Query Performance Evaluation in MySQL and PostgreSQL Databases

Abstract

Query performance evaluation in MySQL and PostgreSQL databases is important because enterprise applications depend on fast, reliable, and scalable SQL execution for transaction processing, reporting, and data analysis. Both database systems provide indexing, query optimization, execution plan analysis, caching, and configuration controls, but their performance behavior may differ depending on workload type, table size, join complexity, and storage design. Existing literature highlights response time, throughput, CPU usage, memory consumption, disk I/O, indexing efficiency, and optimizer behavior as major factors for comparing relational database performance. However, many organizations still face challenges in selecting the right database platform because query speed can vary under read-heavy, write-heavy, analytical, and mixed workloads. This research is important because poor database selection or weak performance tuning can delay application response, increase infrastructure cost, and reduce system reliability. This article discusses query performance evaluation in MySQL and PostgreSQL databases, focusing on benchmark workload design, query execution plans, indexing strategies, join performance, transaction handling, resource utilization, and scalability testing. The study concludes that systematic performance evaluation helps identify workload-specific strengths, supports better database tuning, improves query response time, and enables more reliable enterprise database deployment.