SQL Window Functions

What are SQL Window Functions?

SQL Window Functions, also known as Analytic Functions or Windowing Functions, are a group of functions that allow you to perform calculations across a set of table rows related to the current row. Unlike regular aggregate functions like SUM or AVG, window functions do not collapse rows into a single value but provide a result for each row in the result set while considering a window of related rows.

When you would use it

You would use SQL Window Functions when you want to:

1. Perform calculations that require context: Calculate running totals, rankings, averages, and other metrics while considering a specific context or subset of rows within a result set.

2. Compare rows to their neighboring rows: Analyze data relationships between the current row and its adjacent rows in the same result set.

3. Avoid self-joins: Instead of using self-joins to compare a row with others, you can use window functions for efficient and more readable queries.

4. Simplify complex queries: Instead of writing complex and nested queries to achieve specific analytical results, window functions provide a cleaner and more straightforward way to handle such tasks.

Syntax

The basic syntax for a SQL Window Function is as follows:

function_name(column_name) OVER (PARTITION BY partition_column ORDER BY order_column [window_frame])

• function_name: The window function you want to use, such as ROW_NUMBER, SUM, RANK, LEAD, or LAG.
• column_name: The column for which you want to perform the calculation.
• PARTITION BY: An optional clause that divides the result set into partitions, allowing you to perform calculations within each partition.
• ORDER BY: Specifies the order of rows within each partition.
• window_frame: An optional frame that defines the range of rows considered for calculations (e.g., rows preceding or following the current row).

Parameter values

• function_name: The name of the window function, such as ROW_NUMBER, SUM, RANK, LEAD, or LAG.
• column_name: The column on which you want to perform calculations.
• PARTITION BY: Optional. A column or columns by which you want to partition the result set.
• ORDER BY: The column by which you want to order the rows within each partition.
• window_frame: Optional. Defines the window frame for rows included in calculations, e.g., "ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING."

Example query

Suppose you have a table "sales" with columns "order_date" and "revenue," and you want to calculate the running total of revenue for each order date. You can use the SUM window function like this:

SELECT order_date, revenue, SUM(revenue) OVER (ORDER BY order_date) AS running_total
FROM sales
ORDER BY order_date;

Example table response

Assuming the "sales" table contains the following data:

| order_date  | revenue |
| ----------- | ------- |
| 2023-01-01  | 100     |
| 2023-01-02  | 150     |
| 2023-01-03  | 200     |
| 2023-01-04  | 75      |

The query mentioned earlier would return the following result:

| order_date  | revenue | running_total |
| ----------- | ------- | ------------- |
| 2023-01-01  | 100     | 100           |
| 2023-01-02  | 150     | 250           |
| 2023-01-03  | 200     | 450           |
| 2023-01-04  | 75      | 525           |

This result shows the running total of revenue for each order date, calculated using the SUM window function.

Use cases

• Calculating running totals, averages, and other metrics within a specific context.
• Comparing rows with their neighboring rows.
• Simplifying complex queries that involve self-joins or nested subqueries.
• Ranking, paging, and filtering data within partitions.

SQL languages this is available for

SQL Window Functions are a standard feature available in many modern relational database management systems (RDBMS), including but not limited to:

• PostgreSQL
• SQL Server
• Oracle Database
• MySQL
• SQLite
• IBM Db2
• MariaDB

The syntax and behavior of window functions are generally consistent across these database systems, although there may be minor variations in specific implementations.