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CREATE TABLE MULTIDENSITY (col1 INT, col2 INT); go DECLARE @i INT; SET @i=0; WHILE @i < 10000 BEGIN INSERT INTO MULTIDENSITY(col1, col2) VALUES (@i, @i+1); INSERT INTO MULTIDENSITY(col1, col2) VALUES (@i, @i+2); INSERT INTO MULTIDENSITY(col1, col2) VALUES (@i, @i+3); set @i+=1; END; GO -- create multi-column density information CREATE STATISTICS s1 ON MULTIDENSITY(col1, col2); GO

In col1, there are 10,000 unique values, each duplicated three times. In col2, there are actually 10,002 unique values. For the multicolumn density, each set of (col1, col2) in the table is unique. Figure 8-22 shows the data stored for the multicolumn statistics object.

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B2B (Business to business) Implies communications or data transfer Back Door A hidden method of access, such as a secret password or account that a hacker can use to gain access to a computer without permission A back door can be installed during development by the original programmer, or it can be created by a Trojan Horse Bit The smallest unit of data in a computer However, computers usually store data and execute instructions in multiples of bits, called bytes A bit is abbreviated with a small b Blob Binary large object BML (Bean Markup Language) IBM s markup language for accessing and configuring JavaBeans Bricks and Mortar Slang term used to indicate non-e-commerce, physical stores BSML (Biosequence Markup Language) A graphical language for genetics Byte There are eight bits in a byte A byte is abbreviated with a capital B In communications and data transmission, a byte may be composed of 10 bits because of overhead, compression, and error checking

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DBCC SHOW_STATISTICS ('MULTIDENSITY', 's1')

Each chapter in this book explains when and how to use any code samples for that chapter. When it s time to use a code sample, the book will list the instructions for how to open the files. The chapters are built around scenarios that simulate real programming projects so you can easily apply the skills you learn to your own work. For those of you who like to know all the details, a list of the code sample projects appears in the following table. Almost all projects have solutions available for the practice exercises. The solutions for each project are included in the folder for each chapter and are labeled Complete.

The density information for col1 is 0.0001. 1/0.0001 = 10,000, which is the number of unique values of col1. The density information for (col1, col2) is about 0.00003 (the numbers are stored as oating point and are imprecise). Let s examine the cardinality estimates for the GROUP BY operation using GROUP BY lists that match the density information in Figure 8-23. The actual and estimated cardinalities match up exactly for this query.

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Note I have reordered the columns from the STATISTICS PROFILE output to show the EstimateRows column for the Hash Match implementing the GROUP BY operation. For a query grouping over both columns, you can see that the estimate matches up with the value seen in the density calculation. The STATISTICS PROFILE output in Figure 8-24 shows that this changes the estimate to 30,000 rows.

The Query Optimizer actually has to perform an additional step when calculating the output cardinality for an operator. Because statistics are usually created before the compilation of the query that uses them and are often only samples of the data, the values stored in the statistics object do not usually match the exact count of rows at the time the query is compiled. So the Query Optimizer uses these two values to calculate the fraction of rows that

should qualify in the operation. This is then scaled to the actual number of values in the table at the time that the query is compiled. The Query Optimizer does not expose exactly how each part of the cardinality estimate is computed. However, if you nd that a query has estimates that vary widely from what actually happens when you run the query, statistics pro le can help you identify if the Query Optimizer has bad information. You may need to update statistics to capture new data in the table, create statistics with a higher sample rate, or otherwise make sure that the information used during compilation is accurate. Although SQL Server does this automatically in most cases, this is often a good way to nd and x problems with poor plan selection.

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