Window frames

Window Functions in Snowflake

Jake Roach

Field Data Engineer

Window frames

Output showing a window made up of all records.

Window Functions in Snowflake

Window frames

Output that shows windows built using the member_id field to partition records.

Window Functions in Snowflake

Dynamic window frames

Output that shows a "sliding" window frame.

Window Functions in Snowflake

Finding a running calculation

SELECT
    ...

    AVG(<1>) OVER(
        PARTITION BY ...
        ORDER BY <2>

        -- Window between the first
        -- and current row

ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
) ...

ROWS BETWEEN allows us to create a dynamic window frame

  • UNBOUNDED PRECEEDING AND CURRENT ROW
  • First row in sequence until current row
  • "Running" calculation, not rolling
  • CURRENT ROW AND UNBOUNDED FOLLOWING

$$

<1>: field to take calculation of

<2>: sequences results, builds window frame

Window Functions in Snowflake

Running total of calories burned

SELECT
    member_id AS m_id, 
    calories_burned AS cb,

    -- Running total!
    SUM(calories_burned) OVER(
        PARTITION BY member_id
        ORDER BY workout_date

ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
) AS running_total FROM fitness.workouts;

         m_id  |    cb   |  running_total
       ------- | ------- | --------------
        m_192  |   105   |      105 
        m_192  |   156   |      261 
        m_192  |    69   |      330 
        m_192  |   102   |      432  

        m_74   |   374   |      374  
        m_74   |   396   |      770  
        m_74   |   504   |     1274  

        m_233  |    51   |       51  
        m_233  |    81   |      132  

Window Functions in Snowflake

CURRENT ROW AND UNBOUNDED FOLLOWING

SELECT
    member_id AS m_id, 
    calories_burned AS cb,

    SUM(calories_burned) OVER(
        PARTITION BY member_id
        ORDER BY workout_date

        -- Window between current row and last

ROWS BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING
) AS left_to_burn FROM fitness.workouts;

         m_id  |    cb   |  left_to_burn
       ------- | ------- | --------------
        m_192  |   105   |      432 
        m_192  |   156   |      327 
        m_192  |    69   |      171 
        m_192  |   102   |      102  

        m_74   |   374   |     1274  
        m_74   |   396   |      900 
        m_74   |   504   |      504  

        m_233  |    51   |      132  
        m_233  |    81   |       81  

Window Functions in Snowflake

Calorie-burning trends

SELECT
    member_id,
    workout_date,
    calories_burned,

    AVG(calories_burned) OVER(   -- Running average of calories burned
        PARTITION BY member_id

        -- Crate a window by workout date, from the first workout to the current workout 
        ORDER BY workout_date
        ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW

    ) AS avg_calories_burned

FROM FITNESS.workouts;
Window Functions in Snowflake

Calorie-burning trends


            member_id  |  workout_date  |  calories_burned  |  average_calories_burned
           ----------- | -------------- | ----------------- | -------------------------
              m_192    |   2025-01-01   |        105        |          105.0
              m_192    |   2025-01-03   |        156        |          130.5
              m_192    |   2025-01-04   |         69        |          110.0
              m_192    |   2025-01-10   |        102        |          108.0

               m_74    |   2025-02-10   |        374        |          374.0
               m_74    |   2025-02-13   |        396        |          385.0
               m_74    |   2025-02-14   |        504        |          426.7

              m_233    |   2025-03-05   |         51        |           51.0
              m_233    |   2025-03-12   |         81        |           66.0

Window Functions in Snowflake

Let's practice!

Window Functions in Snowflake

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