Nova: A Multi-Purpose Vector Engine for Low-Latency, Multi-Tenant, and Cross-Table Hybrid Retrieval
Summary: Nova’s dual-layer delta–base architecture decouples ingestion and indexing, providing immediate visibility and lock-free, low-latency queries under bursty, multi-tenant workloads. As a relational optimizer citizen, it supports cross-table hybrid retrieval via adaptive joins and tiered memory/disk storage. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Dechuang Chen (Chinese University of Hong Kong)
- 2. Bing Chen (Alibaba)
- 3. Fangyuan Zhang (Harbin Engineering University)
- 4. Sibo Wang (Chinese University of Hong Kong)
- 5. Jianwei Lu (Alibaba)
- 6. Zhiwei Wu (Alibaba)
- 7. Zeyu Yang (Alibaba)
- 8. Qiang Gu (Alibaba)
- 9. Zeyuan Yu (Alibaba)
- 10. Caihua Yin (Alibaba)
- 11. Wenchao Zhou (Alibaba)
- 12. Feifei Li (Alibaba)
BibTeX Citation
@article{chen_vldb26,
title = {{Nova: A Multi-Purpose Vector Engine for Low-Latency, Multi-Tenant, and Cross-Table Hybrid Retrieval}},
author = {Chen, Dechuang and Chen, Bing and Zhang, Fangyuan and Wang, Sibo and Lu, Jianwei and Wu, Zhiwei and Yang, Zeyu and Gu, Qiang and Yu, Zeyuan and Yin, Caihua and Zhou, Wenchao and Li, Feifei},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {4453--4465},
doi = {10.14778/3827998.3828045},
url = {https://doi.org/10.14778/3827998.3828045},
year = {2026}
}
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