To calculate running totals in SQL, you typically use window functions, specifically the SUM() function with the OVER() clause. This method allows you to create a cumulative sum of a specified column while maintaining the order of your dataset. A running total adds the current row's value to the total of all previous rows based on a defined order, providing insight into trends over time or through a sequence of events.
For instance, consider a table named Sales that includes SaleDate and Revenue columns. To compute a running total of revenue over time, you can structure your SQL query as follows:
SELECT
SaleDate,
Revenue,
SUM(Revenue) OVER (ORDER BY SaleDate) AS RunningTotal
FROM
Sales
ORDER BY
SaleDate;
In this query, SUM(Revenue) computes the cumulative total, while the ORDER BY SaleDate within the OVER() clause ensures that the totals are calculated in chronological order. The result will display each sale alongside its respective running total. The use of an ORDER BY clause is essential because it defines how the rows are processed to calculate the running total.
You can also customize running totals by adding a PARTITION BY clause if you want to calculate separate running totals for different groups within your data. For example, if you have a Region column and you want to get running totals for each region, you could modify the query as follows:
SELECT
SaleDate,
Region,
Revenue,
SUM(Revenue) OVER (PARTITION BY Region ORDER BY SaleDate) AS RunningTotal
FROM
Sales
ORDER BY
Region, SaleDate;
In this version, the running total is calculated separately for each region based on the sale dates within that region. This makes window functions very versatile for handling various data analysis scenarios, providing clear insights into progress and performance over time.