SQL Performance Tuning: Index Optimization

Types of Indexes

Different index types serve different purposes and have varying performance characteristics.

• B-Tree Indexes: The most common type, suitable for equality and range queries.
• Bitmap Indexes: Efficient for columns with low cardinality, often used in data warehousing.
• Hash Indexes: Optimized for equality comparisons but not range queries.
• Clustered Indexes: Determine the physical order of data in the table.
• Non-Clustered Indexes: Separate structure pointing to data rows.

Best Practices for Index Optimization

Effective index optimization requires understanding query patterns and balancing read and write performance.

• Create indexes on columns frequently used in WHERE clauses, JOIN conditions, and ORDER BY clauses.
• Use composite indexes when multiple columns are often queried together.
• Avoid indexing columns with high update frequency to reduce maintenance overhead.
• Limit the number of indexes to avoid excessive storage and slower writes.
• Regularly analyze query execution plans to identify missing or unused indexes.
• Consider index fill factor and fragmentation; rebuild or reorganize indexes as needed.

Common Mistakes in Indexing

Misusing indexes can degrade performance instead of improving it.

• Creating too many indexes leading to slower INSERT/UPDATE/DELETE operations.
• Indexing columns with low selectivity (e.g., boolean flags) that do not filter data effectively.
• Ignoring the order of columns in composite indexes, which affects index usage.
• Not updating statistics or ignoring index fragmentation.
• Relying solely on indexes without considering query rewriting or schema design.

Practical Example: Creating and Using Indexes

Let's consider a table named 'Employees' with columns 'EmployeeID', 'LastName', 'DepartmentID', and 'HireDate'.