datamatrix c# library ADVANCED T-SQL in Visual C#.NET

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CHAPTER 12 ADVANCED T-SQL
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-- Anchor member definition SELECT e.ManagerID, e.EmployeeID, e.Title as EmployeeLevel, 0 AS Level, e.ContactId as ManagerContactId, CAST(' ' as nvarchar(8)) as ManagerTitle, CAST(' ' as nvarchar(50)) as ManagerFirst, CAST(' ' as nvarchar(50)) as ManagerLast, c.Title as EmployeeTitle,c.FirstName as EmployeeFirst, c.LastName as EmployeeLast FROM HumanResources.Employee AS e INNER JOIN Person.Contact c ON c.ContactId = e.ContactId WHERE ManagerID IS NULL UNION ALL -- Recursive member definition SELECT e.ManagerID, e.EmployeeID, e.Title as EmployeeLevel, Level + 1, e.ContactId as ManagerContactId, m.Title as ManagerTitle,m.FirstName as ManagerFirst, m.LastName as ManagerLast, c.Title as EmployeeTitle,c.FirstName as EmployeeFirst, c.LastName as EmployeeLast FROM HumanResources.Employee AS e INNER JOIN Person.Contact c ON c.ContactId = e.ContactId INNER JOIN EmployeeReportingStructure AS d ON d.EmployeeID = e.ManagerID INNER JOIN Person.Contact m ON m.ContactId = d.ManagerContactId ) -- Statement that executes the CTE SELECT ManagerID, EmployeeID, ISNULL(ManagerTitle+' ','')+ManagerFirst+' '+ManagerLast as Manager, EmployeeLevel, ISNULL(EmployeeTitle+' ','')+EmployeeFirst+' '+EmployeeLast as Employee, Level FROM EmployeeReportingStructure ORDER BY Level,EmployeeLast,EmployeeFirst OPTION (MAXRECURSION 4) CTEs are used not only as standalone expressions, but also within other functions, such as pivoting data, which you can see in action next.
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If you have ever used Excel, then you have also probably had to perform some sort of pivoting of results of the data so that rows of information are pivoted into columns of information. It is now possible to perform this kind of operation within SQL Server 2005 via a PIVOT statement. Pivoted data can also be changed back using the UNPIVOT statement, where columns of data can be changed into rows of data. In this next section, you will see both these statements in action. We will be using the AdventureWorks example in this section. The table involved in this example
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CHAPTER 12 ADVANCED T-SQL
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is the SalesOrderDetail table belonging to the Sales schema. This holds details of products ordered, the quantity requested, the price they are at, and then the discount the order received.
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Before we see PIVOT in action, we need to look at the information that we will pivot. The following code lists three products, and for each product we will sum up the amount sold, taking the discount into account: USE AdventureWorks GO SELECT productID,UnitPriceDiscount,SUM(linetotal) FROM Sales.SalesOrderDetail WHERE productID IN (776,711,747) GROUP BY productID,UnitPriceDiscount ORDER BY productID,UnitPriceDiscount This produces one line of output for each product/discount combination, as you can see in the following results: 711 711 711 711 711 747 776 776 776 0.00 0.02 0.05 0.10 0.15 0.00 0.00 0.02 0.35 143788.908000 11421.237324 4384.931245 2679.760530 3131.779950 501788.197700 1198796.448000 23020.131792 32906.152500
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By using PIVOT, we can alter this data so that we can create columns for each of the products, and each row is defined for the discount, therefore giving a cross-reference of product to discount. SELECT pt.Discount,ISNULL([711],0.00) As Product711, ISNULL([747],0.00) As Product747,ISNULL([776],0.00) As Product776 FROM (SELECT sod.LineTotal, sod.ProductID, sod.UnitPriceDiscount as Discount FROM Sales.SalesOrderDetail sod) so PIVOT ( SUM(so.LineTotal) FOR so.ProductID IN ([776], [711], [747]) ) AS pt ORDER BY pt.Discount Before we execute this code, let s take a look at what is happening. First of all, you need to create a subquery that contains the columns of data that the PIVOT operator can use for its aggregation (it will also be used later for displaying in the output). No filtering has been completed
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CHAPTER 12 ADVANCED T-SQL
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at this point any columns not used in the aggregation will be ignored. The code generates a table-valued expression with an alias of so. From this table, you then instruct SQL Server to PIVOT the columns while completing an aggregation on a specific column, in our case a SUM of the LineTotal column from the table value expression. It is also at this point you define the columns to create via the FOR statement, in our case a column for each product of the three products. This is the equivalent to using GROUP BY for the aggregation, and it s also the equivalent of filtering data from the so table value expression. However, within the so table value expression, there s also a third column, UnitPriceDiscount. Without this, the output from the PIVOT would produce one row with three columns one for each product. So this table value expression with the PIVOT produces a temporary resultset, which we name pt. We can then use this temporary resultset to produce our output. When you run the code, you should see output similar to what is shown in Figure 12-4.
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