SQL Rank Interview Questions

Rank functions are frequent hurdles in SQL interview questions because they test not only your ability to write clean queries but also your understanding of advanced data manipulation. Right from the start, you’ll face questions that challenge your grasp of data partitioning, windowing, and the subtleties of rank calculations. It’s no longer about simple SELECT statements; this is where complexity enters.

At its core, ranking functions like RANK(), DENSE_RANK(), ROW_NUMBER(), and NTILE() allow you to assign ranks to rows within a partition of data, but the way you’re expected to handle edge cases in an interview is what makes this topic challenging. If you think you can quickly grasp rank-related interview questions just by reading documentation, think again. These questions are specifically designed to trip you up. The interviewer is not just interested in whether you can write a working query—they want to see if you truly understand the different behaviors of rank functions in complex scenarios.

Take this for example: What happens when two rows have the same rank, but you still need to display a unique identifier for each? Will you use ROW_NUMBER() or RANK() and why?

This is where candidates often get stuck. While ROW_NUMBER() will give each row a unique number, regardless of duplicates in other columns, RANK() will leave gaps in the sequence when there are ties. The same applies to DENSE_RANK(), but without the gaps. Interviewers look for your understanding of these subtle differences.

Key Points Interviewers Test You On:

1. How well do you distinguish between ROW_NUMBER(), RANK(), and DENSE_RANK()?

   • ROW_NUMBER(): Assigns a unique number to each row, no matter if values are tied.
   • RANK(): Assigns the same rank to tied rows but leaves gaps in numbering.
   • DENSE_RANK(): Assigns the same rank to tied rows without leaving gaps.

2. Can you handle partitioning and ordering data efficiently? The PARTITION BY clause is crucial when using ranking functions, as it defines how rows are divided into segments. For example, in a sales dataset, how would you rank employees within each region by their sales figures? You’ll need PARTITION BY region ORDER BY sales DESC. But what happens if sales are tied? This leads us to the next challenge:

3. Ties in rankings and how they impact your result set.
   Handling ties is where the challenge deepens. If you’ve partitioned your data by one field and ordered by another, the ranking functions handle ties differently. RANK() will skip numbers, DENSE_RANK() won’t, and ROW_NUMBER() simply disregards ties, assigning unique numbers. A strong candidate knows this intuitively.

Common Interview Questions on SQL Ranking Functions:

Here’s a look at some specific questions and what interviewers expect:

• "Explain the difference between RANK(), DENSE_RANK(), and ROW_NUMBER()." This is the foundational question that tests your understanding of ranking logic. Make sure you also provide real-world examples of when you’d use each function. For instance, you might use RANK() when you're dealing with a leaderboard where ties exist and skipping ranks makes sense.

• "How would you handle a ranking of salespeople in different departments, ensuring ties are accounted for?" This question pushes you to combine ranking functions with PARTITION BY. You’ll be expected to explain how partitioning works and how the ranking behaves inside each partition.

• "Can you rank employees within each department and, within that, by their years of service?" This is designed to see if you can layer ranking logic effectively. You’d need to apply both partitioning and ordering, and possibly even layer a secondary ORDER BY clause to ensure proper sorting.

• "What happens if two employees have the same score in a ranking? How would you adjust the query?" Interviewers will watch how you handle ties. You can approach this with RANK() if you don’t mind gaps, or DENSE_RANK() if you want continuous ranking. However, if unique ranks are required despite tied scores, ROW_NUMBER() is the answer.

Advanced Challenges:

As interviews progress, expect more nuanced variations. You’ll be asked to work with real-world datasets where edge cases naturally occur. This might include handling nulls in ranking, combining rank functions with OVER() clauses for dynamic windowing, or using ranking with GROUP BY and HAVING clauses.

• Handling NULL values in ranking: Many candidates overlook how nulls are treated in ranking functions. By default, nulls are considered the lowest possible value when ordering in ascending order. What if you need to rank rows excluding null values? This requires careful WHERE or CASE logic to exclude or manage nulls properly.

• Combining Rank Functions with Windowing: Interviewers love to ask about the OVER() clause and how you can use it to create dynamic windows. How would you rank employees within a moving 3-month window of sales performance? Combining ranking with OVER() windowing is a common advanced SQL question, and this requires you to not only use rank functions but to manipulate the window size dynamically.

• Ranking with Aggregates: A tricky question might involve combining ranking with aggregate functions like SUM() or AVG(). For example, how do you rank departments based on their total sales while still displaying individual employee data? This leads to more complex SQL that requires subqueries or CTEs to properly aggregate and then rank the results.

Practice Problem Breakdown:

Consider this example:

In this scenario, we are using RANK() to rank employees within each department. Alice and John have the same sales, so they share the same rank, but notice the gap before Bob’s rank of 3. If the interviewer then asks you to eliminate gaps in ranking, you’ll switch from RANK() to DENSE_RANK().

Your response in an interview should also explain the performance implications. Ranking functions are often computed at query time, meaning large datasets could significantly slow down when multiple partitions are involved.

What Not To Do:

• Avoid giving long, theoretical explanations. Interviews are not lectures; they're conversations. The more practical, the better.
• Don’t overlook edge cases. Interviewers test whether you think about the anomalies and how your query performs under real-world conditions.

Final Thought:

The deeper your understanding of ranking functions and their behavior under various conditions, the better your performance will be in SQL interviews. Ranking questions go beyond syntax—they’re about your ability to think logically, optimize performance, and predict edge cases. Understanding ranking functions deeply will set you apart from others, and that’s the ultimate goal.