With examples, this article will teach you how to use ranking functions in SQL Server. The ranking function is one of SQL Server's window functions. This gives each row a rank depending on the specified column. We have the ranking functions shown below.

Let’s learn each ranking function in detail.

    RANK ()
    ROW_NUMBER ()
    DENSE_RANK ()
    NTILE ()

Firstly, we will create the table StudentsReport to understand each function.
CREATE TABLE StudentsReport
(
StudentName VARCHAR(50),
Subjects VARCHAR(50),
Marks INT
)

INSERT INTO StudentsReport VALUES('Peter','english',90)
INSERT INTO StudentsReport VALUES('John','english',98)
INSERT INTO StudentsReport VALUES('Mark','english',98)
INSERT INTO StudentsReport VALUES('Alex','english',100)
INSERT INTO StudentsReport VALUES('Leo','english',86)
INSERT INTO StudentsReport VALUES('Maria','english',91)
INSERT INTO StudentsReport VALUES('Scott','english',86)
INSERT INTO StudentsReport VALUES('Laura','english',98)

SELECT * FROM StudentsReport;

Now let’s start with very first RANK () function to see how it works in SQL server.

RANK () function
RANK () function is a window function which assign a rank to each row within its partition. Now let’s see the syntax of rank () function and understand how we use it in SQL Server.

SELECT Column_Name
RANK() OVER (PARTITION BY Expression ORDER BY Expression [ASC/DESC]) AS Rank
FROM Table_Name;

IN THIS SYNTAX, OVER clause sets the partitioning and ordering of the results before applying the corresponding window function.
PARTITION BY clause divides the rows of the result set into partitions to which the function is applied.
ORDER BY clause is required to set the rows in ascending or descending order in each partition where the function is applied.

Now let’s understand the rank () function with example.
SELECT StudentName, Subjects, Marks,
RANK() OVER(ORDER BY marks) AS RANKnumber
FROM StudentsReport;

We can observe in the preceding result set that some of the rows have the same RANKnumber because they have the same value in the Marks column. For example, it assigned the same RANKnumber, 1 for identical marks, 86, but skipped the following RANKnumber 2 and went straight to RANKnumber 3.

Because we did not apply the PARTITION BY clause, the above result is displayed in a single partition.

Let's look at another example where we'll use the PARTITION BY clause.

SELECT StudentName, Subjects, Marks,
RANK() OVER(PARTITION BY Marks ORDER BY StudentName) AS RANKnumber
FROM StudentsReport;

In above result set we can see PARTITION BY clause divides the rows based on column Marks and assign the RANKnumber to each row within its partition and used order by clause in column StudentName to sort the result.

ROW_NUMBER () Function
This function returns a unique Rownumber in sequential order for each row in the result. Below is the Syntax of ROW_NUMBER () function.
SELECT Column_Name
ROW_NUMBER() OVER (PARTITION BY Expression ORDER BY Expression [ASC/DESC]) AS Rownumber
FROM Table_Name;

Now let’s understand the ROW_NUMBER () function with example to see how it works.
SELECT StudentName, Subjects, Marks,
ROW_NUMBER() OVER(ORDER BY Marks) Rownumber
FROM  StudentsReport;

On the above result set, we can see that it assigns unique Rownumbers in sequential order and sorts the results based on column Marks. Because we did not apply the PARTITION BY clause, the above result is displayed in a single partition. Now let take another example where we will use PARTITION BY clause.
SELECT StudentName, Subjects, Marks,
ROW_NUMBER() OVER(PARTITION BY Marks ORDER BY StudentName) Rownumber
FROM  StudentsReport;

In above result set we can see PARTITION BY clause works in ROW_NUMBER () function same as RANK () function partition and we can see the same result set.

DENSE_RANK () function
This function returns consecutive rank for column defined in the function. Below is the Syntax of DENSE_RANK () function.
SELECT Column_Name
DENSE_RANK() OVER (PARTITION BY Expression ORDER BY Expression [ASC/DESC]) AS Denserank
FROM Table_Name;

Now let’s understand the DENSE_RANK () function with example to see how it works.
SELECT StudentName, Subjects, Marks,
DENSE_RANK() OVER(ORDER BY Marks) Denserank
FROM  StudentsReport;

In the above result set we can see that some of the rows have same denserank as they have same value in Marks column. for example, it assigned the same denserank i.e. 1 for same marks and then jumped to denserank 2 for another marks.

NTile(N) Function
NTile(N) Function is a window function that distributes rows in a number of groups defined in the function. for example, in NTILE(N) function if we take N=4 and it will distribute result set in 4 groups. Below is the Syntax of NTILE(N) function.

SELECT Column_Name
NTILE(N) OVER (PARTITION BY Expression ORDER BY Expression [ASC/DESC]) AS NTileOutput
FROM Table_Name;

Now let’s understand the NTILE(N) function with example to see how it works.
SELECT StudentName, Subjects, Marks,
NTILE(4) OVER (ORDER BY marks DESC) AS NTileOutput
FROM StudentsReport;

We can see in the above result set that it divides rows into a number of groups defined by the function.
Partitioning by clause works the same for all four-ranking functions.

