Selective Use of Yannakakis' Algorithm to Improve Query Performance: Machine Learning to the Rescue
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913902776811520 |
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| author | Böhm, Daniela Gottlob, Georg Lanzinger, Matthias Longo, Davide Okulmus, Cem Pichler, Reinhard Selzer, Alexander |
| author_facet | Böhm, Daniela Gottlob, Georg Lanzinger, Matthias Longo, Davide Okulmus, Cem Pichler, Reinhard Selzer, Alexander |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_20233 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Selective Use of Yannakakis' Algorithm to Improve Query Performance: Machine Learning to the Rescue Böhm, Daniela Gottlob, Georg Lanzinger, Matthias Longo, Davide Okulmus, Cem Pichler, Reinhard Selzer, Alexander Databases Artificial Intelligence 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. |
| title | Selective Use of Yannakakis' Algorithm to Improve Query Performance: Machine Learning to the Rescue |
| topic | Databases Artificial Intelligence |
| url | https://arxiv.org/abs/2502.20233 |