MINT: Multi-Vector Search Index Tuning
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910186135879680 |
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| author | Zhu, Jiongli Wang, Yue Ding, Bailu Bernstein, Philip A. Narasayya, Vivek Chaudhuri, Surajit |
| author_facet | Zhu, Jiongli Wang, Yue Ding, Bailu Bernstein, Philip A. Narasayya, Vivek Chaudhuri, Surajit |
| contents | Vector search plays a crucial role in many real-world applications. In addition to single-vector search, multi-vector search becomes important for multi-modal and multi-feature scenarios today. In a multi-vector database, each row is an item, each column represents a feature of items, and each cell is a high-dimensional vector. In multi-vector databases, the choice of indexes can have a significant impact on performance. Although index tuning for relational databases has been extensively studied, index tuning for multi-vector search remains unclear and challenging. In this paper, we define multi-vector search index tuning and propose a framework to solve it. Specifically, given a multi-vector search workload, we develop algorithms to find indexes that minimize latency and meet storage and recall constraints. Compared to the baseline, our latency achieves 2.1X to 8.3X speedup. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_20018 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MINT: Multi-Vector Search Index Tuning Zhu, Jiongli Wang, Yue Ding, Bailu Bernstein, Philip A. Narasayya, Vivek Chaudhuri, Surajit Databases Artificial Intelligence Vector search plays a crucial role in many real-world applications. In addition to single-vector search, multi-vector search becomes important for multi-modal and multi-feature scenarios today. In a multi-vector database, each row is an item, each column represents a feature of items, and each cell is a high-dimensional vector. In multi-vector databases, the choice of indexes can have a significant impact on performance. Although index tuning for relational databases has been extensively studied, index tuning for multi-vector search remains unclear and challenging. In this paper, we define multi-vector search index tuning and propose a framework to solve it. Specifically, given a multi-vector search workload, we develop algorithms to find indexes that minimize latency and meet storage and recall constraints. Compared to the baseline, our latency achieves 2.1X to 8.3X speedup. |
| title | MINT: Multi-Vector Search Index Tuning |
| topic | Databases Artificial Intelligence |
| url | https://arxiv.org/abs/2504.20018 |