Clustered and non-clustered indexes are both essential tools in optimizing database queries, but they serve different purposes and function in distinct ways. A clustered index dictates the physical order of data in a table. This means that the rows are stored on disk in the order of the indexed column(s). Each table can have only one clustered index because there can only be one way to sort the data physically. An example of a clustered index is a primary key; when a primary key is set on a table, it automatically creates a clustered index if no other clustered index exists.
On the other hand, a non-clustered index creates a separate structure that points back to the original data. This means that the data in a table does not need to be stored in the same order as the indexed columns. A non-clustered index contains a copy of the indexed columns and a pointer to the location of the corresponding rows in the data table. A table can have multiple non-clustered indexes, allowing for various fields to be indexed for faster searches. For instance, if a table contains user data, you might have a clustered index on the user ID (to ensure that the data is stored by user ID) and non-clustered indexes on fields like email and last name for quicker access to searches based on those attributes.
In summary, the key difference between clustered and non-clustered indexes is in how they organize and store data. Clustered indexes arrange the data physically in the table based on the indexed column, making them efficient for range queries, where the order matters. Non-clustered indexes, however, maintain a separate structure that can be used to quickly look up values without altering the physical storage of the main table. In practice, a well-designed indexing strategy that combines both types can significantly improve query performance and reduce response times in applications.