DbSchema | SQL Server - How to Use Window Functions?

SQL Server: How to Use Window Functions in sqlcmd and DbSchema

Table of Contents

Introduction
Prerequisites
What are Window Functions?
Purpose of Using Window Functions
Restrictions and Permissions
Advantages and Limitations
Types of Window Functions
How to Use Window Functions in sqlcmd and DbSchema
Conclusion
References

1. Introduction

The SQL Server provides a suite of powerful tools and features to handle and manipulate data. Among these, SQL Server window functions stand out as essential tools that enable you to perform data analysis and calculations that were difficult or impossible with traditional SQL. This article will guide you through understanding and utilizing these functions, focusing on their use in sqlcmd and DbSchema.

2. Prerequisites

Before starting, ensure you have the following:

Basic knowledge of SQL commands and syntax
A working SQL Server instance
Installed sqlcmd utility and DbSchema

For installation and establishing connection you can read our article SQL Server-How to create a database?

3. What are Window Functions?

SQL Server window functions operate on a set of rows, known as the window, and return a single value for each row from the underlying query. The window specification defines the window in terms of rows relative to the current row and allows partitioning of the result set into groups or partitions.

4. Purpose of Using Window Functions

Window functions provide a way to perform calculations across a set of rows that are related to the current row. This allows for complex analyses such as calculating running totals, averages, or percentages, which would be challenging to perform using standard SQL queries.

5. Restrictions and Permissions

To execute a query that involves window functions, you need SELECT permissions on the target table. The restrictions include:

Window functions can’t be used in a WHERE clause.
The OVER clause can’t be used in a GROUP BY clause.
The window function and window order clause can’t contain alias column names.

6. Advantages and Limitations

Advantages:

Advantages of window functions include:

Simplified queries: Can replace complex subqueries and self-joins.
Improved performance: Less computing resource consumption compared to traditional methods.
Enhanced data analysis: Allow advanced calculations on sets of rows.

Limitations:

Limitations of window functions include:

Can’t be used in WHERE, GROUP BY, or HAVING clauses.
Can’t be nested.
Don’t support the use of window functions in the window order clause.

7. Types of Window Functions

i. Aggregate Window Functions

These are standard SQL aggregate functions but with an OVER clause to operate on a window of rows. Examples include SUM, COUNT, AVG, MIN, and MAX.

ii. Ranking Window Functions

These provide a ranking value to each row in a window, like RANK, DENSE_RANK, ROW_NUMBER, and NTILE.

iii. Value Window Functions

These provide access to data from another row in the same window without the need for a self-join. These include LAG, LEAD, FIRST_VALUE, and LAST_VALUE.

8. How to Use Window Functions in sqlcmd and DbSchema

Let’s take a sample employees table with the following data for illustration:

emp_id	dept_id	salary	hire_date
1	10	4000	2020-01-10
2	10	6000	2020-06-20
3	20	5000	2020-02-15
4	20	7000	2020-08-30
5	30	5500	2020-03-25
6	30	6500	2020-10-05

sqlcmd

Step 1: Open the sqlcmd utility in your command line.

Step 2: Connect to your database:

sqlcmd -S <server_name> -d <database_name> -U <username> -P <password>

Replace ServerName, UserName, Password, and DatabaseName with your actual SQL Server details.

Let’s work with different window functions on this table.

Aggregate Window Function (SUM)

We calculate the cumulative salary for each department (dept_id).

SELECT 
  emp_id, 
  dept_id, 
  salary,
  SUM(salary) OVER (PARTITION BY dept_id ORDER BY hire_date 
                    ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) 
                    AS cumulative_salary
FROM employees;

Results From the Query:

Following result will be obtained after we run the above query on our sample database table:

emp_id	dept_id	salary	cumulative_salary
1	10	4000	4000
2	10	6000	10000
3	20	5000	5000
4	20	7000	12000
5	30	5500	5500
6	30	6500	12000

Ranking Window Function (RANK)

We rank employees within each department based on salary.

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

Results From the Query:

Following result will be obtained after we run the above query on our sample database table:

emp_id	dept_id	salary	salary_rank
1	10	4000	2
2	10	6000	1
3	20	5000	2
4	20	7000	1
5	30	5500	2
6	30	6500	1

Value Window Function (LAG)

We get the salary of the previously hired employee within each department.

