The Unreasonable Effectiveness of LLMs for Query Optimization
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866916468454588416 |
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| author | Akioyamen, Peter Yi, Zixuan Marcus, Ryan |
| author_facet | Akioyamen, Peter Yi, Zixuan Marcus, Ryan |
| contents | Recent work in database query optimization has used complex machine learning strategies, such as customized reinforcement learning schemes. Surprisingly, we show that LLM embeddings of query text contain useful semantic information for query optimization. Specifically, we show that a simple binary classifier deciding between alternative query plans, trained only on a small number of labeled embedded query vectors, can outperform existing heuristic systems. Although we only present some preliminary results, an LLM-powered query optimizer could provide significant benefits, both in terms of performance and simplicity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_02862 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Unreasonable Effectiveness of LLMs for Query Optimization Akioyamen, Peter Yi, Zixuan Marcus, Ryan Databases Machine Learning Recent work in database query optimization has used complex machine learning strategies, such as customized reinforcement learning schemes. Surprisingly, we show that LLM embeddings of query text contain useful semantic information for query optimization. Specifically, we show that a simple binary classifier deciding between alternative query plans, trained only on a small number of labeled embedded query vectors, can outperform existing heuristic systems. Although we only present some preliminary results, an LLM-powered query optimizer could provide significant benefits, both in terms of performance and simplicity. |
| title | The Unreasonable Effectiveness of LLMs for Query Optimization |
| topic | Databases Machine Learning |
| url | https://arxiv.org/abs/2411.02862 |