Selective Use of Yannakakis' Algorithm to Improve Query Performance: Machine Learning to the Rescue

• URL: http://arxiv.org/abs/2502.20233v1
• Date: Thu, 27 Feb 2025 16:19:54 GMT
• Title: Selective Use of Yannakakis' Algorithm to Improve Query Performance: Machine Learning to the Rescue
• Authors: Daniela Böhm, Georg Gottlob, Matthias Lanzinger, Davide Longo, Cem Okulmus, Reinhard Pichler, Alexander Selzer,
• Abstract summary: We propose a methodology for designing a decision procedure that decides for a given query whether the optimization technique should be applied or not.<n>We present a Machine Learning based approach for its solution.<n> Empirical results with several benchmarks on a variety of database systems show that our approach indeed leads to a statistically significant performance improvement.
• Score: 42.18649178845258
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Query optimization has played a central role in database research for decades. However, more often than not, the proposed optimization techniques lead to a performance improvement in some, but not in all, situations. Therefore, we urgently need a methodology for designing a decision procedure that decides for a given query whether the optimization technique should be applied or not. In this work, we propose such a methodology with a focus on Yannakakis-style query evaluation as our optimization technique of interest. More specifically, we formulate this decision problem as an algorithm selection problem and we present a Machine Learning based approach for its solution. Empirical results with several benchmarks on a variety of database systems show that our approach indeed leads to a statistically significant performance improvement.

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