Sunday, March 3, 2024

SQL Window Functions - How to Calculate Running Total || Accumulative Sum

In this tutorial, you are going to learn "How to Calculate Running Total Or Accumulative Sum in SQL" by using SQL Window Functions.

SQL window functions are a powerful feature that allows you to perform calculations across a set of rows related to the current row, without collapsing the result set. These functions operate on a "window" of rows, which is defined by a specific partition of the data and an optional order.

Window functions are commonly used for analytical and reporting tasks. Window functions have a similar syntax to regular aggregate functions, but they include an additional OVER clause that defines the window specification.
SELECT EmpId, EmpName,Department,Salary, Sum(Salary) over (Order by EmpId) as RunningSalary, Sum(Salary) over (Partition By Department Order by EmpId) as DeptRunningSalary FROM [dbo].[EmployeeDeptMaster]

Common Use Cases:

  • Calculating running totals, averages, or other aggregates over a moving window.
  • Ranking and ordering rows based on certain criteria within partitions.
  • Finding trends or patterns in data over time.
  • Performing complex analytical calculations without using subqueries or self-joins.

Commonly Used Functions: Window functions include aggregate functions like SUM(), AVG(), MIN(), MAX(), as well as ranking functions like ROW_NUMBER(), RANK(), DENSE_RANK(), and analytical functions like LEAD(), LAG(), FIRST_VALUE(), LAST_VALUE(), etc.

Performance Considerations: While window functions can be very powerful, they can also have performance implications, especially on large datasets. Proper indexing and understanding of how the windowing functions operate internally can help optimize performance.

Database Support: Window functions are supported in many modern SQL databases including PostgreSQL, Oracle, SQL Server, MySQL (starting from version 8.0), and others. However, the syntax and available functions may vary slightly between different database systems.
Please watch our demo video at YouTube-

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