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Creation QR in C#.NET Inside Microsoft SQL Server 2008: T-SQL Querying

Inside Microsoft SQL Server 2008: T-SQL Querying
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performance information, subtracting the value stored in the variable from the current value of SYSDATETIME. Note that SYSDATETIME returns a DATETIME2 value, which has an accuracy level of 100 nanoseconds; however, the actual accuracy of the function depends on the computer hardware and version of Windows your SQL Server instance is running on. That s because the SYSDATETIME function internally invokes the GetSystemTimeAsFileTime() Windows API, which is hardware and operating system dependent. When measuring the time statistics of queries for which the accuracy level of this function is insuf cient, run the queries repeatedly in a loop and divide run time for the entire loop by the number of iterations.
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Analyzing Execution Plans
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An execution plan is the work plan the optimizer generates to determine how to process a given query. The plan contains operators that are generally applied in a speci c order. Some operators can be applied while their preceding operator is still in progress. Some operators might be applied more than once. Also, some branches of the plan are invoked in parallel if the optimizer chose a parallel plan. In the plan, the optimizer determines the order in which to access the tables involved in the query, which indexes to use and which access methods to use to apply to them, which join algorithms to use, and so on. In fact, for a given query the optimizer considers multiple execution plans, and it chooses the plan with the lowest cost out of the ones that were generated. Note that SQL Server might not generate all possible execution plans for a given query. If it always did, the optimization process could take too long. SQL Server will calculate thresholds for the optimization process based on the sizes of the tables involved in the query, among other things. One threshold is time based. SQL Server won t spend longer than the time threshold on optimization. Another threshold is cost based. That is, if a plan is found with a lower cost than the cost threshold, it is considered good enough, in which case optimization stops and that plan is used. Throughout these books, I ll frequently analyze execution plans of queries. This section and the one that follows ( Index Tuning ) should give you the background required to follow and understand the discussions involving plan analysis. Note that the purpose of this section is not to familiarize you with all possible operators; instead, it is to familiarize you with the techniques to analyze plans. The Index Tuning section will familiarize you with index-related operators, and later in the book I ll elaborate on additional operators for example, join-related operators will be described in 7, Joins and Set Operations.
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Graphical Execution Plans
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Graphical execution plans are used extensively throughout these books. SSMS allows you both to get an estimated execution plan (by pressing Ctrl+L) and to include an actual one (by pressing Ctrl+M) along with the output of the query you run. Note that both will typically give you the same plan; remember that an execution plan is generated before the query is run. However, when you request an estimated plan, the query is not run at all. Obviously, some measures can be collected only at run time (for example, the actual number of rows
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Query Tuning
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returned from each operator and the number of executions of the operator). In the estimated plan, you will see estimations for measures that can be collected only at run time, while the actual plan will show the actuals and also some of the same estimates. To demonstrate a graphical execution plan analysis, I will use the following query:
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SELECT custid, empid, shipperid, COUNT(*) AS numorders FROM dbo.Orders WHERE orderdate >= '20080201' AND orderdate < '20080301' GROUP BY CUBE(custid, empid, shipperid);
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The query returns aggregated counts of orders for all possible grouping sets that can be de ned based on the attributes custid, empid, and shipperid. I ll discuss the CUBE subclause of the GROUP BY clause in detail in 8, Aggregating and Pivoting Data.
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