# Index Usage and Optimization > [!Note] > Effective index strategies hinge on using **indexes as accelerators** for rapid data access, ensuring **predicate coverage** to minimize I/O, avoiding costly **bookmark lookups**, continually **monitoring usage** to remove dead weight, and performing **maintenance** to sustain peak performance. Indexes serve as critical performance enablers in SQL Server, acting as accelerators that steer the optimizer toward **index seeks** rather than full **table scans**. The **query optimizer** evaluates **predicate coverage**—whether an index not only filters by the WHERE clause but also supplies all SELECTed columns—to determine if an index is **covering**, thus eliminating additional lookups. When an index lacks coverage, the engine resorts to a **bookmark lookup**, accessing the [[Clustered Index]] or [[Heap]] to fetch missing columns, which incurs random I/O and penalizes performance. To validate an index’s value, **monitoring usage** via dynamic management views and execution‐plan analysis reveals which indexes are truly employed in production workloads, guiding the removal or consolidation of unused structures. Finally, since indexes can fragment and [[Statistics]] can become stale, **updates and maintenance**—including reorganizing or rebuilding indexes and refreshing statistics—are essential to preserve the optimizer’s ability to make informed decisions. | **Aspect** | **What It Is** | **Impact** | **Recommendation** | | ----------------------------- | -------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | | **Indexes as Accelerators** | Data structures that enable fast lookups on key columns | Dramatically reduces full table scans and lowers query latency | Define [[Non-Clustered Index]] on frequently filtered or joined columns | | **Predicate Coverage** | Inclusion of both filtering predicates and SELECT list columns within an index | Eliminates additional I/O by avoiding bookmark lookups | Create [[Covering Index]] by adding key and included columns | | **Bookmark Lookup Operation** | Extra I/O step to fetch non‐covered columns from the [[Clustered Index]] or [[Heap]] | Introduces random I/O, increasing CPU and I/O cost | Minimize by extending indexes or redesigning queries | | **Monitoring Usage** | Analysis of index usage statistics and execution plans to identify unused or underutilized indexes | Reveals indexes that add maintenance overhead without benefit | Regularly review DMVs (e.g., sys.dm_db_index_usage_stats) and drop or consolidate as needed | | **Updates and Maintenance** | Rebuilding, reorganizing, and updating statistics to counteract fragmentation and stale metadata | Restores index efficiency and ensures accurate optimizer cost estimates | Schedule periodic maintenance jobs based on fragmentation thresholds and workload patterns | --- ## ## References - Korotkevitch, D. (2022). _SQL Server advanced troubleshooting and performance tuning: Best practices and techniques_. O’Reilly Media. - Nevarez, B. (2022). _SQL Server query tuning and optimization: Optimize Microsoft SQL Server 2022 queries and applications_. Packt Publishing.