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Mastering SQL Window FunctionsVersión en línea

Drills to master window functions in SQL

por Good Sam
1

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

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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2

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

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 .

3

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

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 .

4

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

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 .

5

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

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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 .

6

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