The Selection Problem in Multi-Query Optimization: a Comprehensive Survey
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910800818470912 |
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| author | Zinchenko, Sergey Ponomaryov, Denis |
| author_facet | Zinchenko, Sergey Ponomaryov, Denis |
| contents | View materialization, index selection, and plan caching are well-known techniques for optimization of query processing in database systems. The essence of these tasks is to select and save a subset of the most useful candidates (views/indexes/plans) for reuse within given space/time budget constraints. In this paper, we propose a unified view on these selection problems. We make a detailed analysis of the root causes of their complexity and summarize techniques to address them. Our survey provides a modern classification of selection algorithms known in the literature, including the latest ones based on Machine Learning. We provide a ground for reuse of the selection techniques between different optimization scenarios and highlight challenges and promising directions in the field. Based on our analysis we derive a method to exponentially accelerate some of the state-of-the-art selection algorithms. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_11828 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Selection Problem in Multi-Query Optimization: a Comprehensive Survey Zinchenko, Sergey Ponomaryov, Denis Databases Discrete Mathematics View materialization, index selection, and plan caching are well-known techniques for optimization of query processing in database systems. The essence of these tasks is to select and save a subset of the most useful candidates (views/indexes/plans) for reuse within given space/time budget constraints. In this paper, we propose a unified view on these selection problems. We make a detailed analysis of the root causes of their complexity and summarize techniques to address them. Our survey provides a modern classification of selection algorithms known in the literature, including the latest ones based on Machine Learning. We provide a ground for reuse of the selection techniques between different optimization scenarios and highlight challenges and promising directions in the field. Based on our analysis we derive a method to exponentially accelerate some of the state-of-the-art selection algorithms. |
| title | The Selection Problem in Multi-Query Optimization: a Comprehensive Survey |
| topic | Databases Discrete Mathematics |
| url | https://arxiv.org/abs/2412.11828 |