MSDN Documentation

Relational Database Indexes

Indexes are fundamental to the efficient retrieval of data from relational databases. They work much like an index in a book, allowing the database system to quickly locate specific rows without scanning the entire table. This dramatically improves query performance, especially for large datasets.

What is a Database Index?

An index is a data structure (often a B-tree) that stores a subset of the data from one or more columns of a table in a sorted order. Each entry in the index points to the location of the corresponding row(s) in the actual table. When you execute a query that filters or sorts on an indexed column, the database can use the index to find the relevant data much faster.

Why Use Indexes?

• Faster Data Retrieval: Significantly speeds up SELECT statements, especially with WHERE clauses, JOIN operations, and ORDER BY clauses.
• Improved Performance for Joins: Indexes on columns used in join conditions can drastically reduce the time taken for join operations.
• Enforcing Uniqueness: Unique indexes prevent duplicate values in a column or set of columns, ensuring data integrity.
• Faster Sorting and Grouping: Queries involving ORDER BY and GROUP BY on indexed columns can benefit from pre-sorted data.

Types of Indexes

Databases typically support various types of indexes:

Clustered Indexes

A clustered index determines the physical order of data rows in a table. A table can have only one clustered index. Typically, the primary key of a table is implemented as a clustered index.

Non-Clustered Indexes

A non-clustered index is a separate data structure that contains indexed column values and pointers to the actual data rows. A table can have multiple non-clustered indexes. These are generally faster to create and maintain than clustered indexes.

Unique Indexes

Ensures that all values in an indexed column (or combination of columns) are unique. This is often used to enforce primary keys or unique business constraints.

Full-Text Indexes

Optimized for searching text-based data, allowing for natural language queries and fuzzy matching.

Hash Indexes

Uses a hash function to map index keys to bucket locations. They are very fast for exact-match lookups but less efficient for range queries.

Creating and Managing Indexes

The syntax for creating indexes varies between database systems (e.g., SQL Server, MySQL, PostgreSQL). Here's a general SQL example:

-- Creating a non-clustered index
CREATE INDEX IX_Customers_LastName
ON Customers (LastName);

-- Creating a unique index
CREATE UNIQUE INDEX UQ_Employees_EmployeeID
ON Employees (EmployeeID);

-- Creating a clustered index (syntax may vary)
-- In SQL Server, often defined with PRIMARY KEY constraint
CREATE CLUSTERED INDEX PK_Orders
ON Orders (OrderID);

-- Dropping an index
DROP INDEX IX_Customers_LastName ON Customers;

When to Use Indexes?

Consider creating an index when:

• A column is frequently used in WHERE clauses.
• A column is used in JOIN conditions.
• A column is used in ORDER BY or GROUP BY clauses.
• You need to enforce data uniqueness.

When NOT to Use Indexes?

Be mindful of the overhead:

• Overhead on Writes: Every INSERT, UPDATE, or DELETE operation on a table requires updating all relevant indexes, which can slow down write operations.
• Disk Space: Indexes consume disk space.
• Small Tables: For very small tables, a full table scan might be faster than using an index due to the overhead of index lookup.
• Columns with Low Cardinality: Indexing columns with very few distinct values (e.g., a boolean flag) is often not beneficial.

Choosing the right indexing strategy is crucial for database performance. Regularly analyze your query performance and adjust indexes as needed.