDEV_POP: Returns population standard deviation of values

STDDEV_POP( <expr1> ) OVER (
                           [ PARTITION BY <expr2> ]
                           [ ORDER BY <expr3> [ ASC | DESC ] [ <window_frame> ] ]
                           )

STDEV_SAMP: Returns sample standard deviation of values

STDDEV_SAMP( <expr1> ) OVER (
                            [ PARTITION BY <expr2> ]
                            [ ORDER BY <expr3> [ ASC | DESC ] [ <window_frame> ] ]
                            )

SUM: Returns the sum of all non-null values

SUM( <expr1> ) OVER ( [ PARTITION BY <expr2> ] [ ORDER BY <expr3> [ ASC | DESC ] [ <window_frame> ] ] )

VAR_POP: Returns the population variance of results

VARIANCE_POP( <expr1> ) OVER (
                             [ PARTITION BY <expr2> ]
                             [ ORDER BY <expr3> [ ASC | DESC ] [ <window_frame> ] ]
                             )

VARIANCE | VAR_SAMP: Returns the sample variance of results

VARIANCE( <expr1> ) OVER (
                         [ PARTITION BY <expr2> ]
                         [ ORDER BY <expr3> [ ASC | DESC ] [ <window_frame> ] ]
                         )

‍

There are more, less commonly used window functions available here.

How to find a running total?

SELECT
  season,
  player,
  points,
  SUM(top_scorers.points) OVER (PARTITION BY player ORDER BY season ASC) AS running_total_points
FROM
  TOP_SCORERS
ORDER BY PLAYER ASC, SEASON ASC

‍

SEASON	PLAYER	POINTS	RUNNING_TOTAL_POINTS
2019	Damian Lillard	2067	2067
2020	Damian Lillard	1978	4045
2019	Devin Booker	1700	1700
2020	Devin Booker	1863	3563
2019	James Harden	2818	2818
2020	James Harden	2335	5153
2019	Kemba Walker	2102	2102
2020	Kemba Walker	1145	3247
2019	Paul George	1978	1978
2020	Paul George	1033	3011

To find the running total simply use SUM with an OVER clause where you specify your groupings (PARTITION BY), and the order in which to add them (ORDER BY).

How We Built This

This page was built using Count, the first notebook built around SQL. It combines the best features of a SQL IDE, Data Visualization Tool, and Computational Notebooks. In the Count notebook, each cell acts like a CTE, meaning you can reference any other cell in your queries.

This makes not only for far more readable reports (like this one) but also a much faster and more powerful way to do your analysis, essentially turning your analysis into a connected graph of data frames rather than one-off convoluted queries and CSV files. And with a built-in visualization framework, you won't have to export your data to make your charts. Go from raw data to interactive report in one document.

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