Funciones de ventana en Snowflake
Jake Roach
Field Data Engineer



SELECT ... AVG(<1>) OVER( PARTITION BY ... ORDER BY <2> -- Ventana entre los X anteriores -- y Y registros siguientesROWS BETWEEN <X> PRECEDING AND <Y> FOLLOWING) ...
Especifica el número de registros antes y después de la fila actual
ROWS BETWEEN X PRECEDING AND Y FOLLOWING
CURRENT ROW$$
<X>: # de filas hacia atrás
<Y>: # de filas hacia adelante
SELECT member_id, workout_date, calories_burned, -- Crear un promedio móvil usando una ventana con el entrenamiento anterior, -- el entrenamiento actual y el siguiente AVG(calories_burned) OVER( PARTITION BY member_id ORDER BY workout_dateROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING) AS moving_avg_cb FROM FITNESS.workouts;
member_id | workout_date | calories_burned | moving_avg_cb
----------- | -------------- | ------------------ | ---------------
m_192 | 2024-01-01 | 105 | 130.5
m_192 | 2024-01-03 | 156 | 110.0
m_192 | 2024-01-04 | 69 | 109.0
m_192 | 2024-01-10 | 102 | 120.0
m_192 | 2024-01-11 | 189 | 127.3
m_192 | 2024-01-12 | 91 | 145.0
m_192 | 2024-01-16 | 155 | 127.7
m_192 | 2024-01-19 | 137 | 133.7
m_192 | 2024-01-20 | 109 | 123.0
m_74 | 2024-02-10 | 374 | 385.0
...
SELECT member_id, workout_date, calories_burned, ... -- Usar CURRENT ROW para evitar "mirar adelante" SUM(calories_burned) OVER( PARTITION BY member_id ORDER BY workout_dateROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS last_3_cb FROM FITNESS.workouts;
member_id | workout_date | calories_burned | moving_avg_cb | last_3_cb
----------- | -------------- | ------------------ | --------------- | -----------
m_192 | 2024-01-01 | 105 | 130.5 | 105
m_192 | 2024-01-03 | 156 | 110.0 | 261
m_192 | 2024-01-04 | 69 | 109.0 | 330
m_192 | 2024-01-10 | 102 | 120.0 | 327
m_192 | 2024-01-11 | 189 | 127.3 | 360
m_192 | 2024-01-12 | 91 | 145.0 | 382
m_192 | 2024-01-16 | 155 | 127.7 | 435
m_192 | 2024-01-19 | 137 | 133.7 | 383
m_192 | 2024-01-20 | 109 | 123.0 | 401
m_74 | 2024-02-10 | 374 | 385.0 | 374
...
Funciones de ventana en Snowflake