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

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

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

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:

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

• Running/Cumulative Total
• Moving Average
• Rank rows by custom criteria and groupings
• Finding the year-over-year % Change

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:

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

• 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.

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

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

• LEAD: Returns the value_expression on the subsequent row.
• LAG: Returns the value_expression on the preceding row.‍
• MODE: Returns the most frequent value for the values within the expression

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

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

How to find a year-over-year change:

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

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:

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

Numbering functions

• RANK: Returns the rank of each row in the ordered partition (starts at 1).
• DENSE_RANK : Returns the rank, but values of the same value get the same rank (starts at 1).
• PERCENT_RANK :  Returns the percentile rank of a row.
• CUME_DIST : Returns the relative rank of a row.
• NTILE:  Returns the bucket number after dividing each partition into constant_integer_expression buckets.
• ROW_NUMBER : Returns the sequential row number for each ordered partition.
• 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.
• 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_TRUE_EVENT: Returns a window event number for each row based on the result of the boolean expression.

How to get top 3 results for each group?

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.

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

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.
• AVG: Returns the average of the non-NULL input values.
• CORR: Returns the coefficient (COVAR_POP(y, x) / (STDDEV_POP(x) * STDDEV_POP(y))) of correlation for a set of number pairs.
• COUNT: Returns the number of [distinct] elements in expression
• COUNT_IF: Returns the count of True values for expression
• COVAR_POP: Returns the population covariance of a set of numbers
• COVAR_SAMP:Returns the sample covariance of a set of numbers
• LISTAGG: Returns the concatenated input values, separated by the delimiter string.
• MAX: Returns the maximum non-NULL value of the expression.
• MEDIAN: Returns the median of a set of values.
• MIN: Returns the minimum non-NULL value of the expression.
• PERCENTILE_CONT: Returns the percentile value based on a continuous distribution of the input column
• PERCENTILE_DISC: Returns the percentile value based on a discrete distribution of the input column
• RATIO_TO_REPORT: Returns the ratio of a value within a group to the sum of the values within a group.
• STDEV: Returns the sample standard deviation (square root of sample variance) of non-NULL values.
• STDEV_POP: Returns population standard deviation of values
• STDEV_SAMP:  Returns sample standard deviation of values
• SUM: Returns the sum of all non-null values
• VAR_POP: Returns the population variance of results
• VARIANCE | VAR_SAMP: Returns the sample variance of results

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

How to find a running total?

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.

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)

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

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

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

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

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

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

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

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

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

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)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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