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SQL Server does not need to have indexes on a table to retrieve data. A table can simply be scanned to find the piece of data that is requested, which works reason ably well. Unfortunately, users want to store massive amounts of data in a table and be able to retrieve data instantly. To allow data retrieval to occur very rapidly while also ensuring that performance does not decline as rows are added to a table, indexes are created. Indexes are not a new concept; we use them every day. If you look at the back of this book, you see an index in printed form. If you want to read about partitioning to review for your exam, you can find the information two different ways. You can open this book, start at page 1, and scan each page until you find the information you needed on partitioning. You also can open the index at the back of the book, locate partitioning, and then go to the corresponding page in the book. Either accomplishes your goal, but using the index is much more efficient. An index is useful only if it can provide a means to find data very quickly regardless of the volume of data that is stored. Take a look at the index at the back of this book. The index contains only a small sampling of the words in the book, so it provides a much more compact way to search for information. If the index were constructed

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based on the page in which a particular word first appeared, you would have to read many entries and pages to find your information. Instead, the index is organized alphabetically, which enables you to eliminate a large percentage of the pages in the index to find the page you need. Further, it enables you to scan down to the word you are looking for; after you find the word, you know that you don t have to look any fur ther. The way an index is organized in SQL Server is similar.

The structure that SQL Server uses to build and maintain indexes is called a balanced tree (B-tree). An example of a B-tree is shown in Figure 12-1.

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A B-tree is constructed of a root node that contains a single page of data, zero or more intermediate levels containing additional pages, and a leaf level that contains one or more pages. The leaf-level pages contain entries in sorted order that correspond to the data being indexed. The number of index rows on a page is determined by the storage space required by the columns that are defined in the index. For example, an index defined on a 4-byte integer column will have 5 times as many values per page as an index defined on a char(60) column that requires 60 bytes of storage per page. Intermediate levels are created by taking the first entry on each leaf level page and stor ing them in a page with a pointer to the leaf-level page. The root page is constructed in the same manner.

For a detailed explanation of the entries on an index page, as well as how an index is constructed, please refer to Inside SQL Server 2005: The Storage Engine, by Kalen Delaney (Microsoft Press, 2007) or Inside SQL Server 2005: T-SQL Querying, by Itzik Ben-Gan (Microsoft Press, 2006).

By constructing an index in this manner, tables with billions of rows of data can be searched just as quickly as tables with a few hundred rows of data. A query starts with the root page and scans the page until it finds a page that contains a value. This is probably about as clear as mud, so let s use an example to describe it (shown in Figure 12-2).

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