WINDOW Functions

Introduction to BigQuery

Matthew Forrest

Field CTO

What are WINDOW functions

An illustration of a WINDOW with a rolling SUM

When to use WINDOW functions

WINDOW functions grouped by type

¹ https://towardsdatascience.com/a-guide-to-advanced-sql-window-functions-f63f2642cbf9

WINDOW structure, PARTITION and ORDER BY

SELECT
  customer_id,
  order_date,
  order_total,
  ROW_NUMBER() OVER(

    PARTITION BY customer_id

    ORDER BY order_date

  ) AS order_sequence
FROM orders;

ROW_NUMBER(): The window function that returns the row number
OVER(): Defines the window frame
PARTITION BY customer_id: Groups the data by customer
ORDER BY order_date: Orders data within each partition
order_sequence: Result of the window function operation

RANK and PERCENT_RANK

SELECT
  product_id,
  product_photos_qty,
  -- Ordinal rank for each row
  RANK() OVER(
    ORDER BY product_photos_qty DESC
  ) as rank,
  -- Percentile rank for each row
  PERCENT_RANK() OVER(
    ORDER BY product_photos_qty
  ) as percent
FROM ecommerce.ecomm_products 
ORDER BY product_photos_qty DESC;

Results of the RANK and PERCENT_RANK query

LAG and LEAD

SELECT
  product_id,
  -- Returns value from previous row
  LAG(product_photos_qty) OVER(
    ORDER BY product_photos_qty
  ) as lag,
  product_photos_qty,
  -- Returns value from next row
  LEAD(product_photos_qty) OVER(
    ORDER BY product_photos_qty
  ) as lead
FROM ecommerce.ecomm_products 
ORDER BY product_photos_qty DESC;

Image showing LAG and LEAD in query results

RANGE BETWEEN and CURRENT ROW

SELECT
  order_id,
  order_timestamp,
  SUM(cost) OVER(
    ORDER BY order_timestamp 
    ROWS BETWEEN 2 PRECEDING 
    AND CURRENT ROW) as rolling_avg
FROM sales_data
ORDER BY order_timestamp

Row-based bounding options:

UNBOUNDED PRECEDING: All rows before
UNBOUNDED FOLLOWING: All rows after
[INT] ROWS PRECEDING: Specific number of rows before
[INT] ROWS FOLLOWING: Specific number of rows after

QUALIFY

SELECT
  product_id,
  product_photos_qty,
  RANK() OVER(
    ORDER BY product_photos_qty DESC
  ) as rank
FROM ecommerce.ecomm_products 
-- Filter using QUALIFY
QUALIFY rank < 4
ORDER BY product_photos_qty DESC;

Results of our query to use QUALIFY to find values with a rank fo 3 or higher

Can't use HAVING as we are not aggregating

Let's practice!

Introduction to BigQuery