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The estimate for the operator is taken by looking at the histogram, counting the number of sampled rows matching the criteria (in this case, we have 10 histogram steps with 200 equal rows for values that match the lter condition). Then, the number of qualifying rows (2,000) is normalized against the number of rows sampled when the histogram was created (10,000) to create the selectivity for the operator (0.2). This is then multiplied by the current number of rows in the table (10,000) to get the estimated query output cardinality. The cardinality estimation process is continued for any other lter conditions and the results are usually just multiplied to estimate the total selectivity for each condition. One other interesting aspect of the histogram is RANGE_ROWS, DISTINCT_RANGE_ROWS, and AVG_RANGE_ROWS. Because histograms are limited to 200 steps, some values being queried may not be represented in the histogram steps. These values are represented in the RANGE values, and they are counts of rows between the step values. For query conditions that do not match one of the equal (EQ) rows in the histogram, values in the range are assumed to be uniformly distributed over the domain between the two bounding histogram steps. The fraction is determined from this assumption and used to generate the selectivity, as in the previous examples. Although most operators work using a mechanism similar to Filter, some other operators need additional mechanisms to make good cardinality estimates. For example, GROUP BY actually doesn t try to determine which slices of a histogram should be used to estimate the selectivity of the operator. Instead, it needs to determine the number of unique values over a

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set of columns, as can be seen in Figure 8-30. This information can be estimated by looking at the histogram, but there is another mechanism in the statistics object to help perform this calculation quickly. The density information is stored in the histogram in the second result set, and in this case, it is 0.02 for col3.

SELECT COUNT(*) FROM Table3 GROUP BY col3;

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The next IntelliSense feature I ll discuss is the Organize Usings command. This is a new feature for Visual C# 2008, and it helps you keep your code clean. It helps you do that by eliminating using statements you are not using anywhere in your code. It could be a tedious job to do this manually and check that every statement has its place in your code. In fact, to show how it works, delete the XmlDocument line you entered in the previous section. Then right-click, select Organize Usings, and then select Remove Unused Usings or Remove and Sort. Either of these actions deletes the using System.Xml line. Look at Figure 5-13 for the before (left) and after (right) versions. Note that you can also simply sort the using statements for the clarity and readability of your code.

This is a representation of the average number of duplicates for any value in the table. In other words, this tells us how to compute the number of groups using the total number of rows. For this simple GROUP BY query, the estimate of rows is (1/0.02)*(10,000/10,000) = 50, which matches the number of groups we would expect from our creation script:

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GROUP BY Selectivity = (1/density) GROUP BY Card. Estimate = (Input operator) * (selectivity)

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When a multicolumn statistics object is created, it computes density information for the sets of columns being evaluated in the order of the statistics object. So a statistics object created on (col1, col2, col3) has density information stored for ((col1), (col1, col2), and (col1, col2, col3)). This can be used to compute the cardinality estimate for a query over that table doing GROUP BY col1, GROUP BY col1, col2, or GROUP BY col1, col2, col3. We can see this computation in the results of DBCC SHOW_STATISTICS in Figure 8-31.

CREATE TABLE Table4(col1 int, col2 int, col3 int) GO DECLARE @i int=0 WHILE @i< 10000 BEGIN INSERT INTO Table4(col1, col2, col3) VALUES (@i % 5, @i % 10,@i % 50); SET @i+=1 END CREATE STATISTICS s1 on Table4(col1, col2, col3) DBCC SHOW_STATISTICS (Table4, s1)

IntelliSense can also automatically generate a method stub for you. This feature is an IntelliSense automatic code generation feature that provides you with an easy way to create a new method declaration as you write the code to call it. You just have to let Visual Studio infer the declaration from the call. For instance, in this example, after the second for loop, type the following line of code:

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