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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:

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

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