SQL RANK() vs. ROW_NUMBER(): How Do They Differ When Handling Ties?

SQL RANK() and ROW_NUMBER(): detailed explanation of differences

In SQL, RANK() and ROW_NUMBER() have similar functions and are easily confused. However, understanding their subtle differences is critical for effective data manipulation.

Comparison of RANK() and ROW_NUMBER()

RANK() assigns the same rank to rows and columns with the same sort value within a partition, while ROW_NUMBER() always increments, even for rows and columns with the same value.

Key Difference: Handling Duplicate Values

The main difference between RANK() and ROW_NUMBER() is their behavior when encountering duplicate values ​​within a partition:

• RANK(): Assigns rows with duplicate values ​​the same rank.
• ROW_NUMBER(): Arbitrarily assign increasing ranks to rows with duplicate values.

Example

Consider the following form:

StyleID	ID	Description
1	1	Item A
1	1	Item B
1	1	Item C
1	2	Item D

Use the following query:

<code class="language-sql">SELECT ID, Description, RANK() OVER (PARTITION BY StyleID ORDER BY ID) AS 'Rank' FROM SubStyle;
SELECT ID, Description, ROW_NUMBER() OVER (PARTITION BY StyleID ORDER BY ID) AS 'RowNumber' FROM SubStyle;</code>

The same result can only be obtained if there are no duplicate values ​​within the partition. For example, if you add another row with StyleID 3 and ID 1, you will observe the following:

StyleID	ID	Description	Rank	RowNumber
1	1	Item A	1	1
1	1	Item B	1	2
1	1	Item C	1	3
1	2	Item D	4	4
3	1	New Item	1	5

As you can see, for a unique value with StyleID of 3, ROW_NUMBER() assigns an increasing number, while RANK() assigns it the same rank as a row with a duplicate value of StyleID of 1.

Conclusion

RANK() and ROW_NUMBER() serve different purposes. RANK() is used to rank unique values ​​within a group, handling duplicate values ​​appropriately. ROW_NUMBER(), on the other hand, always increments, ensuring that each row has a distinct numeric value, even for rows with duplicate values. Understanding these differences is critical to making informed decisions when working with SQL data.

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