Decorative image on SQL window functions.

SQL Window Functions – Complete Guide for Beginners

   April 9, 2025  5 Comments on SQL Window Functions – Complete Guide for Beginners  Posted in SQL

SQL Window Functions is an extremely important topic when we start learning SQL. Basic SQL queries like `SELECT`, `WHERE`, `GROUP BY`, and `JOIN` are most likely familiar to you if you have been studying SQL for some time. These abilities are crucial, but as we tackle increasingly challenging real world data issues, we will quickly find that we need to analyze data across rows without collapsing / merging them.

SQL Window Functions are useful in this situation. Despite their immense power, they can initially appear frightening. But don’t worry, this guide will walk you through every step in an easy-to-understand manner with lots of examples and helpful advice.

Then, we will discuss with practical examples what window functions are, why they are useful, how to use them, and how they differ from other SQL functions by the end of this article.

Let’s start exploring SQL Window Functions together!

What Are SQL Window Functions?

SQL Window functions carry out computations on a collection of rows that are connected to the current row in some way. We refer to these rows as a window.

Thus, the most important thing to keep in mind is:

  • Window functions do not collapse rows. This is in contrast to aggregate functions such as SUM() or COUNT() with GROUP BY.
  • They enable you to carry out group-based computations while maintaining individual rows.

🔍 Think of it like this:

Window functions allow us to look at a “window” around each row while keeping each row visible / intact in the output rather than grouping rows together as one single summary row.

Why Are SQL Window Functions Useful?

SQL Window functions help us by unlocking the ability to:

  • Give each row in a group a rank (for example, Top 5 sellers by region).
  • Compute cumulative sums or running totals.
  • Find out moving averages.
  • Retrieve values from the preceding or subsequent rows.
  • Compare the values of one row with those of another row.
  • Divide data into percentiles or buckets.

Thus, in real world job scenarios, SQL window functions are crucial when we want to:

  • Create compelling dashboards

  • Prepare impact financial reports

  • Running cohort analysis

  • Calculating growth metrics

  • Finding out anomalies in datasets

Basic Syntax of SQL Window Functions

Now, have a look at the basic structure of a window function:

SELECT column1, column2, window_function(column3) OVER (PARTITION BY column4 ORDER BY column5) FROM your_table;

Explanation:

  • window_function: This indicates the calculation we want to do (e.g., SUM(), ROW_NUMBER(), RANK())

  • OVER: This is a signal that we are using a window function

  • PARTITION BY (optional): This is used to divide rows into smaller groups (like departments, regions, etc.)

  • ORDER BY (optional): Tough this is optional part but it allows us to order rows inside each partition to calculate things like running totals or rankings

How SQL Window Functions Differ From GROUP BY

Below table captures a quick summary of key difference between the two –

Feature GROUP BY Window Functions
Result Aggregates data, one row per group Keeps all individual rows
Use Case Simple summaries Detailed analysis (rank, running total, lagging data)
Example Total sales per region Running total of sales per salesperson

Bottom line: Thus, we should use GROUP BY when we are trying to reduce number of rows. And also we should use window functions when we are looking to analyze rows without reducing them.

Most Common SQL Window Functions (With Examples)

Now let’s discuss the most popular window functions with easy to understad real world examples.

1. ROW_NUMBER()

This is used to assign a unique sequential number to each row within a partition and this numbering starts at 1.

SELECT employee_name, department, salary, ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS row_num FROM employees;

✅ Use case: We can thus locate the highest-paid employee in each department with this function.

2. RANK() and DENSE_RANK()

Both of these functions provide ranks, however, they handle ties differently as below:

  • RANK() will skip rankings when it comes across a tie.

  • DENSE_RANK() does not skip rankings  in case of tie.

 
SELECT employee_name, department, salary, RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS salary_rank FROM employees;

✅ Use case: Find out the top 3 earners in each department, even if their salaries are tied.

3. SUM(), AVG(), MIN(), MAX() with OVER()

We can use aggregate functions over a window to compute running totals, averages, minimums, or maximums.

Example: Running total of sales

SELECT salesperson, sales_date, amount, SUM(amount) OVER (PARTITION BY salesperson ORDER BY sales_date) AS running_total FROM sales;

✅ Use case: With this query, we will hence be able to understand how a salesperson’s total sales grows over time.

4. LAG() and LEAD()

  • LAG() is used to fetch data from a previous row.

  • LEAD() is used to fetch data from a future row.

 
SELECT salesperson, sales_date, amount, LAG(amount) OVER (PARTITION BY salesperson ORDER BY sales_date) AS previous_sale, LEAD(amount) OVER (PARTITION BY salesperson ORDER BY sales_date) AS next_sale FROM sales;

✅ Use case: This SQL window function query will thus enable us to calculate the difference between a salesperson’s current sale and their previous sale.

