To calculate running totals in SQL, you typically use window functions with the SUM() aggregation and an OVER clause. A running total accumulates values row by row based on a specified order, such as dates or IDs. The key is to define a window frame that includes all rows from the start of the dataset up to the current row. For example, if you have a sales table with order_date and amount columns, the query SELECT order_date, amount, SUM(amount) OVER (ORDER BY order_date) AS running_total FROM sales; calculates the cumulative sales amount over time. The ORDER BY order_date in the OVER clause ensures the sum is computed sequentially, adding each row’s amount to the previous total.
An alternative approach for databases that don’t support window functions (e.g., older MySQL versions) involves self-joins. This method joins the table to itself, matching each row with all prior rows based on the ordering column. For instance, using the same sales table, you could write: SELECT s1.order_date, SUM(s2.amount) FROM sales s1 JOIN sales s2 ON s1.order_date >= s2.order_date GROUP BY s1.order_date;. Here, each row s1 aggregates all s2 rows where s2.order_date is earlier or equal to s1.order_date. While this works, it’s less efficient than window functions, especially for large datasets, because it requires quadratic time complexity due to the join and grouping.
When using window functions, consider performance and use cases. Adding a PARTITION BY clause allows separate running totals per group (e.g., SUM(amount) OVER (PARTITION BY region ORDER BY order_date)). Ensure the ordering column (e.g., order_date) is indexed to optimize performance. Window functions are generally preferred for readability and efficiency, as they process data in a single pass. Self-joins may still be useful in limited scenarios but should be avoided for large-scale data. Always verify the SQL dialect’s support for window functions and test query performance with your dataset size.