SELECT 
  emp_id, 
  dept_id, 
  salary,
  LAG(salary) OVER (PARTITION BY dept_id ORDER BY hire_date) 
  AS prev_salary
FROM employees;

Results From the Query:

Following result will be obtained after we run the above query on our sample database table:

emp_id	dept_id	salary	prev_salary
1	10	4000	NULL
2	10	6000	4000
3	20	5000	NULL
4	20	7000	5000
5	30	5500	NULL
6	30	6500	5500

DbSchema

DbSchema is a user-friendly environment for working with databases, and it has SQL Editor that allows you to write and execute SQL queries. Let’s dive into a step-by-step guide on how to use window functions within DbSchema.

Before we start, let’s consider a sales table in our database, which has the following records:

sale_id	product_id	sale_date	sale_amount
1	101	2023-01-15	500
2	102	2023-01-20	400
3	101	2023-02-01	450
4	103	2023-02-10	300
5	102	2023-03-05	500
6	101	2023-03-20	700
7	103	2023-04-05	350
8	102	2023-04-15	600
9	101	2023-05-10	800
10	103	2023-05-20	400

1. Aggregate Window Function

Let’s calculate the running total of sale_amount for each product_id ordered by sale_date.

Open DbSchema and connect to your database.
Navigate to SQL Editor and open a new window.
Input the following query:

SELECT 
  sale_id, 
  product_id, 
  sale_date,
  sale_amount, 
  SUM(sale_amount) OVER (PARTITION BY product_id ORDER BY sale_date 
                         ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) 
                         AS running_total
FROM sales;

Click the Execute button. You will see the results displayed in tabular format below the SQL editor.

Results From the Query:

Following result will be obtained after we run the above query on our sample database table:

The result of the running total of sale_amount for each product_id ordered by sale_date:

sale_id	product_id	sale_date	sale_amount	running_total
1	101	2023-01-15	500	500
3	101	2023-02-01	450	950
6	101	2023-03-20	700	1650
9	101	2023-05-10	800	2450
2	102	2023-01-20	400	400
5	102	2023-03-05	500	900
8	102	2023-04-15	600	1500
4	103	2023-02-10	300	300
7	103	2023-04-05	350	650
10	103	2023-05-20	400	1050

2. Ranking Window Function

Now let’s rank sales records within each product_id based on sale_amount.

In the SQL Editor, type in the following:

SELECT 
  sale_id, 
  product_id, 
  sale_date,
  sale_amount, 
  RANK() OVER (PARTITION BY product_id ORDER BY sale_amount DESC) 
  AS sale_rank
FROM sales;

Click the Execute button. You will see the results in the output panel.

Results From the Query:

Following result will be obtained after we run the above query on our sample database table:

The ranking of sales records within each product_id based on sale_amount:

sale_id	product_id	sale_date	sale_amount	sale_rank
1	101	2023-01-15	500	3
3	101	2023-02-01	450	4
6	101	2023-03-20	700	2
9	101	2023-05-10	800	1
2	102	2023-01-20	400	3
5	102	2023-03-05	500	2
8	102	2023-04-15	600	1
4	103	2023-02-10	300	3
7	103	2023-04-05	350	2
10	103	2023-05-20	400	1

3. Value Window Function

Let’s get the sale_amount of the previous sale within each product_id.

In the SQL Editor, write the following:

SELECT 
  sale_id, 
  product_id, 
  sale_date,
  sale_amount, 
  LAG(sale_amount) OVER (PARTITION BY product_id ORDER BY sale_date) 
  AS previous_sale_amount
FROM sales;

Click the Execute button. The output panel will display the result of this query.

Results From the Query:

Following result will be obtained after we run the above query on our sample database table:

The sale_amount of the previous sale within each product_id:

sale_id	product_id	sale_date	sale_amount	previous_sale_amount
1	101	2023-01-15	500	NULL
3	101	2023-02-01	450	500
6	101	2023-03-20	700	450
9	101	2023-05-10	800	700
2	102	2023-01-20	400	NULL
5	102	2023-03-05	500	400
8	102	2023-04-15	600	500
4	103	2023-02-10	300	NULL
7	103	2023-04-05	350	300
10	103	2023-05-20	400	350

Please note that in the case of the LAG function, we have NULL values whenever there isn’t a preceding row within the partition

Note: Please remember to replace the table name, column names, and partition details according to your specific database schema. Window functions are a powerful tool in SQL Server and can provide insights into your data in ways that other methods cannot.

Visually Manage SQL Server using DbSchema

DbSchema is a SQL Server client and visual designer. DbSchema has a free Community Edition, which can be downloaded here.

Key Features of DbSchema:

Following are the key features of DbSchema which distinguish it from other database GUI tools.

9. Conclusion

SQL Server window functions are a powerful tool for data analysis. By learning to use them in sqlcmd and DbSchema, you can perform complex queries and calculations with relative ease. Ensure you understand the advantages, limitations, and types of window functions to make the most of these tools.