HQANN: Efficient And Robust Similarity Search For Hybrid Queries With Structured And Unstructured Constraints
2022 · Wei Wu, Junlin He, Yu Qiao, et al.
Abstract
The in-memory approximate nearest neighbor search (ANNS) algorithms have achieved great success for fast high-recall query processing, but are extremely inefficient when handling hybrid queries with unstructured (i.e., feature vectors) and structured (i.e., related attributes) constraints. In this paper, we present HQANN, a simple yet highly efficient hybrid query processing framework which can be easily embedded into existing proximity graph-based ANNS algorithms. We guarantee both low latency and high recall by leveraging navigation sense among attributes and fusing vector similarity search with attribute filtering. Experimental results on both public and in-house datasets demonstrate that HQANN is 10x faster than the state-of-the-art hybrid ANNS solutions to reach the same recall quality and its performance is hardly affected by the complexity of attributes. It can reach 99% recall@10 in just around 50 microseconds On GLOVE-1.2M with thousands of attribute constraints.
Authors
(none)
Tags
Stats
Related papers
- Navigable Proximity Graph-driven Native Hybrid Queries With Structured And Unstructured Constraints (2022)0.00
- Efficient And Effective Retrieval Of Dense-sparse Hybrid Vectors Using Graph-based Approximate Nearest Neighbor Search (2024)0.00
- CHASE: A Native Relational Database For Hybrid Queries On Structured And Unstructured Data (2025)0.00
- Frequency-aware Graph Construction And Search For Dynamic Vector Databases (2025)0.00
- DEG: Efficient Hybrid Vector Search Using The Dynamic Edge Navigation Graph (2025)6.34
- UNIFY: Unified Index For Range Filtered Approximate Nearest Neighbors Search (2024)3.58
- SPANN: Highly-efficient Billion-scale Approximate Nearest Neighbor Search (2021)0.00
- Fusionanns: An Efficient CPU/GPU Cooperative Processing Architecture For Billion-scale Approximate Nearest Neighbor Search (2024)0.00