SQL Server 2008 R2 : Creating and Managing Indexes - Types of Indexes

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SQL Server has two main types of indexes: clustered and nonclustered. They both help the query engine get at data faster, but they have different effects on the storage of the underlying data. The following sections describe these two main types of indexes and provide some insight into when to use each type.

Clustered Indexes

Clustered indexes sort and store the data rows for a table, based on the columns defined in the index. For example, if you were to create a clustered index on the LastName and FirstName columns in a table, the data rows for that table would be organized or sorted according to these two columns. This has some obvious advantages for data retrieval. Queries that search for data based on the clustered index keys have a sequential path to the underlying data, which helps reduce I/O.

A clustered index is analogous to a filing cabinet where each drawer contains a set of file folders stored in alphabetical order, and each file folder stores the files in alphabetical order. Each file drawer contains a label that indicates which folders it contains (for example, folders A–D). To locate a specific file, you first locate the drawer containing the appropriate file folders, then locate the appropriate file folder within the drawer, and then scan the files in that folder in sequence until you find the one you need.

A clustered index is structured as a balanced tree (B-tree). Figure 1 shows a simplified diagram of a clustered index defined on a last name column.

Figure 1. A simplified diagram of a clustered index.

The top, or root, node is a single page where searches via the clustered index are started. The bottom level of the index is the leaf nodes. With a clustered index, the leaf nodes of the index are also the data pages of the table. Any levels of the index between the root and leaf nodes are referred to as intermediate nodes. All index key values are stored in the clustered index levels in sorted order. To locate a data row via a clustered index, SQL Server starts at the root node and navigates through the appropriate index pages in the intermediate levels of the index until it reaches the data page that should contain the desired data row(s). It then scans the rows on the data page until it locates the desired value.

There can be only one clustered index per table. This restriction is driven by the fact that the underlying data rows can be sorted and stored in only one way. With very few exceptions, every table in a database should have a clustered index. The selection of columns for a clustered index is very important and should be driven by the way the data is most commonly accessed in the table. You should consider using the following types of columns in a clustered index:

  • Those that are often accessed sequentially

  • Those that contain a large number of distinct values

  • Those that are used in range queries that use operators such as BETWEEN, >, >=, <, or <= in the WHERE clause

  • Those that are frequently used by queries to join or group the result set

When you are using these criteria, it is important to focus on the most critical data access: the queries that are run most often or that must have the best performance. This approach can be challenging but ultimately reduces the number of data pages and related I/O for the queries that matter.

Nonclustered Indexes

A nonclustered index is a separate index structure, independent of the physical sort order of the data rows in the table. You are therefore not restricted to creating only 1 nonclustered index per table; in fact, in SQL Server 2008 you can create up to 999 nonclustered indexes per table. This is an increase from SQL Server 2005, which was limited to 249.

A nonclustered index is analogous to an index in the back of a book. To find the pages on which a specific subject is discussed, you look up the subject in the index and then go to the pages referenced in the index. With nonclustered indexes, you may have to jump around to many different nonsequential pages to find all the references.

A nonclustered index is also structured as a B-tree. Figure 2 shows a simplified diagram of a nonclustered index defined on a first name column.

Figure 2. A simplified diagram of a nonclustered index.

As with a clustered index, in a nonclustered index, all index key values are stored in the nonclustered index levels in sorted order, based on the index key(s). This sort order is typically different from the sort order of the table itself. The main difference between a nonclustered index and clustered index is that the leaf row of a nonclustered index is independent of the data rows in the table. The leaf level of a nonclustered index contains a row for every data row in the table, along with a pointer to locate the data row. This pointer is either the clustered index key for the data row, if the table has a clustered index on it, or the data page ID and row ID of the data row if the table is stored as a heap structure (that is, if the table has no clustered index defined on it).

To locate a data row via a nonclustered index, SQL Server starts at the root node and navigates through the appropriate index pages in the intermediate levels of the index until it reaches the leaf page, which should contain the index key for the desired data row. It then scans the keys on the leaf page until it locates the desired index key value. SQL Server then uses the pointer to the data row stored with the index key to retrieve the corresponding data row.


For a more detailed discussion of clustered tables versus heap tables (that is, tables with no clustered indexes) and more detailed descriptions of clustered and nonclustered index key structures and index key rows, as well as how SQL Server internally maintains indexes.

The efficiency of the index lookup and the types of lookups should drive the selection of nonclustered indexes. In the book index example, a single page reference is a very simple lookup for the book reader and requires little work. If, however, many pages are referenced in the index, and those pages are spread throughout the book, the lookup is no longer simple, and much more work is required to get all the information.

You should choose your nonclustered indexes with the book index example in mind. You should consider using nonclustered indexes for the following:

  • Queries that do not return large result sets

  • Columns that are frequently used in the WHERE clause that return exact matches

  • Columns that have many distinct values (that is, high cardinality)

  • All columns referenced in a critical query (a special nonclustered index called a covering index that eliminates the need to go to the underlying data pages)

Having a good understanding of your data access is essential to creating nonclustered indexes. Fortunately, SQL Server comes with tools such as the SQL Server Profiler and Database Engine Tuning Advisor that can help you evaluate your data access paths and determine which columns are the best candidates. 

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