MSDN Documentation

Index Types in SQL Server

This document provides a comprehensive overview of the different index types available in SQL Server and their use cases to optimize database performance.

Understanding Indexes

An index is a database structure associated with a table or a view that speeds up the retrieval of rows by creating pointers to data. When you query data, SQL Server can use an index to locate the specific rows much faster than scanning the entire table.

Clustered Indexes

A clustered index determines the physical order of data in the table. Because of this, a table can have only one clustered index. The leaf nodes of the clustered index contain the actual data pages of the table.

• Key Characteristics:
• Data is physically sorted based on the clustered index key.
• Primary keys are often good candidates for clustered indexes.
• Each table can have only one clustered index.
• Excellent for range queries (e.g., WHERE OrderDate BETWEEN '2023-01-01' AND '2023-01-31').

Nonclustered Indexes

A nonclustered index is a separate structure from the data. It contains index key values and row locators that point to the actual data rows. The data rows are not stored in the order of the nonclustered index.

• Key Characteristics:
• Data is not physically sorted.
• Leaf nodes contain index key values and pointers (RID for heaps, clustered index key for clustered tables) to data rows.
• A table can have multiple nonclustered indexes.
• Useful for queries that retrieve a subset of columns or for specific WHERE clause conditions.

Specialized Index Types

SQL Server offers several specialized index types for specific scenarios:

Columnstore Indexes

Columnstore indexes store data column by column, rather than row by row. This is highly efficient for analytical workloads and data warehousing scenarios that involve large scans and aggregations over a few columns.

• Types: Clustered Columnstore and Nonclustered Columnstore.
• Benefits: High data compression ratios, faster analytical queries.
• Use Cases: Data warehousing, reporting, large fact tables.

Unique Indexes

A unique index enforces the uniqueness of index key values. This means that no two rows in the table can have the same index key value.

• Purpose: To prevent duplicate values in a column or a set of columns.
• Enforcement: Automatically enforced by SQL Server.

Filtered Indexes

A filtered index is an optimized nonclustered index on a subset of rows in a table. It is defined using a WHERE clause.

• Benefits: Improved query performance, reduced index maintenance costs, and reduced index storage.
• Use Cases: Indexing only active records, or specific status codes.

Full-Text Indexes

Full-text indexes enable fast searching of character-based data for specific words or phrases. They are designed for natural language searches rather than exact matches.

• Purpose: Searching large text documents efficiently.
• Capabilities: Supports linguistic rules, proximity searches, and rank ordering.

Spatial Indexes

Spatial indexes are designed to support efficient querying of spatial data types (e.g., geography and geometry). They optimize queries that involve spatial relationships.

• Use Cases: Location-based services, geographic information systems (GIS).

XML Indexes

XML indexes are used to improve the performance of queries that target XML data stored in XML data type columns.

• Purpose: Efficiently querying XML documents.
• Types: Primary XML Index and Secondary XML Indexes (PATH, VALUE, PROPERTY).

Choosing the Right Index

The choice of index type depends on several factors:

• Query Patterns: How is the data being queried? (e.g., range queries, exact matches, text searches).
• Data Distribution: Highly skewed data might benefit from different indexing strategies.
• Write vs. Read Operations: Indexes can speed up reads but can slow down writes (inserts, updates, deletes).
• Storage Space: Some index types, like columnstore indexes, offer high compression.

Index Maintenance

Indexes require maintenance to remain effective. This includes:

• Reorganizing or Rebuilding: To combat fragmentation caused by data modifications.
• Updating Statistics: To ensure the query optimizer has accurate information about data distribution.