Comparative analysis of query optimization techniques in modern relational database systems

This study presents a comparative analysis of query optimization techniques in modern relational database systems, focusing on B-Tree indexing, columnar storage, table partitioning, and materialized views. Evaluated across OLTP, OLAP, and HTAP workloads, results highlight trade-offs between read efficiency, write overhead, and storage utilization. Research findings demonstrate that hybrid, workload-aware strategies combining multiple techniques achieve optimal performance. The study provides guidance for database architects and identifies directions for future research in adaptive and AI-driven optimization.