In SQL, the ROLLUP operator allows us to generate subtotal calculations in our query results. Subtotals can provide valuable insights by summarizing data at different levels of granularity. This article explores how to use the ROLLUP operator to perform subtotal calculations in SQL, specifically focusing on generating a “Total” label for the “product” column.

The ROLLUP operator is used in conjunction with the GROUP BY clause to produce subtotal calculations. It generates result sets that include not only the individual groupings but also subtotal rows representing higher-level summaries.

Let’s consider the following example using a sales table:

SELECT 
  CASE WHEN product IS NULL THEN 'Total' ELSE product END AS product,
  region,
  SUM(sales) AS total_sales
FROM sales
GROUP BY ROLLUP(product, region)
HAVING product IS NOT NULL OR region IS NOT NULL

In this query, the CASE statement is employed to replace null values in the “product” column with the label “Total.” The ROLLUP(product, region) expression generates the necessary subtotals and the GROUP BY clause specifies the grouping criteria. The HAVING clause is used to filter out the rows representing subtotals.

The resulting output displays the “product,” “region,” and “total_sales” columns, with the “Total” label appearing in the “product” column for the subtotal row.

By utilizing the ROLLUP operator and the CASE statement, we were able to generate subtotal calculations and display the label “Total” in the “product” column. This allows for a clear distinction between individual product entries and the subtotal row.

Performing subtotal calculations with ROLLUP in SQL provides a powerful way to analyze and summarize data at different levels of granularity. By incorporating the CASE statement, we can customize the labels displayed in our query results. In this article, we focused on generating a “Total” label for the “product” column. Understanding and utilizing these techniques can enhance your SQL skills and enable you to extract valuable insights from your data.

I hope you find this article helpful. Happy coding!