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SQL Server 2005 introduces native partitioning of tables and indexes. Partitioning your objects means that they are internally split into multiple physical units that together make the object (table or index). Partitioning is virtually unavoidable in medium to large environments. By partitioning your objects, you improve the manageability and maintainability of your system and you improve the performance of activities such as purging historic data, data loads, and others as well. Partitioning in SQL Server is nativethat is, you have built-in tools to partition the tables and indexes, while logically, to the applications and users they appear as whole units. In SQL Server 2000, partitioning was achieved by manually creating multiple tables and a view on top that unifies the pieces. SQL Server 2005 native partitioning has many advantages over partitioned views in SQL Server 2000, including improved execution plans, a substantially relaxed set of requirements, and more.
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Partitioning is outside the scope of this book, but I covered it in detail in a series of articles in SQL Server Magazine . You can find these articles at http://www.sqlmag.com . (Look up instant document IDs 45153, 45533, 45877, and 46207.)
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When conducting performance tests, it is vital that the sample data you use be well prepared so that it reflects the production system as closely as possible, especially with respect to the factors you are trying to tune. Typically, it's not realistic to just copy all the data from the production tables, at least not with the big ones. However, you should make your best effort to have an adequate representation, which will reflect similar data distribution, density of keys, cardinality, and so on. Also, you want your queries against the test system to have similar selectivity to the queries against the production system. Performance tests can be skewed when the sample data does not adequately represent the production data. In this section, I'll provide an example of skewed performance testing results resulting from inadequate sample data. I'll also discuss the new TABLESAMPLE option.
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When I prepared the sample data for this chapter's demonstrations, I didn't need to reflect a specific production system, so preparing sample data was fairly simple. I mainly needed it for the "Tuning Methodology" and "Index Tuning" sections. I could express most of my points through simple random distribution of the different attributes that were relevant to our discussions. But our main data tableOrdersdoes not accurately reflect an average production Orders table. For example, I produced a fairly even distribution of values in the different attributes, while typically in production systems, different attributes have different types of distribution (some uniform, some standard). Some customers place many orders, and others place few. Also, some customers are more active in some periods of time and less active in others. Depending on your tuning needs, you might or might not need to reflect such things in your sample data, but you definitely need to consider them and decide whether they do matter. When you need large tables with sample data, the easiest thing to do is to generate some small table and duplicate its content (save the key columns) many times. This can be fine if, for example, you want to test the performance of a user-defined function invoked against every row or a cursor manipulation iterating through many rows. But such sample data in some cases can yield completely different performance than what you would get with sample data that more adequately reflects your production data. To demonstrate this, I'll walk you through an example that I cover in much more depth in Inside T-SQL Programming. I often give this exercise in class and ask students to prepare a large amount of sample data without giving any hints. The exercise has to do with a table called Sessions, which you create and populate by running the code in Listing 3-6.
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