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arXiv · 2502.20233

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

Daniela Böhm·Georg Gottlob·Matthias Lanzinger·Davide Longo·Cem Okulmus·Reinhard Pichler·Alexander Selzer
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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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Daniela Böhm, Georg Gottlob, Matthias Lanzinger, Davide Longo, Cem Okulmus, Reinhard Pichler, Alexander Selzer. 2025-02-27. Selective Use of Yannakakis' Algorithm to Improve Query Performance: Machine Learning to the Rescue. https://arxiv.org/abs/2502.20233

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