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Mastering SQL Window FunctionsOnline version

Drills to master window functions in SQL

by Good Sam
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AS ORDER sale_date running_total sales amount BY FROM OVER SUM sale_date amount SELECT

Problem 1 : Calculate Running Total
Question : You have a table sales ( sale_date DATE , amount DECIMAL ) . Write a SQL query to calculate a running total of amount , ordered by sale_date .

Solution :

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ROWS sale_date CURRENT as BETWEEN BY sales sale_date CURRENT amount amount FROM BY BY UNBOUNDED amount sale_date PRECEDING current_avg BETWEEN BETWEEN as SUM sales SELECT sale_date FROM amount as AVG BY ROWS ROWS sale_date ROW moving_avg moving_avg amount BETWEEN 3 AVG amount AND amount amount ROW FOLLOWING AND sales sales amount BETWEEN FROM FOLLOWING 6 BY AND ROW 3 OVER OVER ROWS sales UNBOUNDED OVER SUM ROWS FROM ORDER as SELECT ORDER sale_date as FROM CURRENT SELECT sale_date SELECT AND AVG ROW ROW OVER CURRENT sale_date ORDER ORDER ORDER running_total AND PRECEDING sale_date PRECEDING OVER sum_to_end CURRENT

Problem 2 : Calculate Moving Average
Question : Calculate a 7 - day moving average of sales from the sales table .

Solution :

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Example 2 : Fixed Range with Both PRECEDING and FOLLOWING

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This calculates the average amount using a window that includes three rows before , the current row , and three rows after the current row .

Example 3 : From Start of Data to Current Row
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This query computes a running total starting from the first row in the partition or result set up to the current row .

Example 4 : Current Row to End of Data
SELECT sale_date , amount ,
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This sums the amount from the current row to the last row of the partition or result set .

Example 5 : Current Row Only
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This calculates the average of just the current row's amount , which effectively returns the amount itself .

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name OVER total_purchases DESC BY rank id RANK customers total_purchases SELECT FROM ORDER AS

Problem 3 : Rank Customers by Sales

Question : From a table customers ( id INT , name VARCHAR , total_purchases DECIMAL ) , rank customers based on their total_purchases in descending order .

Solution :

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Explanation : RANK ( ) assigns a unique rank to each row , with gaps in the ranking for ties , based on the total_purchases in descending order .

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sales sale_date row_num amount ORDER AS sale_date BY ROW_NUMBER() OVER SELECT FROM

Problem 4 : Row Numbering

Question : Assign a unique row number to each sale in the sales table ordered by sale_date .

Solution :

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Explanation : ROW_NUMBER ( ) generates a unique number for each row , starting at 1 , based on the ordering of sale_date .

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FROM MIN PARTITION customer_id BY first_purchase purchase_date AS OVER customer_id SELECT purchases

Problem 5 : Find the First Purchase Date for Each Customer
Question : Given a table purchases ( customer_id INT , purchase_date DATE ) , write a SQL query to find the first purchase date for each customer .

Solution :

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Explanation : MIN ( ) window function is used here , partitioned by customer_id so that the minimum purchase date is calculated for each customer separately .

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amount LAG amount AS ORDER SELECT sale_date 1 amount AS ORDER sale_date sales_data change_in_amount OVER LAG BY BY 1 amount sale_date OVER FROM previous_day_amount

The LAG function is very useful in scenarios where you need to compare successive entries or calculate differences between them . For example , calculating day - over - day sales changes :


SELECT sale_date ,
amount ,
LAG ( amount , 1 ) OVER ( ORDER BY sale_date ) AS previous_day_amount ,
amount - LAG ( amount , 1 ) OVER ( ORDER BY sale_date ) AS change_in_amount
FROM sales_data ;



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In this query , the change_in_amount field computes the difference in sales between consecutive days . If the LAG function references a row that doesn't exist ( e . g . , the first row in the dataset ) , it will return NULL unless a default value is specified .


The LAG window function in SQL is used to access data from a previous row in the same result set without the need for a self - join . It's a part of the SQL window functions that provide the ability to perform calculations across rows that are related to the current row . LAG is particularly useful for comparisons between records in ordered data .

How LAG Works :
LAG takes up to three arguments :

Expression : The column or expression you want to retrieve from a preceding row .
Offset : An optional integer specifying how many rows back from the current row the function should look . If not specified , the default is 1 , meaning the immediate previous row .
Default : An optional argument that provides a default value to return if the LAG function attempts to go beyond the first row of the dataset .
Syntax :
LAG ( expression , offset , default ) OVER ( [ PARTITION BY partition_expression ] ORDER BY sort_expression )