MySQL's LAG() window function helps you compare sales totals across rows and calculate changes between consecutive records. See how to retrieve previous row values, order sales data by date, and calculate sales differences using the LAG() function in MySQL: https://dev.mysql.com/doc/refman/26.10/en/window-function-descriptions.html#function_lag
Learn how to use the MySQL LAG() window function to compare values between consecutive rows and calculate changes in sales data. This demo shows how LAG() retrieves a value from a previous row, making it possible to compare current and previous sales totals within a SQL query.
The demonstration begins with a table containing sales dates and sales totals. A SQL query retrieves these values in date order, providing a starting point for comparing sales across records. The LAG() function then retrieves the sales total from the previous row, adding it alongside the current row's sales data.
See how the OVER clause defines the window for LAG() and how ORDER BY sales date establishes the sequence used to retrieve previous row values. By default, LAG() looks back one row. For the first row, the function returns NULL because no preceding row exists. Each subsequent row includes the sales total from the previous row in the defined sequence.
The demo also shows how to calculate the difference between consecutive sales totals by subtracting the value returned by LAG() from the current row's total. This calculation produces a new column that displays the change in sales from one row to the next.
Explore how MySQL window functions, SQL row comparisons, and the LAG() function support sales data analysis by making it easier to examine previous values and calculate differences directly within query results.
Demo scripts: https://github.com/mysql/mysql-community/tree/main/content/mysql_shorts/Episode%20106%20-%20lag()
Learn how to use the MySQL LAG() window function to compare values between consecutive rows and calculate changes in sales data. This demo shows how LAG() retrieves a value from a previous row, making it possible to compare current and previous sales totals within a SQL query.
The demonstration begins with a table containing sales dates and sales totals. A SQL query retrieves these values in date order, providing a starting point for comparing sales across records. The LAG() function then retrieves the sales total from the previous row, adding it alongside the current row's sales data.
See how the OVER clause defines the window for LAG() and how ORDER BY sales date establishes the sequence used to retrieve previous row values. By default, LAG() looks back one row. For the first row, the function returns NULL because no preceding row exists. Each subsequent row includes the sales total from the previous row in the defined sequence.
The demo also shows how to calculate the difference between consecutive sales totals by subtracting the value returned by LAG() from the current row's total. This calculation produces a new column that displays the change in sales from one row to the next.
Explore how MySQL window functions, SQL row comparisons, and the LAG() function support sales data analysis by making it easier to examine previous values and calculate differences directly within query results.
Demo scripts: https://github.com/mysql/mysql-community/tree/main/content/mysql_shorts/Episode%20106%20-%20lag()