Hello Dev! In today’s article, we’ll be taking a deep dive into SQL Server windowed functions. Windowed functions are a powerful feature in SQL Server that allows you to perform calculations on a specific window or subset of data, rather than the entire dataset.
What are Windowed Functions?
Windowed functions, also known as analytic functions, are a type of SQL function that allows you to perform calculations on a specific window or subset of data. This is useful when you need to perform calculations on a specific range of rows or columns, rather than the entire dataset.
Windowed functions are used with the OVER clause, which defines the window or subset of data on which the function is performed. The window can be defined based on a number of criteria such as a date range, a partition of data or a specific order of data.
Windowed functions are distinct from aggregate functions, which perform calculations on the entire dataset. With windowed functions, you can perform calculations on a subset of the data and return the results alongside the original dataset.
How do Windowed Functions Work?
Windowed functions operate on a subset of the data defined by the OVER clause. The OVER clause specifies the windowing criteria, which can be based on a range of rows or columns, a partition of data or a specific order of data.
The windowing criteria used by the OVER clause is similar to the GROUP BY clause used with aggregate functions. However, unlike GROUP BY, windowing functions do not group the data, but rather partition it into distinct subsets.
For example, if you have a dataset that includes sales data for different regions, you can use the PARTITION BY clause to partition the data by region. You can then use a windowed function to calculate the percentage of sales for each region.
Why Use Windowed Functions?
Windowed functions are useful when you need to perform calculations on a specific subset of data, rather than the entire dataset. This can be helpful when you need to calculate statistics such as running totals, moving averages or rank values based on specific criteria.
Windowed functions can also be used to return additional information alongside the original dataset. For example, you could use a windowed function to calculate the total sales for each region, and return this information alongside the original sales data.
Types of Windowed Functions
SQL Server supports a range of windowed functions that can be used for different purposes. In this section, we’ll cover some of the most commonly used types of windowed functions.
Aggregate Functions
Aggregate functions are used to perform calculations on a specific subset of data, such as a partition or range of data. Unlike regular aggregate functions, windowed aggregate functions return a result for each row in the partition.
Commonly used windowed aggregate functions include SUM, AVG, COUNT, MAX and MIN.
Function |
Description |
|---|---|
SUM() |
Returns the sum of a specific column within the window. |
AVG() |
Returns the average of a specific column within the window. |
COUNT() |
Returns the count of non-null values within the window. |
MAX() |
Returns the maximum value within the window. |
MIN() |
Returns the minimum value within the window. |
Ranking Functions
Ranking functions are used to assign a rank or order to a specific subset of data based on specific criteria. The rank assigned to each row is based on the order defined by the OVER clause.
Commonly used windowed ranking functions include RANK, DENSE_RANK and ROW_NUMBER.
Function |
Description |
|---|---|
RANK() |
Returns the rank of each row within the window, with ties receiving the same rank. |
DENSE_RANK() |
Returns the rank of each row within the window, with ties receiving the same rank and no gaps in the ranking values. |
ROW_NUMBER() |
Returns a unique number for each row within the window, in the order defined by the OVER clause. |
Offset Functions
Offset functions allow you to perform calculations on rows that are offset from the current row by a specific number of rows or columns. This can be useful when you need to calculate values such as running totals or moving averages.
Commonly used windowed offset functions include LAG, LEAD and FIRST_VALUE/LAST_VALUE.
Function |
Description |
|---|---|
LAG() |
Returns the value from a specific column in the row that is offset by a specific number of rows before the current row. |
LEAD() |
Returns the value from a specific column in the row that is offset by a specific number of rows after the current row. |
FIRST_VALUE() |
Returns the first value of a specific column within the window. |
LAST_VALUE() |
Returns the last value of a specific column within the window. |
Using Windowed Functions in SQL Server
Now that we’ve covered the basics of windowed functions, let’s take a look at how they can be used in SQL Server. In this section, we’ll cover some common use cases for windowed functions in SQL Server.
Calculating Running Totals
One common use case for windowed functions is to calculate running totals. A running total is the cumulative sum of a specific column up to the current row within the window.
To calculate a running total in SQL Server, you can use the SUM() windowed aggregate function with the ORDER BY clause to define the order of the rows within the window.
Example:
Calculate the running total of sales for each region:
SELECT region, date, sales,SUM(sales) OVER (PARTITION BY regionORDER BY date) as running_totalFROM sales_dataCalculating Moving Averages
Another common use case for windowed functions is to calculate moving averages. A moving average is the average value of a specific column over a sliding window of rows within the dataset.
To calculate a moving average in SQL Server, you can use the AVG() windowed aggregate function with the ROWS or RANGE clause to define the size of the sliding window.
Example:
Calculate the 7-day moving average of sales:
SELECT date, sales,AVG(sales) OVER (ORDER BY dateROWS BETWEEN 6 PRECEDINGAND CURRENT ROW) as moving_averageFROM sales_dataCalculating Rank Values
Windowed ranking functions can be used to assign a rank or order to a subset of data based on specific criteria. This is useful when you need to identify the top or bottom values within a dataset.
To calculate rank values in SQL Server, you can use the RANK(), DENSE_RANK() or ROW_NUMBER() windowed ranking functions with the ORDER BY clause to define the order of the rows within the window.
Example:
Rank the top 10 sales by region:
WITH ranked_sales AS (SELECT region, date, sales,RANK() OVER (PARTITION BY regionORDER BY sales DESC) as rankFROM sales_data)SELECT region, date, salesFROM ranked_sales WHERE rank <= 10FAQ
What is the difference between a windowed function and an aggregate function?
Windowed functions allow you to perform calculations on a specific window or subset of data, rather than the entire dataset. Windowed functions are used with the OVER clause, which defines the window or subset of data on which the function is performed. Aggregate functions, on the other hand, perform calculations on the entire dataset.
What are some common use cases for windowed functions?
Windowed functions are useful when you need to perform calculations on a specific subset of data, rather than the entire dataset. This can be helpful when you need to calculate statistics such as running totals, moving averages or rank values based on specific criteria. Windowed functions can also be used to return additional information alongside the original dataset.
What types of windowed functions are supported in SQL Server?
SQL Server supports a range of windowed functions, including aggregate functions, ranking functions and offset functions. Commonly used windowed aggregate functions include SUM, AVG, COUNT, MAX and MIN. Commonly used windowed ranking functions include RANK, DENSE_RANK and ROW_NUMBER. Commonly used windowed offset functions include LAG, LEAD and FIRST_VALUE/LAST_VALUE.
Conclusion
Windowed functions are a powerful feature in SQL Server that allow you to perform calculations on a specific window or subset of data, rather than the entire dataset. Windowed functions are distinct from aggregate functions and can be used for a range of purposes, including calculating running totals, moving averages and rank values. By mastering windowed functions, you can improve the efficiency and accuracy of your SQL queries and gain deeper insights into your data.