5. NTILE()

This is often used to divides rows into equal groups (tiles). Have a look at the blow query –

SELECT employee_name, salary, NTILE(4) OVER (ORDER BY salary DESC) AS salary_quartile FROM employees;

✅ Use case: Prepare salary quartiles (top 25%, next 25% etc) categorizations of employees’ salary.

Understanding PARTITION BY and ORDER BY

We use these two keywords inside OVER() part to control how the window function is behaving.

PARTITION BY

  • Is used to break data into groups.

  • Important to note that calculations do restart for each partition we use.

Example:

SUM(sales_amount) OVER (PARTITION BY region)

Hence, each region’s sales will be summed up separately.

ORDER BY

  • Is helpful in defining the order of rows within each partition.

  • Order by is cruicial for ranking, running totals, lag/lead.

Example:

ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC)

This query will thus rank employees within each department based on who has highest salary.

Real-Life Practical Examples

Hope the above tutorial gave you an understanding of SQL window functions. Now let’s practice with examples.

🔹 Business Question 1:
Find out the top 3 performing salespeople in terms of monthly sales each month?

Solution:

SELECT salesperson, month, amount, RANK() OVER (PARTITION BY month ORDER BY amount DESC) AS monthly_rank FROM sales;

This will sort salespeople based on their monthly sales. Additionally, we can then filter by monthly_rank <= 3 to get the top 3.

🔹 Business Question 2:
How does each sale compare to the previous one?

Solution:

SELECT salesperson, sales_date, amount, LAG(amount) OVER (PARTITION BY salesperson ORDER BY sales_date) AS previous_sale, amount - LAG(amount) OVER (PARTITION BY salesperson ORDER BY sales_date) AS difference FROM sales;

Hence, this will help us in identifying big sales jumps or drops. Leaders often ask for this data to analyze the reasons and come up with solutions.

🔹 Business Question 3:
Show cumulative expenses by employee.

Solution:

SELECT employee_id, expense_date, expense_amount, SUM(expense_amount) OVER (PARTITION BY employee_id ORDER BY expense_date) AS cumulative_expense FROM expenses;
 
Let’s understand this part by part. This query first retrieved the expense details of each employee. It then calculated a running total of their expenses over time. Then, the SUM() OVER (PARTITION BY employee_id ORDER BY expense_date) part computed a cumulative sum of expense_amount, by restarting the total for each employee_id and adding up expenses in the order of expense_date.

Common Mistakes Beginners Make

Here are some common mistakes we often make during our early days in SQL –

  • ❌ Forgetting ORDER BY inside OVER(): Some functions will not work adequately without it. For instance -like ROW_NUMBER().

  • ❌ Not using PARTITION BY when needed: We may by mistake end up calculating across the entire table.

  • ❌ Confusing RANK() and ROW_NUMBER(): We should always remember that RANK() handles ties in a different manner.

  • ❌ Performance issues on big datasets: Window functions can often be heavy, particularly when we have millions of rows. Thus, always test carefully!

Best Practices

  • Always start simple: Start with using one window function and then we can combine multiple.

  • Use meaningful partitions: We should think about how we group our data (region, department, month) in a logical manner.

  • Be careful with NULLs: If no previous/next row exists, functions like LAG() and LEAD() will return NULL .

  • Finally, learn to read the query backward: Understand what each part of OVER() is doing in the query.

Practice Questions (Try Yourself!)

  1. Create a query to give each customer order a row number, arranged by the date of purchase.

  2. Then, determine the top two products each month based on revenue.

  3. Then, determine the total number of people who visit a website every day.

  4. Now, for every region, compare the sales from the previous month to the sales from this month.

  5. Finally, based on their overall purchases, divide your clientele into five spending tiers.

✍️ Pro Tip: Try to solve these questions on a sample dataset like sales, orders, or employees.

Conclusion

Evidently, among SQL’s most potent and adaptable capabilities are SQL window functions. They let you perform intricate computations without sacrificing the specifics of individual rows. They may appear challenging at first, but you will become accustomed to them with practice.

Learn the fundamentals first, such as ROW_NUMBER(), SUM(), OVER(), and LAG(), then work your way up to more complex ones, such as RANK(), DENSE_RANK(), and NTILE().

Hence, you will write SQL queries that are clearer, faster, and much more powerful after you become proficient with window functions. 🚀

Happy Learning! Now keep practicing and mastering your SQL skills. 📚✨

Also check out our other articles – 

  1. Understanding SQL Indexes: Clustered vs Non Clustered
  2. Stored Procedures vs Functions in SQL : Understanding the Differences
  3. SQL – Common Interview Questions and Answers

Finally, learn more about SQL Window Functions here – 

SELECT – Window Clause (Transact-SQL)

Window Function Concepts and Syntax

Author: admin

5 thoughts on “SQL Window Functions – Complete Guide for Beginners”

  4. Pingback: Pivot Tables: Step-by-Step Guide for Beginners - CodeAndExcel
  5. Pingback: VBA Macros in Excel: Automate Tasks (A Beginner's Guide) - CodeAndExcel

Leave a Reply

Your email address will not be published. Required fields are marked *