Adventures in Machine Learning

Unlocking the Power of SQL Ranking Functions: A Comprehensive Guide

Introduction to SQL Ranking Functions

Have you ever wanted to know which product is the most popular, or which salesperson is the most successful? Ranking is a fundamental concept that helps us understand how items stack up against each other.

In the world of data and databases, SQL ranking functions are crucial tools that make ranking possible. But what exactly are SQL ranking functions, and how are they used in everyday life?

What are SQL Ranking Functions?

SQL ranking functions are special functions in Structured Query Language (SQL) that allow you to rank data according to particular criteria.

Ranking functions are primarily classified as “window functions,” which means that they operate on a set of rows that are defined by a window or group. This window is defined using a “window frame” that specifies which rows are included in the calculation.

The resulting ranking values are assigned to each row returned by the query.

Ranking Examples in Everyday Life

There are many examples of ranking in our daily lives. For instance, sports leagues rank teams based on their success in games, and universities rank students based on their grades.

In business, sales teams rank products or customers based on sales performance. Social media platforms use ranking algorithms to sort posts by relevance or popularity.

Each of these examples speaks to the importance of ranking and how it helps us understand how different entities compare to each other.

Difficulty of Using SQL Ranking Functions

While ranking sounds straightforward enough, implementing ranking in SQL can be quite complex. Ranking functions involve a lot of logic and require a good understanding of SQL syntax.

Additionally, the ranking function that you select will depend on your specific use case. Choosing the wrong function can result in incorrect or misleading rankings.

SQL Ranking Basics

Now let’s look at the basics of SQL ranking functions. The three most popular SQL ranking functions are RANK(), DENSE_RANK(), and ROW_NUMBER().

Each function is similar in many ways but differs in some key aspects, as explained below.


The RANK() function assigns equal ranking values to items that tie in a specific order. For example, in a sales ranking, if two salespeople sell the same amount, they will both receive the same rank.

The next ranked salesperson will then be assigned a rank that is two ranks higher than the previous rank.


The DENSE_RANK() function works similar to the RANK() function, but it doesn’t skip ranking values.

If two salespeople tie in sales, they both receive the same rank. The next ranked salesperson will get the next consecutive rank, even if this rank would have been assigned to a salesperson already.

In other words, DENSE_RANK() doesn’t create gaps between ranks like RANK() does.


The ROW_NUMBER() function assigns a unique rank to each row in the result set. This function doesn’t take into account if multiple values tie.

Every row will receive a different rank value, even if the values in the ranking column are the same.


In summary, SQL ranking functions are essential tools that enable us to rank data according to specific criteria. Knowing how to use SQL ranking functions is invaluable in businesses that rely on data analysis.

Understanding the differences between the various ranking functions will ensure that you choose the right function for your use case. With this knowledge, you can now start using ranking functions to gain valuable insights into your data.

Comparing SQL Ranking Functions

To better understand the different SQL ranking functions, let’s consider an example where we want to rank students’ exam results based on their grades. We’ll assume that the exam had 50 questions, with a possible score of two points per answer, for a total of 100 points.

Let’s first look at the ranking results for each function using the following sample data:

Student Score
Jane 95
Tom 85
Sarah 90
John 95
Lily 85


Student Score Rank Student Score Rank Student Score Rank
Jane 95 1 Jane 95 1 Jane 95 1
John 95 1 John 95 1 Tom 85 2
Sarah 90 3 Sarah 90 2 Sarah 90 3
Tom 85 4 Lily 85 3 John 95 4
Lily 85 4 Tom 85 3 Lily 85 5

From our example, we can see that the RANK() and DENSE_RANK() functions assign the same ranking value to Jane and John since they both scored 95, while the ROW_NUMBER() function assigns different ranking values to each of them. Additionally, the RANK() function skips a rank in the case of a tie between Tom and Lily, which means that there is no second-ranking value.

In contrast, the DENSE_RANK() function doesn’t create gaps between rankings, so Tom and Lily both rank second.

SQL Ranking Use Cases

Besides ranking exam scores, SQL ranking functions have many other use cases, such as ranking dates, ranking with aggregate functions (such as GROUP BY, COUNT(), and SUM()), showing top results, and ranking by month.

Ranking with Dates

Suppose we want to rank sales for each day in a given period, we can use ranking functions to show which days were the most successful. Here’s an example of ranking using dates:

FROM sales_table
WHERE date BETWEEN '2022-01-01' AND '2022-01-31'

This query will rank the sales for each day in January 2022 and return the date, sales, and DENSE_RANK(). The “ORDER BY sales DESC” clause ranks the sales results in descending order, and the “BETWEEN” clause filters the dates between 1st January and 31st January.

Ranking with Aggregate Functions

Aggregating and ranking with SQL rank functions are best for ranking the total number of sales made by each employee or branch. For example, the following query uses RANK() to rank total sales made by each salesperson:

    SUM(total_sales) AS total_sales,
    RANK() OVER(ORDER BY SUM(total_sales) DESC) AS rank
FROM sales_table
GROUP BY employee_id

This query returns the employee ID, the total sales for each employee, and the ranked position based on the total sales. The “ORDER BY SUM(total_sales) DESC” clause sorts the total sales of employees in descending order, and the “GROUP BY employee_id” groups the results by each employee.

Showing Top Results

SQL rank functions are great for summarizing data and showing top results. For example, this query returns the top three products by the number of sales:

    SUM(quantity_sold) AS total_sales,
    RANK() OVER(ORDER BY SUM(quantity_sold) DESC) AS rank
FROM sales_table
GROUP BY product_name
HAVING rank <= 3
ORDER BY rank;

The HAVING clause ensures that we only see the top three products by setting a condition that the rank should be less than or equal to three.

Ranking by Month

You can also use SQL ranking functions to provide monthly rankings. For example, this query ranks the total sales recorded during each month of the year:

    DATE_TRUNC('month',order_date) AS month,
    SUM(total_sales) AS total_sales,
    DENSE_RANK() OVER(ORDER BY SUM(total_sales) DESC) AS rank
FROM sales_table

The “DATE_TRUNC('month', order_date)” function truncates the order date to the nearest month, and the “GROUP BY” clause groups the data by month. The query then returns a monthly summary of total sales and ranks each month’s sales using DENSE_RANK().


SQL ranking functions are powerful tools that can be used in various use cases, including ranking results based on scores, dates, or total sales. Understanding the different SQL ranking functions and their differences helps you to make the right choice based on the specific needs of your data analysis project.

In conclusion, SQL ranking functions are essential tools for data analysis that allow us to rank data based on specific criteria. The primary SQL ranking functions, including RANK(), DENSE_RANK(), and ROW_NUMBER(), offer different ways of ranking data, depending on the use case.

Knowing how to use these functions is vital for business and data analysis to help gain valuable insights. Proper implementation of ranking in SQL requires a good understanding of SQL syntax and the ranking function suited for the specific requirement.

SQL ranking can help remove ambiguities and give clear perspectives on the results.

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