SQL RANKING FUNCTIONS – Get Row Numbers and Ranking

Ranking functions are an essential tool for SQL developers. They allow you to assign sequential row numbers or rank values to results to help organize result sets and perform sophisticated analysis. In this guide, we’ll explore the most common SQL ranking functions, examine use cases with examples, and provide best practices.

Ranking Functions Overview

SQL offers several built-in functions to add row numbers or ranks to your query results. Let’s look at how each one works:

ROW_NUMBER()

The ROW_NUMBER() function assigns a sequential row number to each row in the result set, beginning with 1. It does not take ties into account – every row gets a unique number regardless of duplicate values.

RANK()

The RANK() function assigns ranks based on the order of rows returned. If multiple rows tie for the same rank value, RANK() will skip the next rank and assign that value to the next row. Therefore, ranks may not be consecutive numbers.

DENSE_RANK()

Like RANK(), DENSE_RANK() assigns ranks to rows. However, there are no gaps in rank values when there are ties. If multiple rows have the same value, they will get the same rank. The next rank value will increment by 1 rather than skipping ranks.

NTILE()

NTILE(N) divides the rows into N buckets of equal size. Bucket values range from 1 to N, assigning each row a bucket number. It helps break result sets into groups of rows.

CUME_DIST()

CUME_DIST() calculates the relative position of a row compared to other rows. The returned values range from 0 to 1 representing the percentage of rows with values lower than or equal to the current row.

Use Cases and Examples

Now let’s look at common use cases for employing ranking functions in SQL queries:

Paginating Result Sets

Using ROW_NUMBER(), we can paginate results for displaying a subset of rows at a time:

 
    ,
    ROW_NUMBER()  (  )  row_num

    

   row_num    
  
   ;

Identifying Top/Bottom Results

To retrieve extreme values, we can use RANK() or DENSE_RANK() to find top/bottom results:

 *  (
    *, 
      ()  (  points )   
    players
)
  <= ; 

Bucketing Results into Groups

By applying NTILE(), we can divide records into groups of rows numbering them by their respective bucket:

   NTILE()  (  )  bucket, 
   *
 ;

There are many other examples, but these demonstrate how the functions enable common ranking tasks.

Advanced Ranking Techniques

We can level up our use of ranking functions by incorporating other SQL capabilities:

Combining Window Functions

Using PARTITION BY, we can divide rows into groups before ranking:

 
    ()  (
          department 
          sales )  dept_rank
 employees;

Accounting for Ties

We can break ties by providing multiple order by columns:

RANK() OVER (  sales , id )  rank

Optimizing Performance

For faster results, we can use SQL indexes in combination with ranking functions.

By mastering advanced tactics, we gain flexibility in ranking logic.

Choosing the Right Ranking Function

With several options to assign ranked row numbers, which function should you use?

Here are some guidelines:

• ROW_NUMBER(): Any time you need a sequentially numbered result set
• RANK(): When you require non-consecutive ranks for ties
• DENSE_RANK(): When you want consecutive ranks despite ties
• NTILE(): To break rows into numbered groups
• CUME_DIST(): To calculate relative position percentage

Think about your specific ranking needs to decide which method works best.

FAQ

What is the difference between RANK() and DENSE_RANK()?

RANK() will skip the next rank number when there is a tie, while DENSE_RANK() will number tied rows with the same rank and increment the next rank by 1.

When should I use NTILE vs other ranking functions?

NTILE() is specifically focused on dividing result sets into equally sized buckets. Other functions emphasize numerical order rather than creating groups.

Can I use ranking functions as filters?

Yes, by wrapping a ranking function in a subquery, we can filter the outer query based on row numbers, ranks, or buckets just like any other column value. This enables retrieving top/bottom results or specific groups.

Conclusion

SQL ranking functionality is invaluable for sorting and analyzing data in sophisticated ways with row numbers, ranks, groups, distributions and more. By mastering the built-in ranking functions, developers multiply the capabilities of querying and reporting possible with SQL databases. We explored the primary ranking methods available, how to apply them effectively to meet common needs, along with best practices for optimal rankings. By creatively employing these techniques, SQL developers can more deeply mine their data for insights.