Use SQL Ranking Functions Like a PRO: Don't be a Rookie

Welcome to our comprehensive guide on SQL Ranking Functions! In this tutorial, we aim to unlock the mysteries surrounding these powerful tools in SQL, which are essential for managing and analyzing data efficiently. We'll start by exploring what SQL Ranking Functions are and why they are indispensable in data manipulation. Throughout this guide, we’ll delve deep into various ranking functions like RANK(), DENSE_RANK(), ROW_NUMBER(), and many more, uncovering their syntax, usage, and special characteristics.

We will navigate through practical examples, showcasing how these functions operate and where they are most effectively applied, such as in creating top-N queries, managing duplicates, and handling partitions. Plus, we’ll equip you with troubleshooting strategies and performance optimization tips to tackle common challenges effortlessly. So, buckle up and get ready to embark on an enlightening journey through SQL Ranking Functions, curated to offer clarity and confidence in utilizing these functions to elevate your data handling capabilities.

 

Sample Tables for Examples

Below are some sample tables which we will use throughout the article for demonstration of different SQL Ranking Functions:

Table-1: Employees

CREATE TABLE Employees (
    EmployeeID INT PRIMARY KEY,
    FirstName VARCHAR(50),
    LastName VARCHAR(50),
    Department VARCHAR(50),
    Salary INT
);

INSERT INTO Employees (EmployeeID, FirstName, LastName, Department, Salary) VALUES
(1, 'Aarav', 'Sharma', 'HR', 50000),
(2, 'Vihaan', 'Patel', 'IT', 60000),
(3, 'Advait', 'Deshmukh', 'Finance', 55000),
(4, 'Ishaan', 'Gupta', 'Marketing', 52000),
(5, 'Saanvi', 'Verma', 'IT', 62000),
(6, 'Parv', 'Singh', 'HR', 51000),
(7, 'Anaya', 'Kumar', 'Marketing', 54000);

Table-2: StudentGrades

CREATE TABLE StudentGrades (
    StudentID INT PRIMARY KEY,
    FirstName VARCHAR(50),
    LastName VARCHAR(50),
    Subject VARCHAR(50),
    Marks INT
);

INSERT INTO StudentGrades (StudentID, FirstName, LastName, Subject, Marks) VALUES
(1, 'Dhruv', 'Raj', 'Math', 85),
(2, 'Prisha', 'Saxena', 'Math', 90),
(3, 'Aaryan', 'Menon', 'Math', 80),
(4, 'Riya', 'Shah', 'Math', 90),
(5, 'Ved', 'Nair', 'Math', 82);

Table-3: OrderDetails

CREATE TABLE OrderDetails (
    OrderID INT PRIMARY KEY,
    CustomerName VARCHAR(50),
    Product VARCHAR(50),
    Quantity INT,
    OrderDate DATE
);

INSERT INTO OrderDetails (OrderID, CustomerName, Product, Quantity, OrderDate) VALUES
(1, 'Nikhil', 'Mobile', 2, '2023-01-15'),
(2, 'Manvi', 'Laptop', 1, '2023-02-20'),
(3, 'Kavya', 'Tablet', 3, '2023-03-05'),
(4, 'Yash', 'Mobile', 1, '2023-01-18'),
(5, 'Tanvi', 'Laptop', 1, '2023-02-25');

 

List of different RANKING Functions in SQL

• RANK(): Assigns a unique rank to rows within a result set based on specified column values, leaving gaps in rank numbers in case of identical values.
• DENSE_RANK(): Similar to RANK(), but assigns ranks without leaving gaps, ensuring consecutive rank numbers even when values are identical.
• ROW_NUMBER(): Assigns a unique sequential integer to rows within a result set based on the order specified, without considering the uniqueness of the values.
• NTILE(n): Distributes the rows in an ordered partition into a specified number of roughly equal parts, assigning a unique bucket number to each row.
• FIRST_VALUE(): Returns the first value in an ordered set of values, based on specified criteria, making it accessible for each row in the result set.
• LAST_VALUE(): Returns the last value in an ordered set of values, based on specified criteria, making it accessible for each row in the result set.
• CUME_DIST(): Calculates the cumulative distribution of a value within a result set, providing a relative rank of the values.
• PERCENT_RANK(): Computes the relative rank of each row

 

1. RANK()

The RANK() function is used to assign a unique rank to each row within a result set based on specified column values.

Syntax:

RANK() OVER (
    [PARTITION BY partition_expression, ... ]
    ORDER BY sort_expression [ASC | DESC], ...
)

• PARTITION BY partition_expression: This is optional. It divides the result set into partitions and a rank is assigned within each partition.
• ORDER BY sort_expression: It specifies the order in which the ranks will be assigned.

Example Using Employees Table: Ranking Employees based on Salary

SELECT FirstName, Department, Salary, 
       RANK() OVER (ORDER BY Salary DESC) as Rank
FROM Employees;

In this example, each employee gets a rank based on their salary. If two employees have the same salary, they get the same rank, and the next rank is skipped.

