ANALYSIS OF THE IMPACT OF INDEXING ON SQL QUERY PERFORMANCE

Abstract

This work explores methods of SQL queries performance improvement in the context of relational database management systems by systematically applying and evaluating contemporary indexing strategies. As data volumes expand rapidly and the complexity of both transactional and analytical workloads, ensuring efficient query execution has become a major challenge for modern databases. Indexes play a critical role in speeding up data retrieval, reducing disk input/output operations, and enhancing the overall scalability of database systems. However, poorly designed or excessive indexing may result in increased storage requirements, slower update operations, and additional maintenance burdens.

This study examines the effects of different index types—including B-tree, hash, bitmap, and composite indexes—on query performance across different dataset sizes and access patterns. The experiments were carried out in a controlled computing environment, maintaining consistent datasets, query sets, and execution conditions to ensure fair and reproducible results. Performance was measured using several key indicators, including execution duration, and the time required for index creation and maintenance, index storage footprint, and effects on data modification operations. Findings indicate that carefully planned indexing strategies can substantially boost query efficiency: hash indexes are highly effective for equality searches, B-tree and composite indexes show strong performance for range queries and multi-attribute filtering, and bitmap indexes are particularly suitable for analytical tasks involving low-cardinality columns.

Additionally, the study presents a practical methodology for choosing index structures depending on the features of the workload and the properties of the underlying data. By tailoring indexing approaches to specific query patterns, database systems can achieve notable improvements in speed, responsiveness, and scalability while keeping the overhead from maintaining indexes during write-heavy operations to a minimum. This guidance provides practical guidance for database administrators and developers seeking to enhance the performance of relational database systems in real-world scenarios.