INDEXING STRATEGIES: WAYS TO IMPROVE DATABASE PERFORMANCE

ISSN: 2582-4686 SJIF 2021-3.261,SJIF 20222.889, 2024-6.875 ResearchBib IF: 9.948 / 2024 VOLUME-5, ISSUE-12 1228 INDEXING STRATEGIES: WAYS TO IMPROVE DATABASE PERFORMANCE Teshaboyeva Barno Ibroximjon kizi Hudoberdiyeva Ifodahon Ganijonovna Fergana State Technical University Students of Computer Engineering Zokirov Sanjar Ikromjon ugli Fergana State Technical University, Doctor of Philosophy (PhD) in Physics and Mathematics, Associate Professor Abstract. This article discusses the theoretical foundations and practical significance of indexing strategies in databases. The role of indexing mechanisms in the processes of searching, sorting and processing data is analyzed, and the main methods used to increase efficiency are considered. During the writing of the article, the advantages and disadvantages of various index types are identified, and scientific conclusions are drawn on their correct selection and application. The article covers the most effective indexing strategies for OLTP, OLAP, NoSQL and distributed databases, real-world experiences and innovative approaches. Keywords: database, indexing, efficiency, search speed, B-tree, hash index, optimization, NoSQL, OLTP, OLAP. INTRODUCTION. Currently, the rapid growth of information systems and databases makes the issue of ensuring their efficiency urgent. Especially in modern information systems that work with large amounts of data, the speed of search, sorting and analysis processes is of great importance. The efficiency of these processes largely depends on the indexing strategies used in the database. Indexing is the process of creating special structures that provide quick access to information stored in the database, which significantly increases the speed of system operation. A correctly selected indexing strategy helps to reduce query execution time, rationally use server resources, and improve user experience. At the same time, improper organization of indexing can lead to increased memory consumption and slow down write operations. This article discusses the theoretical foundations of indexing strategies, their types, and their role in improving database efficiency. The advantages and limitations of using indexing are also analyzed. Literature review and methodology In recent years, a lot of scientific and practical research has been conducted on indexing strategies. Official documents and technical books for leading database systems (Oracle, MySQL, PostgreSQL, SQL Server) extensively cover the main types of indexing and their mechanisms of operation. Modern research deeply studies the impact of indexing strategies on query execution speed and resource utilization, as well as the index selection problem. New scientific articles present modern structures such as machine learning-based indexes (learned indexes), LSM-tree, R-tree, inverted index, their practical results and performance indicators based on real-world experiences (IBM FileNet P8, e-commerce, finance). In recent years, automatic index selection and hardware-adapted indexing strategies have also been actively studied. Modern benchmarking and evaluation methodologies (TPC-C, TPC-H, YCSB) allow for accurate and comparable measurement of indexing efficiency. ISSN: 2582-4686 SJIF 2021-3.261,SJIF 20222.889, 2024-6.875 ResearchBib IF: 9.948 / 2024 VOLUME-5, ISSUE-12 1229 An analysis of the existing scientific literature shows that indexing is one of the main technological solutions for improving database efficiency. Many researchers have studied the principles of operation of various indexing models - B-tree, B+tree, hash and bitmap indexes, and substantiated their advantages in working with large amounts of data. The literature also notes that improper use of indexing can lead to memory consumption and slow write operations. Methodology The article was analyzed based on the following methodology: • Source selection: Official documentation of leading database systems, modern technical books, scientific articles published in 2020–2025, industry reports, and real-world experience were studied. • Index types and architecture: The operating principle, advantages and limitations of traditional (B-tree, hash, bitmap, clustered, non-clustered) and modern (LSM-tree, R-tree, inverted index, learned indexes) index types were analyzed. • Performance evaluation: Indexing efficiency was measured based on standard benchmarking (TPC-C, TPC-H, YCSB) and modern experimental methodologies. • Workloads and results: Indexing strategies and their real results for OLTP, OLAP, NoSQL and distributed databases were compared. Comparative analysis, systematic approach and practical observation methods were used as the research methodology. Different indexing strategies were theoretically compared and their impact on database performance was analyzed. Results and Discussion The results of the study showed that the correct choice of indexing strategies significantly increases the overall efficiency of the database. In particular, the use of B-tree indexes for frequently searched fields reduces the execution time of SELECT queries. Hash indexes, on the other hand, give high results in searches based on exact matches, but are inefficient for intermediate searches. It was also found that the use of multi-column (composite) indexes helps to optimize complex queries. However, creating excessive indexes can slow down write operations (INSERT, UPDATE, DELETE). Therefore, the indexing strategy should be developed taking into account the tasks and load characteristics of the database Main types of indexing and their efficiency Index type Advantages Limitations / When not to use B-tree Fast, widespread for structured and intermediate queries Updates are slow for large records Hash Very fast in equality queries Not suitable for intermediate queries Bitmap Efficient, analytical queries for low-value columns Fragmentation problem if records change a lot Clustered Physically arranges the table in index order, fast search Each table can only have one Non-clustered Can have multiple tables in one table, suitable for many queries The cost of updating increases when records change. ISSN: 2582-4686 SJIF 2021-3.261,SJIF 20222.889, 2024-6.875 ResearchBib IF: 9.948 / 2024 VOLUME-5, ISSUE-12 1230 Modern and advanced indexing strategies • LSM-tree: High performance for record-rich and large-scale workloads, the main structure in NoSQL and distributed databases. • R-tree: Most efficient for spatial data, especially when used in conjunction with LSM-tree. • Inverted index: The main structure for full-text and document-oriented queries. • Composite, partial and functional indexes: Play an important role in accelerating complex and specialized queries. • Learned indexes: Based on machine learning, can perform 3 times faster and consume 10 times less memory than B-tree. Indexing performance evaluation and results • In OLTP systems: Properly selected indexes reduce query execution time by up to 70%. • OLAP and analytical queries: Adaptive indexing and memory management improved transaction latency by 24.9%, and analytical query performance by 31.7%. • Real-world experiences: Indexing on IBM FileNet P8 reduced transaction response time from 7000 ms to 200 ms (35x improvement), and CPU utilization decreased from 50–60% to 10–20%. • NoSQL and distributed databases: New frameworks (NEXT, BOURBON) based on LSM-tree and inverted index improved query speed and efficiency. • Hardware-adaptive indexing: Multi-core and GPU-based performance increased by 32.4–48.6%. Conclusion In conclusion, indexing strategies are an important factor in improving database efficiency. Properly selected and rationally applied indexes increase search speed, ensure efficient use of system resources, and enable fast response to user requests. At the same time, data volume, query type, and system load should be taken into account when planning indexing. The results of the study serve as a theoretical and practical basis for improving indexing strategies in database design and management. Indexing is one of the most important tools for improving database efficiency. While traditional indexes are reliable for OLTP and OLAP, modern approaches (LSM-tree, R-tree, inverted index, learned indexes) are showing advantages in large-scale and complex workloads. In recent years, innovations based on machine learning, automatic index selection, and hardware adaptation have significantly increased indexing efficiency. When choosing an indexing strategy, workload, data volume, hardware configuration, and index update costs should be taken into account. Modern approaches and automated index selection tools allow you to maximize database efficiency. References: 1.Silberschatz A., Korth H.F., Sudarshan S. Database System Concepts. 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