Use Case in StudentGrades: Ranking Students based on Marks

SELECT FirstName, Subject, Marks,
       RANK() OVER (PARTITION BY Subject ORDER BY Marks DESC) as Rank
FROM StudentGrades;

This assigns ranks to students within each subject based on their marks, helping in understanding the standing of each student in each subject.

 

2. DENSE_RANK()

DENSE_RANK() assigns a rank to each unique value in a result set, but unlike RANK(), it does not leave gaps between ranks when there are duplicate values.

Syntax:

DENSE_RANK() OVER (
    [PARTITION BY partition_expression, ... ]
    ORDER BY sort_expression [ASC | DESC], ...
)

Example Using Employees Table: Dense Ranking Employees based on Salary

SELECT FirstName, Department, Salary, 
       DENSE_RANK() OVER (ORDER BY Salary DESC) as Dense_Rank
FROM Employees;

This query ranks employees based on salary without leaving gaps in ranks when salaries are identical.

Use Case in StudentGrades: Dense Ranking Students based on Marks

SELECT FirstName, Subject, Marks,
       DENSE_RANK() OVER (PARTITION BY Subject ORDER BY Marks DESC) as Dense_Rank
FROM StudentGrades;

Students are ranked within each subject, providing a clear comparative rank without gaps, even when students have identical marks.

 

3. ROW_NUMBER()

ROW_NUMBER() assigns a unique sequential number to rows within a result set based on a specified order, even when values are duplicated.

Syntax:

ROW_NUMBER() OVER (
    [PARTITION BY partition_expression, ... ]
    ORDER BY sort_expression [ASC | DESC], ...
)

Example Using OrderDetails Table: Assigning Row Numbers to Orders

SELECT OrderID, CustomerName, Product, OrderDate,
       ROW_NUMBER() OVER (ORDER BY OrderDate) as Row
FROM OrderDetails;

This assigns a unique row number to each order based on the order date, helping in sequential data analysis.

Use Case in Employees: Assigning Row Numbers to Employees in Each Department

SELECT FirstName, Department, Salary,
       ROW_NUMBER() OVER (PARTITION BY Department ORDER BY Salary DESC) as Row
FROM Employees;

This assigns row numbers to employees within each department based on their salaries, offering a way to uniquely identify employees even when salaries are identical.

 

4. NTILE(n)

NTILE(n) function distributes the rows of an ordered partition into a specified number of approximately equal parts, or "buckets", assigning a unique bucket number to each row.

Syntax:

NTILE(number_of_buckets) OVER (
    [PARTITION BY partition_expression, ... ]
    ORDER BY sort_expression [ASC | DESC], ...
)

number_of_buckets specifies the number of buckets the data should be divided into.

Example Using StudentGrades Table: Dividing Students into Percentiles

SELECT FirstName, Marks,
       NTILE(4) OVER (ORDER BY Marks) as Quartile
FROM StudentGrades;

This assigns each student to a quartile based on their marks, allowing for easy identification of performance categories.

 

5. FIRST_VALUE() and LAST_VALUE()

FIRST_VALUE() and LAST_VALUE() functions return the first and last values respectively from an ordered sequence of values in each partition.

Syntax:

FIRST_VALUE(expression) OVER (
    [PARTITION BY partition_expression, ... ]
    ORDER BY sort_expression [ASC | DESC], ...
)
LAST_VALUE(expression) OVER (
    [PARTITION BY partition_expression, ... ]
    ORDER BY sort_expression [ASC | DESC], ...
)

Example Using OrderDetails Table: Finding the First and Last Order Dates

SELECT OrderID, Product, OrderDate,
       FIRST_VALUE(OrderDate) OVER (ORDER BY OrderDate) as FirstOrderDate,
       LAST_VALUE(OrderDate) OVER (ORDER BY OrderDate) as LastOrderDate
FROM OrderDetails;

This helps in analyzing the time frame of orders by showing the first and last order dates alongside each order.

 

6. CUME_DIST()

CUME_DIST() calculates the cumulative distribution of a value within an ordered set of values, effectively providing a relative rank.

Syntax:

CUME_DIST() OVER (
    [PARTITION BY partition_expression, ... ]
    ORDER BY sort_expression [ASC | DESC], ...
)

Example Using Employees Table: Finding Cumulative Distribution of Salaries

SELECT FirstName, Salary,
       CUME_DIST() OVER (ORDER BY Salary) as CumulativeDistribution
FROM Employees;

This illustrates where each employee’s salary stands relative to others in a cumulative manner, facilitating relative comparisons.

 

7. PERCENT_RANK()

PERCENT_RANK() computes the relative rank of each row as a percentage within each partition of the result set.

