SQL Running Total

How do you calculate a running total in SQL?

A running total, also known as a cumulative sum, calculates a sum of values up to a given point in a dataset. SQL provides ways to achieve this using window functions, offering a powerful tool for analyzing trends and patterns in data.

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Description

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Running totals are crucial for tracking changes over time or across categories. Imagine analyzing sales figures; you might want to see how total sales grow each month. Or, in a logistics context, you might need to calculate the cumulative weight of packages loaded onto a truck. SQL's window functions are the key to efficiently calculating these running totals.Window functions operate on a set of rows related to the current row, without grouping the data. This is different from aggregate functions like SUM, which group data into summary rows. The `SUM()` function, when used with a `PARTITION BY` clause, can be part of a running total calculation, but it doesn't inherently provide the cumulative effect.The `SUM() OVER()` window function is the most common way to calculate running totals. The `OVER()` clause specifies the window of rows to consider when calculating the sum. Different window frame specifications allow for various running total calculations, such as cumulative sums, running averages, and more.

Why SQL Running Total is important

Running totals are essential for data analysis and reporting. They provide insights into trends, allowing businesses to make data-driven decisions. For example, tracking sales growth over time helps identify seasonal patterns or marketing campaign effectiveness.

SQL Running Total Example Usage


-- Example using PostgreSQL
SELECT customer_name
FROM customers
WHERE customer_name REGEXP '^[A-Z][a-z]+';

-- Example using MySQL
SELECT customer_name
FROM customers
WHERE customer_name REGEXP '^[A-Z][a-z]+';

-- Example extracting city from address
SELECT
    REGEXP_SUBSTR(address, '[^ ]+\s[^ ]+$') AS city
FROM
    customers;

SQL Running Total Syntax

Common Mistakes

Forgetting to use `OVER()` clause with window functions.
Incorrectly specifying the `ORDER BY` clause in the `OVER()` clause, leading to incorrect cumulative sums.
Confusing window functions with aggregate functions like `SUM()` without `OVER()`
Not understanding the difference between `PARTITION BY` and `ORDER BY` within the `OVER()` clause.

Frequently Asked Questions (FAQs)

Why is `SUM() OVER()` preferred over a classic `SUM()` + `GROUP BY` for running totals?

A grouped SUM() collapses rows into a single aggregate, so you lose the row-by-row view that a running total requires. The window-function form SUM(amount) OVER (PARTITION BY category ORDER BY transaction_date) keeps every original row and simply adds a cumulative column beside it. That means you can sort, filter, or join on the detailed data while still seeing the continuously growing total—all in one pass through the table.

How does the window frame definition change the result of a running total?

In a window function, the frame tells the database which rows to include when calculating the value for the current row. For a true cumulative total you typically use ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW, which includes every prior row up to the current one. Switching to something like ROWS BETWEEN 3 PRECEDING AND CURRENT ROW would instead give you a 4-row rolling sum, while RANGE BETWEEN INTERVAL '30 days' PRECEDING AND CURRENT ROW delivers a time-based running total. Choosing the right frame is what turns a generic window sum into the exact running or rolling metric you need.

How can Galaxy make writing and sharing running-total queries faster?

Galaxy’s context-aware AI copilot autocompletes window-function syntax, suggests optimal frame clauses, and even explains the difference between cumulative and rolling totals as you type. Once your query is ready, you can drop it into a Galaxy Collection and “Endorse” it so teammates reuse the same, trusted running-total logic—no more pasting SQL into Slack threads.