1. ADVANCE T-SQL: WINDOW FUNCTIONS Rahman Wehelie 7/16/2013 ITC 226

2. WINDOWING Window Function belong to a type of function called 'set function‘ Window is used to refer to set of rows that the function works on Window were added to SQL:2003 - the fifth revision of the SQL database query language. other DBMSs such as Oracle, Sybase and DB2 have had support for window functions SQL Server has had only a partial implementation until SQL 2012 You implement window functions as part of a query’s SELECT expression

3. MAIN BENEFIT A big benefits of window functions is that we can access the detail of the rows from an aggregation GO CREATE TABLE Table (ID INT, Value Numeric(18,2)) GO INSERT INTO Table (ID, Value) VALUES(1, 50.3), (1, 123.3), (1, 132.9), (2, 50.3), (2, 123.3), (2, 132.9), (2, 88.9), (3, 50.3), (3, 123.3); GO

4. AGGREGATION If we sum the value column by conventional GROUP By SELECT ID, SUM(Value) FROM Table GROUP BY ID;

5. CONT… Here is the set that SUM aggregation function worked on Because we applied the aggregation function in the column value, grouping the results by ID, we the lose the details of the data

6. ROW DETAILS Suppose you need to write a query to return the total /average/quantity value of sales for each ID, and still return the actual values of the rows SELECT ID, Value, SUM(Value) AS "Sum" AVG(Value) AS "Avg" COUNT(Value) AS "Quantity" FROM Table GROUP BY ID; Column 'Table.Value' is invalid in the select list because it is not contained in either an aggregate function or the GROUP BY clause it is against the way that aggregations work

7. OVER() CLAUSE A commonly used alternative is to write every aggregation into a subquery The clause OVER() allows us to access the details of the rows that have been aggregated. SELECT ID, Value, SUM(Value) OVER() AS "Sum" AVG(Value) OVER() AS "Avg" COUNT(Value) OVER() AS "Quantity" FROM Table

8. NEW WINDOW OF DATA - PARTITION BY Suppose you want the aggregate the data grouped by ID To do this, use the clause PARTITION BY clause SELECT ID, Value, SUM(Value) OVER(PARTITION BY ID) AS "Sum" AVG(Value) OVER(PARTITION BY ID) AS "Avg" COUNT(Value) OVER(PARTITION BY ID) AS "Quantity" FROM Table

9. CONT..

10. ROW_NUMBER, RANK, DENSE_RANK AND NTILE To test the functions, let's create a table called Table1 GO CREATE TABLE Table1 (Col1 INT) GO INSERT INTO Tab1 VALUES(5), (5), (3), (1) GO

11. ROW_NUMBER() The ROW_NUMBER function is used to generate a sequence of numbers based on a set in a specific order. -- RowNumber SELECT Col1, ROW_NUMBER() OVER(ORDER BY Col1 DESC) AS "ROW_NUMBER()" FROM Table1

12. RANK() & DENSE_RANK() Returns the rank of each row within the partition of a result set. The rank of a row is one plus the number of ranks that come before the row in question. Rank() returns the result with a GAP after a tie, whereas the function DENSE_RANK doesn’t.

13. EXAMPLE -- Rank SELECT Col1, RANK() OVER(ORDER BY Col1 DESC) AS "RANK()" FROM Table1 GO -- Dense_Rank SELECT Col1, DENSE_RANK() OVER(ORDER BY Col1 DESC) AS "DENSE_RANK" FROM T able1

14. NTILE() The NTILE function is used for calculating summary statistics Distributes the rows in an ordered partition into a specified number of groups. The groups are numbered, starting at one. For each row, NTILE returns the number of the group to which the row belongs.

15. NTILE EXAMPLE -- NTILE SELECT Col1, NTILE(3) OVER(ORDER BY Col1 DESC) AS "NTILE(3)" FROM Table1 We can see that 4 rows were divided by 3, the remaining row is added in the initial group -- NTILE SELECT Col1, NTILE(2) OVER(ORDER BY Col1 DESC) AS "NTILE(2)" FROM Table1 In the next example, there are no remained rows