A clustered index determines the physical order of data in a table. A clustered index is analogous to a telephone directory, which arranges data by last name. Because the clustered index dictates the physical storage order of the data in the table, a table can contain only one clustered index. However, the index can comprise multiple columns (a composite index), like the way a telephone directory is organized by last name and first name.
A clustered index is particularly efficient on columns that are often searched for ranges of values. After the row with the first value is found using the clustered index, rows with subsequent indexed values are guaranteed to be physically adjacent. For example, if an application frequently executes a query to retrieve records between a range of dates, a clustered index can quickly locate the row containing the beginning date, and then retrieve all adjacent rows in the table until the last date is reached. This can help increase the performance of this type of query. Also, if there is a column(s) that is used frequently to sort the data retrieved from a table, it can be advantageous to cluster (physically sort) the table on that column(s) to save the cost of a sort each time the column(s) is queried.
Clustered indexes are also efficient for finding a specific row when the indexed value is unique. For example, the fastest way to find a particular employee using the unique employee ID column emp_id is to create a clustered index or PRIMARY KEY constraint on the emp_id column.
Note: PRIMARY KEY constraints create clustered indexes automatically if no clustered index already exists on the table and a nonclustered index is not specified when you create the PRIMARY KEY constraint.
Alternatively, a clustered index could be created on lname, fname (last name, first name), because employee records are often grouped and queried in this way rather than by employee ID.