TEngineDB-V: An OLAP-Native Vector Search System for Large-k Workloads at Tencent
Summary: TEngineDB-V makes large-k vector search a first-class OLAP primitive via a global, segment-decoupled relational index, avoiding per-segment scatter-gather amplification. Relational IVFPQ decomposition, DPPQ quantization, query rewriting, and distributed cost modeling yield up to 145× speedups. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Xufei Wu (Shanghai Jiao Tong University)
- 2. Pengcheng Zhang (Tencent)
- 3. Yitong Song (Hong Kong Baptist University)
- 4. Xiaobo Zhang (Shanghai Jiao Tong University)
- 5. Anqi Liang (Hong Kong University of Science and Technology)
- 6. Kai Wang (Shanghai Jiao Tong University)
- 7. Jijun Du (Tencent)
- 8. Yidi Xiong (Tencent)
- 9. Guangxu Cheng (Tencent)
- 10. Zhe Chen (Tencent)
- 11. Peng Chen (Tencent)
- 12. Guoliang Li (Tsinghua University)
- 13. Xuanhe Zhou (Shanghai Jiao Tong University)
- 14. Fan Wu (Shanghai Jiao Tong University)
BibTeX Citation
@article{wu_vldb26,
title = {{TEngineDB-V: An OLAP-Native Vector Search System for Large-k Workloads at Tencent}},
author = {Wu, Xufei and Zhang, Pengcheng and Song, Yitong and Zhang, Xiaobo and Liang, Anqi and Wang, Kai and Du, Jijun and Xiong, Yidi and Cheng, Guangxu and Chen, Zhe and Chen, Peng and Li, Guoliang and Zhou, Xuanhe and Wu, Fan},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {3955--3968},
doi = {10.14778/3827998.3828008},
url = {https://doi.org/10.14778/3827998.3828008},
year = {2026}
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