**SQL window functions**, also known as analytical functions, are a powerful feature in SQL that allow you to perform calculations across multiple rows in a result set. Window functions operate on a “window” of data that is defined by a specific set of rows within a result set, and can be used to calculate running totals, ranking and percentiles, and other complex calculations that are difficult to achieve with standard SQL queries.

One of the key benefits of window functions is that they allow you to perform calculations on a subset of data within a result set, without having to use subqueries or temporary tables. This makes it much easier to perform complex calculations that would otherwise be very difficult to achieve with standard SQL queries.

## Types

There are several types of window functions available in SQL, including:

Aggregate functions – These functions calculate a value based on a specific subset of rows in a result set, such as the average or sum of a column.

Ranking functions – These functions assign a rank or row number to each row in a result set based on a specified order.

Analytical functions – These functions perform a calculation on a specific set of rows in a result set, but return the result for each row in the result set.

To use a window function, you first need to define a window within your SQL query. This is done using the OVER() clause, which allows you to specify the window specification. The window specification defines the range of rows that will be included in the window, based on criteria such as the order of the rows, the partitioning of the data, and the framing of the window.

### Example

Here’s an example of a simple SQL query that uses a window function to calculate the running total of sales for each month in a sales table:

SELECT month, sales, SUM(sales) OVER (ORDER BY month) AS running_total FROM sales_table;

In this example, the **OVER()** clause is used to define the window specification as “ORDER BY month”, which means that the window includes all rows up to and including the current row, ordered by the month column. The SUM() function is then used to calculate the running total of sales for each row in the result set.

Overall, **window functions** are a powerful and flexible tool in SQL that can help you perform complex calculations and analysis on large datasets. By understanding how to use window functions effectively, you can gain deeper insights into your data and make more informed decisions based on your findings.