TEngineDB-V: An OLAP-Native Vector Search System for Large-$k$ Workloads at Tencent

Xufei Wu; Pengcheng Zhang; Yitong Song; Xiaobo Zhang; Anqi Liang; Kai Wang; Jijun Du; Yidi Xiong; Guangxu Cheng; Zhe Chen; Peng Chen; Guoliang Li; Xuanhe Zhou; Fan Wu

arXiv · 2608.00650

TEngineDB-V: An OLAP-Native Vector Search System for Large-$k$ Workloads at Tencent

Xufei Wu·Pengcheng Zhang·Yitong Song·Xiaobo Zhang·Anqi Liang·Kai Wang·Jijun Du·Yidi Xiong·Guangxu Cheng·Zhe Chen·Peng Chen·Guoliang Li·Xuanhe Zhou·Fan Wu

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Abstract

Vector search systems are essential infrastructure for modern data-driven applications. Large-$k$ analytical vector search, which retrieves $k=10^3$--$10^5$ results for analytics (e.g., aggregation, filtering, joins), is increasingly important for emerging workloads, including LLM data management and advertising analysis at Tencent. Existing systems remain inadequate: specialized vector databases often cap $k$ (e.g., $k \leq 10^4$) to satisfy tail-latency constraints and offer limited analytical support, while OLAP systems typically embed per-segment vector indexes as black boxes, causing severe read/compute amplification and preventing native query optimization. This paper presents TEngineDB-V, an OLAP-native vector search system for large-$k$ workloads. TEngineDB-V makes vector search a first-class analytical primitive in Tencent's OLAP engine through a global segment-decoupled index materialized as relational tables, eliminating scatter-gather execution, reducing amplification, and enabling native storage optimizations. It decomposes IVFPQ-based search into relational operators, integrates OLAP optimizations, and introduces DPPQ, which combines direction-aware quantization with hierarchical residual refinement to improve recall while preserving relational efficiency. TEngineDB-V further incorporates index-aware query rewriting and a distributed-aware cost model for efficient distributed execution. Experiments show that TEngineDB-V achieves up to a $145\times$ speedup over competitive systems such as StarRocks, and up to a $52\times$ improvement in 10-billion-scale production deployments.

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Xufei Wu, Pengcheng Zhang, Yitong Song, Xiaobo Zhang, Anqi Liang, Kai Wang, Jijun Du, Yidi Xiong, Guangxu Cheng, Zhe Chen, Peng Chen, Guoliang Li, Xuanhe Zhou, Fan Wu. 2026-08-01. TEngineDB-V: An OLAP-Native Vector Search System for Large-$k$ Workloads at Tencent. https://arxiv.org/abs/2608.00650

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