Fill in the Blanks: Mastering SQL Window Functions


Fill in the Blanks Mastering SQL Window FunctionsOnline version Drills to master window functions in SQL by Good Sam 1 SUM OVER SELECT ORDER BY sales FROM sale_date AS sale_date running_total amount amount 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 : , , ( ) ( ) ; 2 amount sale_date moving_avg AND amount sale_date OVER as current_avg BETWEEN ROW BY sum_to_end AVG BETWEEN amount AND FROM ROW as FOLLOWING amount FROM sale_date ROWS SUM BETWEEN ROW sale_date PRECEDING BY ORDER 6 ROW PRECEDING ROWS PRECEDING ROWS 3 as CURRENT SELECT sale_date 3 sale_date moving_avg ROWS sale_date ORDER AVG CURRENT UNBOUNDED running_total AND AVG amount OVER as SELECT sales ROWS CURRENT amount BETWEEN amount sales CURRENT sales sales CURRENT sale_date AND amount SELECT OVER as OVER SELECT ROW sale_date OVER sales FROM AND FOLLOWING BY FROM amount BY BY ORDER UNBOUNDED ORDER ORDER SUM FROM BETWEEN Problem 2 : Calculate Moving Average Question : Calculate a 7 - day moving average of sales from the sales table . Solution : , , ( ) ( ) ; Example 2 : Fixed Range with Both PRECEDING and FOLLOWING , , ( ) ( ) ; 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 , , ( ) ( ) ; 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 , ( ) ( ) ; This sums the amount from the current row to the last row of the partition or result set . Example 5 : Current Row Only , , ( ) ( ) ; This calculates the average of just the current row's amount , which effectively returns the amount itself . 3 SELECT OVER AS ORDER total_purchases RANK total_purchases BY rank id FROM customers DESC name 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 : , , , ( ) ( ) ; Explanation : RANK ( ) assigns a unique rank to each row , with gaps in the ranking for ties , based on the total_purchases in descending order . 4 ROW_NUMBER() OVER sale_date AS BY row_num FROM amount SELECT sale_date ORDER sales Problem 4 : Row Numbering Question : Assign a unique row number to each sale in the sales table ordered by sale_date . Solution : , , ( ) ; Explanation : ROW_NUMBER ( ) generates a unique number for each row , starting at 1 , based on the ordering of sale_date . 5 FROM purchases customer_id SELECT PARTITION BY purchase_date customer_id AS MIN OVER first_purchase 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 : , ( ) ( ) ; Explanation : MIN ( ) window function is used here , partitioned by customer_id so that the minimum purchase date is calculated for each customer separately . 6 AS sale_date amount AS LAG amount BY FROM LAG SELECT sales_data amount 1 ORDER sale_date 1 OVER change_in_amount previous_day_amount OVER sale_date amount BY ORDER 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 ; , , ( , ) ( ) , - ( , ) ( ) ; 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 )

1

SUM OVER SELECT ORDER BY sales FROM sale_date AS sale_date running_total amount amount

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 :

, ,
( ) ( )
;

2

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

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

Solution :

, ,
( ) ( )
;

Example 2 : Fixed Range with Both PRECEDING and FOLLOWING

, ,
( ) ( )
;

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
, ,
( ) ( )
;

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 ,
( ) ( )
;

This sums the amount from the current row to the last row of the partition or result set .

Example 5 : Current Row Only
, ,
( ) ( )
;

This calculates the average of just the current row's amount , which effectively returns the amount itself .

3

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

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 :

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

4

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

Problem 4 : Row Numbering

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

Solution :

, ,
( )
;

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

5

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

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 :

, ( ) ( )
;

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

6

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

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 ;

,
,
( , ) ( ) ,
- ( , ) ( )
;

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 )

Mastering SQL Window Functions

You can integrate the game into an LMS compatible with LTI 1.1 or LTI 1.3 such as Canvas, Moodle, or Blackboard. This way, the scores will be automatically saved into the platform’s gradebook.

Mastering SQL Window Functions