Query Performance Optimization in MySQL Databases: An Experimental Analysis of Normalization and Indexing

Abstract

The rapid growth of information systems has increased the demand for database architectures that are able to provide fast, accurate, consistent, and efficient data access. However, many small- and medium-scale information systems still encounter performance problems caused by suboptimal database design, including data redundancy, update anomalies, inconsistent records, and slow query execution. These problems become more critical when the volume of stored data increases and database transactions involve frequent retrieval, insertion, and updating operations. This study aims to analyze the effect of normalization and indexing techniques on database performance optimization in a relational database environment. An experimental method was applied by comparing query execution performance before and after optimization. The experiment was conducted using MySQL as the database management system with a dataset consisting of 50,000 records. Several query operations were tested, including SELECT, INSERT, and UPDATE, to evaluate the impact of structural design improvement and index implementation on query response time. The results indicate that normalization improves database structure by reducing redundancy, minimizing data anomalies, and supporting better data consistency. In addition, indexing substantially improves retrieval performance, particularly in SELECT operations, with query execution time reduced by up to 70% after optimization. Nevertheless, the implementation of indexes must be carefully planned because excessive or inappropriate indexing may increase storage requirements and affect write operations. This study contributes practical insights for database designers, developers, and system administrators in optimizing relational database performance through appropriate schema design and indexing strategies, particularly for small- and medium-scale information systems.