Optimizing Database Joins

Cost Models and Benchmarking for CPU and GPU Systems

Master thesis (2024)

Authors

M. Matušovič Electrical Engineering, Mathematics and Computer Science

Contributors

R. Hai Web Information Systems - EEMCS (mentor)
C. Lofi Web Information Systems - EEMCS (graduation committee member)
Jérémie Decouchant Data-Intensive Systems - EEMCS (graduation committee member)
W. Sun Web Information Systems - EEMCS (coach)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
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Published Date

25-04-2024

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Optimizing SQL query execution through effective cost models is a critical challenge in database management systems (DBMS). This thesis introduces a modular benchmarking system for cost models, with a pluggable architecture for both cost models and execution engines, enabling comprehensive benchmarking across various scenarios. Accompanied by a detailed methodology for the empirical measurement of cost model performance across different execution engines, a standardized approach is established, ensuring consistent and reproducible benchmarks. Furthermore, as a showcase of the developed system's capabilities, an analysis of key features influencing join-order optimization performance in both CPU and GPU systems is presented. This analysis demonstrates the system's utility in developing more effective cost models and optimizers. These contributions pave the way for future research in DBMS optimization, providing a research platform for the accelerated development of new cost models.