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Prior to SQL Server 2005, reasonably simple set-based calculations of row numbers were possible, given a unique partitioning + sort column(s) combination. As I will describe later, set-based row number calculations without this unique combination also exist, but they are substantially more complex. All ranking value calculations can be achieved by counting rows. To calculate row numbers, you can employ the following fundamental technique. You simply use a subquery to count the number of rows with a smaller or equal sort value. This count corresponds to the desired row number. For example, the following query produces row numbers based on empid, generating the output in Table 4-22: SELECT empid, (SELECT COUNT(*) FROM dbo.Sales AS S2 WHERE S2.empid <= S1.empid) AS rownum FROM dbo.Sales AS S1 ORDER BY empid;

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Table 4-22.

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This technique to calculate row numbers, though fairly simple, is extremely slow. To understand why, examine the execution plan shown in Figure 4-6 created for the query.

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There is an index on the sort column (empid) that happens to be the Sales table's clustered index. The table is first fully scanned (Clustered Index Scan operator) to return all rows. For each row returned from the initial full scan, the Nested Loops operator invokes the activity that generates the row number by counting rows. Each row number calculation involves a seek operation within the clustered index, followed by a partial scan operation (from the head of the leaf level's linked list to the last point where inner_empid is smaller than or equal to outer_empid). Note that there are two different operators that use the clustered indexfirst, a full scan to return all rows; second, a seek followed by a partial scan for each outer row to achieve the count. Remember that the primary factor affecting the performance of queries that do data manipulation

Page 325 Index Symbols .asf 164 .jpg. See JPEG .png. See Portable Network Graphic (PNG) A ActiveX controls 51 animation 95 archiving projects 19, 124 audio 48 adding to presentations 163 capture devices 66, 72, 154 capturing 7, 66, 72, 114, 131, 141 editing 45, 169, 175 effects 175 file formats 89 importing 120, 123, 131 in slides 207 in templates 106, 250, 261, 264 narrations 52, 135, 137 options 139, 143, 150 playback level 175, 176 recording 77, 78, 82, 89, 101, 130 synchronizing with slides 116, 117, 199 AutoPlay 276 AVI 9, 10. See also audio B background colors 109 bandwidth 91 bit rates 90, 151, 214, 241 broadcasting standards 59

will usually be I/O A rough estimate of the number or rows accessed here will show how inefficient this execution plan is To calculate rownum for the first row of the table, SQL Server needs to scan 1 row in the index For the second row, it needs to scan 2 rows For the third row, it needs to scan 3 rows, and so on, and for the nth row of the table, it needs to scan n rows For a table with n rows, having an index based on the sort column in place, the total number of rows scanned is 1 + 2 + 3 + .. + n You may not grasp immediately the large number of rows that are going to be scanned To give you a sense, for a table with 100,000 rows, you're looking at 5,000,050,000 rows that are going to be scanned in total.

This section describes some of the design goals behind Prism and contains a brief summary of Prism s key concepts It then provides an overview of the first few steps required to create a basic Prism application Prism is designed around the core architectural design principles of separation of concerns and loose coupling This allows Prism to provide many benefits, including the following: Reuse Prism promotes reuse by allowing components and services to be easily developed, tested and integrated into one or more applications At the component level, Prism facilitates the reuse of unit-tested components that can be easily discovered and integrated at run time through dependency injection At the application level, Prism supports the use of modules that encapsulate application-level capabilities and can be reused across applications Extensibility.

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