Syntax:

PERCENT_RANK() OVER (
    [PARTITION BY partition_expression, ... ]
    ORDER BY sort_expression [ASC | DESC], ...
)

Example Using StudentGrades Table: Calculating Percentile Ranks

SELECT FirstName, Marks,
       PERCENT_RANK() OVER (ORDER BY Marks) as PercentileRank
FROM StudentGrades;

This gives a percentile rank to students based on their marks, providing insights into each student's relative position in terms of performance.

 

Working with Partitions in Ranking Functions

Partitions in SQL ranking functions allow you to divide the result set into smaller groups or sections, and then apply the ranking functions to each partition separately. Think of partitions as different categories within your data, and within each category, the ranking starts from scratch.

Using partitions involves the PARTITION BY clause in the syntax of the ranking function. When a partition is defined, the ranking function resets for each distinct value in the partition column(s).

Example Using Employee Table with RANK() Function

SELECT FirstName, Department, Salary,
       RANK() OVER (PARTITION BY Department ORDER BY Salary DESC) as Rank
FROM Employees;

In this example, employees are ranked within each department based on their salaries. The ranking starts over for each department.

Example Using NTILE() Function with Partitions

SELECT FirstName, Department, Salary,
       NTILE(3) OVER (PARTITION BY Department ORDER BY Salary) as Tile
FROM Employees;

This will create three tiles within each department, each containing roughly the same number of rows, allowing for department-specific evaluations.

 

Handling Duplicates with Ranking Functions

Different ranking functions manage duplicates in various ways. Functions like RANK() and DENSE_RANK() assign the same rank to duplicate values but handle subsequent ranks differently. RANK() leaves gaps after duplicates, while DENSE_RANK() does not. ROW_NUMBER(), however, assigns a unique rank to each row, even if values are duplicated.

Strategies for Managing Duplicates

• Choosing the Right Function: Select the ranking function that aligns with how you want duplicates to be treated—RANK(), DENSE_RANK(), or ROW_NUMBER().
• Using DISTINCT: Implement the DISTINCT keyword to eliminate duplicate rows, ensuring that each value is unique.
• Applying Additional Sorting Criteria: Introduce more columns in the ORDER BY clause to create a unique sorting order, helping functions like ROW_NUMBER() to assign unique ranks.
• Manipulating Data Before Ranking: Pre-process the data to handle duplicates before applying ranking functions, such as by using aggregation functions.

Example of Managing Duplicates with ROW_NUMBER()

SELECT FirstName, Salary,
       ROW_NUMBER() OVER (ORDER BY Salary) as Row
FROM Employees;

In this example, even if two employees have the same salary, ROW_NUMBER() assigns a unique row number, ensuring each employee has a distinct rank.

 

Comparison of Different SQL Ranking Functions

This table summarizes the main attributes and use cases of each SQL ranking function, providing a comparative overview to guide the choice of function based on specific requirements and preferences.

 

Advanced Topics

1. Combining Ranking Functions

Combining ranking functions means using more than one ranking function in a single query. It can make your query more versatile and can provide deeper insights into the data by offering multiple perspectives in a single view.

Example Using Employees Table:

SELECT FirstName, Department, Salary,
       RANK() OVER (ORDER BY Salary DESC) as Rank,
       DENSE_RANK() OVER (ORDER BY Salary DESC) as Dense_Rank
FROM Employees;

In this example, the query displays the regular rank and dense rank of employees based on their salaries. Combining the functions helps to see the effect of duplicates on the ranking directly.

 

2. Nested Ranking Queries

Nested ranking queries involve embedding one ranking query within another, allowing for multiple layers of ranking and analysis. Nested queries often serve to provide a refined or secondary ranking based on the results of an initial ranking.

Example Using StudentGrades Table:

SELECT FirstName, Subject, Marks, Rank,
       DENSE_RANK() OVER (PARTITION BY Subject ORDER BY Rank) as Dense_Rank
FROM (
    SELECT FirstName, Subject, Marks,
           RANK() OVER (PARTITION BY Subject ORDER BY Marks DESC) as Rank
    FROM StudentGrades
) as InnerQuery;

In this nested query example, students are first ranked within each subject based on marks, and then a dense rank is applied to those ranks, illustrating a layered approach to ranking.

 

Frequently Asked Questions (FAQs)

 

Summary

• SQL ranking functions are powerful tools in data analysis, helping in generating insights based on ordering and ranking.
• They are versatile, allowing for handling of duplicates, partitioning of data, and use in nested queries.
• Different ranking functions have various applications, such as RANK(), DENSE_RANK(), ROW_NUMBER(), NTILE(), and others, each suitable for different scenarios and requirements.
• Advanced utilization includes combining ranking functions, nested ranking queries, and further exploration through various educational resources.

Further Reading Resources

• SQL Server: Microsoft SQL Server Documentation
• MySQL: MySQL 8.0 Reference Manual
• PostgreSQL: PostgreSQL 13.0 Documentation

These official documentations are comprehensive guides that cover the syntax, usage, examples, and advanced concepts of SQL ranking functions, providing a strong foundation for deepening SQL knowledge.