2026/08/01 by Xufei Wu, Pengcheng Zhang, Yitong Song +11
Computer Science · #cs.DB
arxiv created 2026/08/01 · arxiv updated 2026/08/04
Vector search systems are essential infrastructure for modern data-driven applications. Large-k analytical vector search, which retrieves k=103--105 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 ≤ 104) 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× speedup over competitive systems such as StarRocks, and up to a 52× improvement in 10-billion-scale production deployments.