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 sale_date sales amount FROM BY ORDER OVER AS SUM SELECT amount running_total sale_date 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 CURRENT SUM amount ROW ORDER ROW sale_date as CURRENT BY PRECEDING CURRENT FROM FROM CURRENT sales CURRENT AVG BY amount 3 FROM moving_avg sales sale_date sale_date OVER sale_date BETWEEN SELECT sale_date ORDER ORDER UNBOUNDED AND ROWS AND 3 amount sale_date ORDER sale_date as BETWEEN OVER OVER AND UNBOUNDED amount as AND ROW sales AND sale_date BETWEEN AVG as PRECEDING AVG BY moving_avg FOLLOWING OVER amount ROW SUM BY ROW SELECT amount sale_date 6 FOLLOWING BETWEEN FROM ORDER ROWS sales sum_to_end BY ROWS FROM as running_total BETWEEN ROWS amount PRECEDING SELECT OVER current_avg amount sales amount ROWS SELECT 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 total_purchases id DESC AS RANK customers total_purchases BY SELECT ORDER rank OVER name FROM 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 AS sales ROW_NUMBER() OVER sale_date row_num amount BY FROM SELECT sale_date ORDER 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 customer_id customer_id AS FROM BY first_purchase SELECT purchases MIN PARTITION purchase_date OVER 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 FROM amount sale_date sale_date BY amount OVER change_in_amount 1 SELECT previous_day_amount LAG ORDER BY sales_data amount amount sale_date OVER LAG 1 AS 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

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

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

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

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

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

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

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

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

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

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