LAG

The SQL LAG() function is a powerful analytical function that allows you to access data from a previous row within a result set. Specifically, it allows you to retrieve data from a specified offset before the current row, based on the order of rows defined in the query. This function is useful for a wide range of analytical tasks, such as calculating differences between adjacent rows, identifying trends in data, and performing time-series analysis.

Syntax

The LAG() function takes three arguments: the column you want to retrieve data from, the number of rows back you want to retrieve the data from, and a default value to use if there is no previous row. Here is the basic syntax:

LAG(column, offset, default) OVER (ORDER BY column)

The column argument specifies the column you want to retrieve data from. The offset argument specifies the number of rows back from the current row you want to retrieve data from. Finally, the default argument specifies the value to use if there is no previous row available.

The OVER clause is used to define the order of rows within the result set. You can specify one or more columns to order by, and you can also specify ascending or descending order for each column.

Example

Here’s an example of how to use the LAG() function to calculate the difference between adjacent rows in a table of sales data:

SELECT
order_date,
total_sales,
LAG(total_sales, 1, 0) OVER (ORDER BY order_date) AS previous_sales,
total_sales - LAG(total_sales, 1, 0) OVER (ORDER BY order_date) AS sales_diff
FROM sales_data;

In this example, the LAG() function is used to retrieve the total sales from the previous row, based on the order of rows defined by the order_date column. The sales_diff column then calculates the difference between the current row’s sales and the previous row’s sales.

Overall, the SQL LAG() function is a powerful tool for analyzing data within a result set. Whether you’re working with time-series data or just need to access data from previous rows, the LAG() function can help you perform complex analytical tasks with ease.