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Ranking functions in SQL are powerful tools that allow data analysts and developers to rank rows of data based on specific criteria. These functions are particularly useful when working with large datasets that require sorting and filtering to extract meaningful insights. In this article, we’ll explore the syntax and examples of ranking functions in SQL.
Syntax of Ranking Functions in SQL SQL provides several ranking functions used to assign a rank to each row in a result set based on a specified order. These ranking functions are as follows:
RANK() – Ranking Function in SQL
This function assigns a rank to each row within a result set. If two rows have the same value, they will be assigned the same rank, and the next rank will be skipped.
DENSE_RANK() – Ranking Function in SQL
This function assigns a rank to each row within a result set. If two rows have the same value, they will be assigned the same rank, and the next rank will not be skipped.
ROW_NUMBER() – Ranking Function in SQL
This function assigns a unique number to each row within a result set.
NTILE() – Ranking Function in SQL
The NTILE() function is a useful ranking function in SQL that allows you to divide a result set into a specified number of groups or “tiles” and assign a rank to each row within those groups. This function is particularly useful when you want to analyze large datasets and extract insights from them more efficiently.
Examples of Ranking Functions in SQL Let’s take a look at some examples to understand how to use ranking functions in SQL.
Consider the following table named ‘sales’:
| Date | Product | Sales |
|---|---|---|
| 2022-01-01 | A | 1000 |
| 2022-01-02 | B | 2000 |
| 2022-01-03 | C | 3000 |
| 2022-01-04 | D | 1000 |
| 2022-01-05 | E | 5000 |
| 2022-01-06 | F | 2000 |
| 2022-01-07 | G | 4000 |
To rank the rows in the ‘sales’ table by sales, we can use the RANK() function as follows:
SELECT Product, Sales, RANK() OVER (ORDER BY Sales DESC) AS Rank
FROM sales
The above query will produce the following output:
| Product | Sales | Rank |
|---|---|---|
| E | 5000 | 1 |
| G | 4000 | 2 |
| C | 3000 | 3 |
| B | 2000 | 4 |
| F | 2000 | 4 |
| A | 1000 | 6 |
| D | 1000 | 6 |
As you can see, the RANK() function assigns ranks to each row in the ‘sales’ table based on their sales figures. The rows with the highest sales figures are assigned a rank of 1, and the rows with the lowest sales figures are assigned a higher rank.
The above query assigns ranks to each row in the ‘sales’ table based on their sales figures. The rows with the highest sales figures are assigned a rank of 1, and the rows with the lowest sales figures are assigned a higher rank.
To use the DENSE_RANK() function instead of the RANK() function, we can modify the query as follows:
SELECT Product, Sales, DENSE_RANK() OVER (ORDER BY Sales DESC) AS Rank
FROM sales
The above query assigns ranks to each row in the ‘sales’ table based on their sales figures, with no gaps between ranks. If two or more rows have the same sales figures, they will be assigned the same rank.
The above query will produce the following output:
| Product | Sales | Rank |
|---|---|---|
| E | 5000 | 1 |
| G | 4000 | 2 |
| C | 3000 | 3 |
| B | 2000 | 4 |
| F | 2000 | 4 |
| A | 1000 | 5 |
| D | 1000 | 5 |
As you can see, the DENSE_RANK in above.
The NTILE() function is another powerful ranking function in SQL that partitions a result set into a specified number of groups or “tiles” and assigns a rank to each row within those groups. This function is useful when you want to divide a large dataset into a smaller number of evenly sized groups.
The syntax for the NTILE() function is as follows:
NTILE(n) OVER (ORDER BY column_name)
In this syntax, ‘n’ is the number of groups or “tiles” that you want to divide the result set into, and ‘column_name’ is the column that you want to order the rows by.
Let’s take an example to understand how to use the NTILE() function. Suppose we have a table named ’employees’ with the following columns: EmployeeID, FirstName, LastName, and Salary. To divide the ’employees’ table into three groups based on salary, we can use the following query:
SELECT EmployeeID, FirstName, LastName, Salary, NTILE(3) OVER (ORDER BY Salary DESC) AS Tile
FROM employees
The above query assigns a rank or “tile” to each row in the ’employees’ table based on their salary, with each tile representing a third of the total number of rows in the result set. If there are any rows left over after the division, they will be assigned to the highest tile.
In the result set, the ‘Tile’ column will display the assigned tile number for each row. For instance, if there are nine rows in the result set, the rows with the highest salaries will be assigned to Tile 1, the rows with the next highest salaries will be assigned to Tile 2, and the remaining rows will be assigned to Tile 3.
Conclusion:
In summary, ranking functions in SQL are a powerful tool that can help you analyze data more effectively by assigning a rank to each row in a result set. With the RANK() and NTILE() functions, you can easily divide a large dataset into smaller groups and analyze the data more efficiently.
The RANK() function assigns a unique rank to each row in a result set based on the values in one or more columns, while the NTILE() function divides a result set into a specified number of groups or “tiles” and assigns a rank to each row within those groups. Both functions are useful for gaining insights into large datasets and can help you identify patterns, trends, and outliers more easily.
By understanding how to use ranking functions in SQL, you can improve your ability to analyze data and gain insights that can inform your decision-making. Whether you are working with small or large datasets, these functions can help you extract meaningful insights that can drive better outcomes.
Read other SQL functions here.
Official resource related to SQL Server here.
