Rank Function in SQL

Introduction

Imagine you have a list of employees of your company’s sales department and you have to assign the best salespersons. Again, since there are thousands of transactions and numerous factors to consider, the task of sorting and ranking the data through traditional simple methods is a hectic. Gather ranking functions of SQL which are intelligent methods of ranking your database contents conveniently. Besides, the functions provided can not only help you simplify the rank operation while making decisions but also help you derive useful information for your business. Now, let’s proceed to the analysis of what ranking in SQL is, how it operates, when it may be used, and why.

Learning Outcomes

• Understand the concept of ranking in SQL and its importance.
• Learn about the different ranking functions available in SQL.
• Discover practical examples of how to use ranking functions.
• Explore the advantages and potential pitfalls of using ranking functions in SQL.
• Gain insights into best practices for effectively utilizing ranking functions in SQL.

Understanding Ranking in SQL

Ranking in SQL is a technique for assigning a rank to each row in the result set as per some selected column. This is very helpful especially in ordered data like in ranking the salesman performance, arrangement in scores, or the products by their demand. There are several ranking functions constructed in SQL; they are RANK(), DENSE_RANK(), ROW_NUMBER(), and NTILE().

Ranking Functions in SQL

Let us now explore ranking functions in SQL:

RANK()

• Assigns a unique rank number to each distinct row within a partition.
• Rows with equal values receive the same rank, with gaps in the ranking sequence.
• Example: If two rows share the same rank of 1, the next rank assigned will be 3.

DENSE_RANK()

• Similar to RANK(), but without gaps in the ranking sequence.
• Rows with equal values receive the same rank, but the subsequent rank follows immediately.
• Example: If two rows share the same rank of 1, the next rank assigned will be 2.

ROW_NUMBER()

• Assigns a unique sequential integer to each row within a partition.
• Each row receives a different rank, regardless of the values in the column.
• Useful for generating unique row identifiers.

NTILE()

• Distributes rows into a specified number of approximately equal-sized groups.
• Each row is assigned a group number from 1 to the specified number of groups.
• Useful for dividing data into quartiles or percentiles.

Practical Examples

Below we will discuss some practical examples of rank function.

Dataset

CREATE TABLE Employees (
    EmployeeID INT,
    Name VARCHAR(50),
    Department VARCHAR(50),
    Salary DECIMAL(10, 2)
);

INSERT INTO Employees (EmployeeID, Name, Department, Salary) VALUES
(1, 'John Doe', 'HR', 50000),
(2, 'Jane Smith', 'Finance', 60000),
(3, 'Sam Brown', 'Finance', 55000),
(4, 'Emily Davis', 'HR', 52000),
(5, 'Michael Johnson', 'IT', 75000),
(6, 'Sarah Wilson', 'IT', 72000);

Using RANK() to Rank Sales Representatives

This function assigns a rank to each row within a partition of the result set. The rank of rows with equal values is the same, with gaps in the ranking numbers if there are ties.

SELECT 
    EmployeeID,
    Name,
    Department,
    Salary,
    RANK() OVER (ORDER BY Salary DESC) AS Rank
FROM Employees;

Output:

Using DENSE_RANK() to Rank Students by Test Scores

Similar to RANK(), but without gaps in the ranking numbers. Rows with equal values receive the same rank, and subsequent ranks are consecutive integers.

SELECT 
    EmployeeID,
    Name,
    Department,
    Salary,
    DENSE_RANK() OVER (ORDER BY Salary DESC) AS DenseRank
FROM Employees;

Output:

Using ROW_NUMBER() to Assign Unique Identifiers

Assigns a unique sequential integer to rows, starting from 1. There are no gaps, even if there are ties.

SELECT 
    EmployeeID,
    Name,
    Department,
    Salary,
    ROW_NUMBER() OVER (ORDER BY Salary DESC) AS RowNumber
FROM Employees;

Output:

Using NTILE() to Divide Employees into Quartiles

Using NTILE() is useful for statistical analysis and reporting when you need to segment data into quantifiable parts, making it easier to analyze and interpret distributions and trends.

SELECT 
    EmployeeID,
    Name,
    Department,
    Salary,
    NTILE(3) OVER (ORDER BY Salary DESC) AS Quartile
FROM Employees;

Output:

This divides the result set into 3 approximately equal parts based on the Salary in descending order. Each employee is assigned a Quartile number indicating their position within the salary distribution.

Advantages of Ranking Functions

• Simplifies complex ranking and ordering tasks.
• Enhances the ability to generate meaningful insights from ordered data.
• Reduces the need for manual data sorting and ranking.
• Facilitates data segmentation and grouping.

Potential Pitfalls

• Performance issues with large datasets due to sorting and partitioning.
• Misunderstanding the differences between RANK(), DENSE_RANK(), and ROW_NUMBER() can lead to incorrect results.
• Overhead associated with calculating ranks in real-time queries.

Best Practices

• Use appropriate ranking functions based on the specific requirements of your query.
• Consider indexing columns used in ranking functions to improve performance.
• Test and optimize queries with ranking functions on large datasets to ensure efficiency.

Conclusion

Ranking functions in SQL are a set of crucial tools that are applied to deal with ordered data. No matter you are sorting the sales representatives, test scores, or want to divide data into quartiles, these functions help and give more information in an easier way. Hence, learning the differences between RANK(), DENSE_RANK(), ROW_NUMBER(), and NTILE() and applying best practices, you gain more control over ranking functions and can further boost data and information analysis.

Also read: Top 10 SQL Projects for Data Analysis

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Ayushi Trivedi

My name is Ayushi Trivedi. I am a B. Tech graduate. I have 3 years of experience working as an educator and content editor. I have worked with various python libraries, like numpy, pandas, seaborn, matplotlib, scikit, imblearn, linear regression and many more. I am also an author. My first book named #turning25 has been published and is available on amazon and flipkart. Here, I am technical content editor at Analytics Vidhya. I feel proud and happy to be AVian. I have a great team to work with. I love building the bridge between the technology and the learner.