VIDEX: A Disaggregated and Extensible Virtual Index for the Cloud and AI Era
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913862030196736 |
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| author | Kang, Rong Wang, Shuai Zhang, Tieying Xu, Xianghong Xu, Linhui Liang, Zhimin Zhang, Lei Shi, Rui Chen, Jianjun |
| author_facet | Kang, Rong Wang, Shuai Zhang, Tieying Xu, Xianghong Xu, Linhui Liang, Zhimin Zhang, Lei Shi, Rui Chen, Jianjun |
| contents | Virtual index, also known as hypothetical indexes, play a crucial role in database query optimization. However, with the rapid advancement of cloud computing and AI-driven models for database optimization, traditional virtual index approaches face significant challenges. Cloud-native environments often prohibit direct conducting query optimization process on production databases due to stability requirements and data privacy concerns. Moreover, while AI models show promising progress, their integration with database systems poses challenges in system complexity, inference acceleration, and model hot updates. In this paper, we present VIDEX, a three-layer disaggregated architecture that decouples database instances, the virtual index optimizer, and algorithm services, providing standardized interfaces for AI model integration. Users can configure VIDEX by either collecting production statistics or by loading from a prepared file; this setup allows for high-accurate what-if analyses based on virtual indexes, achieving query plans that are identical to those of the production instance. Additionally, users can freely integrate new AI-driven algorithms into VIDEX. VIDEX has been successfully deployed at ByteDance, serving thousands of MySQL instances daily and over millions of SQL queries for index optimization tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_23776 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VIDEX: A Disaggregated and Extensible Virtual Index for the Cloud and AI Era Kang, Rong Wang, Shuai Zhang, Tieying Xu, Xianghong Xu, Linhui Liang, Zhimin Zhang, Lei Shi, Rui Chen, Jianjun Databases Virtual index, also known as hypothetical indexes, play a crucial role in database query optimization. However, with the rapid advancement of cloud computing and AI-driven models for database optimization, traditional virtual index approaches face significant challenges. Cloud-native environments often prohibit direct conducting query optimization process on production databases due to stability requirements and data privacy concerns. Moreover, while AI models show promising progress, their integration with database systems poses challenges in system complexity, inference acceleration, and model hot updates. In this paper, we present VIDEX, a three-layer disaggregated architecture that decouples database instances, the virtual index optimizer, and algorithm services, providing standardized interfaces for AI model integration. Users can configure VIDEX by either collecting production statistics or by loading from a prepared file; this setup allows for high-accurate what-if analyses based on virtual indexes, achieving query plans that are identical to those of the production instance. Additionally, users can freely integrate new AI-driven algorithms into VIDEX. VIDEX has been successfully deployed at ByteDance, serving thousands of MySQL instances daily and over millions of SQL queries for index optimization tasks. |
| title | VIDEX: A Disaggregated and Extensible Virtual Index for the Cloud and AI Era |
| topic | Databases |
| url | https://arxiv.org/abs/2503.23776 |