Analytical & Window Functions

Please refer to my previous post in which schema and data for EMP and DEPT tables available.
In this article we are about to discuss about SQL Server Analytical and Window functions. As stated in Microsoft docs, analytic functions calculate an aggregate value based on a group of rows. Unlike aggregate functions, however, analytic functions can return multiple rows for each group. Use analytic functions to compute moving averages, running totals, percentages or top-N results within a group.
These functions are common in most of the RDBMS applications and are widely used by the data and business analysts.
NTILE
It divides/distributes an ordered data set (or partition) into a specified number of groups which we call it buckets and assigns an appropriate (bucket) number to each row. The bucket number will represent each row to which bucket it belongs to.
In other words, it is used to divide rows into equal sets and assign a number to each row.

SELECT Ename, sal, NTILE(2) OVER (ORDER BY sal DESC) Bucket FROM Emp;


SELECT Ename, sal, NTILE(5) OVER (ORDER BY sal DESC) Bucket FROM Emp;


The following query retrieves the records from the first bucket.

SELECT * FROM (
SELECT          Ename,
sal, NTILE(4) OVER (ORDER BY sal DESC) Bucket
FROM Emp
) EmpAlias
WHERE Bucket=1;
ROW NUMBER:
This function represents each row with a unique and sequential value based on the column used in OVER clause. Here, we are having 10 rows in our Emp table and will use ROW_NUMBER on these records.
This can also be used to assign a serial /row number to the rows within the provided dataset.
SELECT DeptNo, sal, ROW_NUMBER() OVER (ORDER BY sal) AS row_num FROM emp;

SELECT 
     DeptNo, 
     sal, 
     ROW_NUMBER() OVER (PARTITION BY deptno ORDER BY sal DESC) AS row_num 
FROM emp;
RANK:
This function is used to assign a rank to the rows based on the column values in OVER clause.

The row with equal values assigned the same rank with next rank value skipped.   

SELECT DeptNo, sal, RANK() OVER(ORDER BY sal DESC) AS rnk FROM emp;


SELECT   DeptNo, 
                 sal, 
                 RANK() OVER(PARTITION BY DeptNo ORDER BY sal DESC) AS rnk 
FROM emp;
DENSE_RANK: The DENSE_RANK analytics function used to assign a rank to each row. The rows with equal values receive the same rank and this rank assigned in the sequential order so that norank values are skipped.

SELECT 
          DeptNo, 
          sal, 
          DENSE_RANK() OVER(PARTITION BY DeptNo ORDER BY sal DESC) AS dns_rnk 
FROM emp;
Let us use all the above functions in one query to see the difference in the results.

SELECT DeptNo AS dept, sal AS sal,
ROW_NUMBER() OVER (PARTITION BY DeptNo ORDER BY sal DESC) AS RowNumber,
RANK() OVER (PARTITION BY DeptNo ORDER BY sal DESC) AS iRank,
DENSE_RANK() OVER(PARTITION BY DeptNo ORDER BY sal DESC) AS DenseRank
FROM emp;


CUME_DIST:
This function stands for cumulative distribution. It computes the relative position of a column value in a group. Here, we can calculate the cumulative distribution of salaries among all departments. For a row, the cumulative distribution of salary is calculated as:
SELECT DeptNo, sal, CUME_DIST() OVER (ORDER BY sal) AS cum_dist FROM emp;

SELECT DeptNo, sal, CUME_DIST() OVER (ORDER BY sal) AS cum_dist FROM emp
WHERE DEPTNO in(20,30);


CUME_DIST(salary) = Number of rows with the value lower than or equals to salary / total number of rows in the dataset.

In the above example, due to ORDER BY clause, 1st row from salary will be counted as 1 and it will be divided by the total number of rows. That is 1/14 = 0.1

For the second row, it is 2/14 = 0.14;

For the 4th row and the next immediate row too has the same value, it will be calculated as 5/14 = 0.35 and assign it for the both rows.

Look at the outcome to understand.
PERCENT_RANK:
It is very similar to the CUME_DIST function. It ranks the row as a percentage. In other words, it calculates the relative rank of a row within a group of rows.

The range of values returned by PERCENT_RANK is between 0 to 1 and first row in the dataset is always zero. This means the return value is of the double type.

Let’s rank the salary by department wise as percentage:
Percent_Rank = (rank decreased by 1)/(remaining rows in the group)
SELECT DeptNo, sal,
RANK() OVER (PARTITION BY deptNo ORDER BY sal DESC) AS iRank,
CUME_DIST() OVER (PARTITION BY deptno ORDER BY sal) AS cum_dist,
PERCENT_RANK() OVER (PARTITION BY deptNo ORDER BY sal) AS perc_rnk 
FROM EMP;
Go


If you observe its behavior when it calculates the relative rank for the rows with same values, it assigns same percentage rank value (0.75) to both of them. The behavior is similar to rank function.

Range Between & Rows Between:
These functions are called window functionswhich fetches records right before and after the current record to perform the aggregation. It is similar to lead and lag functions however a window function defines a frame or window of rows with a given length around the current row, and performs a calculation across the set of data in the window. Mostly these functions will be used to get the cumulative sum/average, running or moving sum or averages.
Examples:
SELECT DISTINCT    DeptNo, –sal,
            SUM(sal) OVER(ORDER BY DeptNo RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS ‘RangeUnbound’
FROM emp
GO


SELECT DISTINCT    DeptNo, –sal,
            SUM(sal) OVER(ORDER BY DeptNo ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS ‘RowsUnbound’
FROM emp
GO


SELECT DISTINCT    EMPNO, sal,
            SUM(sal) OVER(ORDER BY EmpNo ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS ‘RowsUnbound’
FROM emp
GO


SELECT DISTINCT    EMPNO, sal,
            SUM(sal) OVER(ORDER BY EmpNo RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS ‘RangeUnbound’
FROM emp
GO


SELECT DISTINCT    Job, sal, DeptNo,
            SUM(sal) OVER(ORDER BY DeptNo RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS ‘RangeUnbound’
FROM emp
GO


lag AND Lead FUNCTIONS:
The LAG function gets the information from a past column, while LEAD brings information from an ensuing line. The two functions are fundamentally the same as one another and you can simply supplant one by the other by changing the sort request.
SELECT ename, deptno, sal,
LEAD(sal, 1) OVER(PARTITION BY deptno ORDER BY sal) AS lead1,
LEAD(sal, 2) OVER(PARTITION BY deptno ORDER BY sal) AS lead2,
LAG(sal,1) OVER(PARTITION BY deptno ORDER BY sal) AS lag1,
LAG(sal,2) OVER(PARTITION BY deptno ORDER BY sal) AS lag2
FROM emp ORDER BY deptno,sal;


Hope this article helped you in understanding Analytical and Window functions.

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