Conclusion
We learned about the ranking functions available in SQL Server and how to apply them in this post. I hope you found this post helpful; please share your thoughts and suggestions in the comments area.

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The maintenance of identity columns is critical in SQL databases for maintaining data integrity. After an INSERT operation, two often used functions, @@IDENTITY and SCOPE_IDENTITY(), aid in recovering recently produced identity data. Understanding the distinctions between these two is critical for database administrators and developers.

What exactly is @@IDENTITY?
@@IDENTITY is a global SQL Server variable that stores the most recent identity value generated during the current session across all tables with identity columns. While it may appear to be useful, it is crucial to realize that @@IDENTITY may not always yield the correct value. @@IDENTITY may capture the most recent identity value from any table in scenarios involving triggers or additional operations that create new tables with identity columns, potentially resulting in inaccurate results.

What exactly is SCOPE_IDENTITY()?
To alleviate the restrictions of @@IDENTITY, SQL Server provides SCOPE_IDENTITY(), a method built expressly to address identity column retrieval difficulties. SCOPE_IDENTITY() returns the most recently generated identity value within the current scope or session, making it a more dependable and accurate method of retrieving the recently inserted identity value.

Recommended Practices
When working with identity columns and needing to capture newly generated identity values after an INSERT operation, SCOPE_IDENTITY() is highly recommended. Unlike @@IDENTITY, SCOPE_IDENTITY() verifies the accuracy of the obtained identity value, avoiding potential conflicts caused by triggers or concurrent actions.

Conclusion
In conclusion, while @@IDENTITY may appear to be a convenient way to acquire the most recent identity value, its instability in certain contexts can result in unexpected results. SCOPE_IDENTITY(), on the other hand, provides a more secure and dependable method of retrieving the most recently created identity value within the current scope or session. Choosing between @@IDENTITY and SCOPE_IDENTITY() is critical for proper identity column retrieval and maintaining data consistency in SQL databases.

Understanding the difference between @@IDENTITY and SCOPE_IDENTITY() enables developers to manage identity columns with confidence and get accurate identity values, contributing to a more robust and dependable database environment.

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Two strong SQL tools—GROUP BY and the OVER clause—play critical roles in data management and analysis. Understanding these tools is critical for unlocking the full power of SQL queries. Let's investigate their subtleties and see how they help with data aggregation and window operations.

GROUP BY: Data Aggregation
The GROUP BY clause is essential for data aggregation in SQL. It enables you to group rows with similar values in one or more columns and then use aggregate methods like COUNT, SUM, AVG, MAX, and MIN to get summary results.

Consider the following scenario.
SELECT department, AVG(salary) AS avg_salary
FROM employees
GROUP BY department;

In this example, the query computes the average pay for each department in the 'employees' dataset, using the 'department' field to group the data. The category BY clause categorizes the output and computes the average income within each category.

GROUP BY isn't limited to one column; it can group data more precisely by using many columns, allowing extensive insights into diverse combinations of those columns.
The OVER clause is as follows: Activating Window Functions

The OVER clause adds a powerful feature known as window functions. It only works on a subset of rows defined by a window. These methods compute across a set of table rows linked to the current row, rather than condensing the result set into a single output like aggregate functions do.
SELECT employee_id, salary,
       AVG(salary) OVER (PARTITION BY department) AS avg_salary_department
FROM employees;

This query employs the OVER clause with the AVG function to calculate the average salary for each department alongside individual employee data. The PARTITION BY clause divides the rows into partitions based on the 'department', enabling the calculation of the average salary within each partition.

Window functions are versatile, offering numerous functions like ROW_NUMBER, RANK, NTILE, and more. They empower users to perform complex analytical tasks, such as ranking, cumulative sums, moving averages, and identifying top or bottom performers within specific partitions.

Key Differences and Use Cases

While both GROUP BY and the OVER clause perform data aggregation, their functionalities differ significantly. GROUP BY creates a single row per group by collapsing the result set, whereas the OVER clause works with window functions to provide analytical insights while preserving individual rows.

GROUP BY is ideal for summarizing and reducing data and is often used in aggregate queries. Conversely, the OVER clause shines in analytical scenarios where a detailed view of the dataset is required without losing individual records. Mastering GROUP BY and the OVER clause is crucial for leveraging the full potential of SQL in data analysis. Understanding their capabilities and distinctions empowers SQL practitioners to craft sophisticated queries for both aggregating and analyzing data, unlocking deeper insights from databases.

These tools are invaluable for anyone working with SQL, offering a robust arsenal to tackle diverse data analysis and reporting tasks. Harness the power of GROUP BY and the OVER clause to elevate your SQL skills and unearth rich insights from your data.

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