Analysis of Tools for Measuring PostgreSQL Query Cost

Abstract

Amidst the realm of database management, the precise prediction of query execution time holds paramount importance in the pursuit of performance optimization. This intricate endeavor hinges upon intricate cost models yet is full of challenges owing to the intricacies of selectivity estimation and the propensity for cost-modeling errors. In the domain of open-source databases, PostgreSQL emerges as a standout contender, distinguished by its sophisticated cost models. Delving into the crux of this matter, the present paper undertakes a comprehensive analysis utilizing a spectrum of tools such as EXPLAIN, EXPLAIN ANALYZE, PgAdmin, Dalibo, and Depesz. Through these lenses, the challenges aforementioned are meticulously dissected, revealing the nuanced landscape of query execution. Notably, PostgreSQL's trajectory in addressing these challenges is meticulously showcased, illuminating its evolutionary journey. The insights thus garnered extend their value to the intricacies of query optimization, where the judicious selection of query cost tools plays a pivotal role. By bridging theory and practice, this exposition contributes to refining database systems, equipping practitioners and researchers with invaluable knowledge for enhancing efficiency and proficiency.