Approximate nearest neighbor (ANN) search in high-dimensional space plays an essential role in a variety of real-world applications. A well-known solution to ANN search, inverted file product quantization (IVFPQ) adopts inverted files to avoid exhaustive examination and compresses vectors using product quantization to reduce the space overhead. However, existing implementations use the same fixed probing cardinality (i.e., the number of cells to probe) setting for all queries, which leads to too many or too few cell examinations, thus increasing the average query latency or reducing the recall. To achieve a better trade-off between latency and accuracy, we enable probing cardinality estimation for high-dimensional ANN search by using deep learning techniques. We develop HBK-means, a hierarchical balanced clustering algorithm that reduces the data distribution imbalance of cells to enable a better estimation. Next, we develop PCE-Net, an encoder-decoder based neural network for estimating query-dependent minimum probing cardinality. In addition, we introduce two query optimization strategies: lower bound sorting based pruning (LBS-Pruning) and early termination (ET), to further reduce query latency. Extensive experiments with real-world data offer evidence that the proposed solution is capable of achieving better performance than IVFPQ and its variants.

Maximum inner product search (MIPS) in high-dimensional spaces has wide applications but is computationally expensive due to the curse of dimensionality. Existing studies employ asymmetric transformations that reduce the MIPS problem to a nearest neighbor search (NNS) problem, which can be solved using locality-sensitive hashing (LSH). However, these studies usually maintain multiple hash tables and locally examine them one by one, which may cause additional costs on probing unnecessary points. In addition, LSH is applied without taking into account the properties of the inner product. In this paper, we develop a fast search framework FARGO for MIPS on large-scale, high-dimensional data. We propose a global multi-probing (GMP) strategy that exploits the properties of the inner product to globally examine high quality candidates. In addition, we develop two optimization techniques. First, different with existing transformations that introduce either distortion errors or data distribution imbalances, we design a novel transformation, called random XBOX transformation, that avoids the negative effects of data distribution imbalances. Second, we propose a global adaptive early termination condition that finds results quickly and offers theoretical guarantees. We conduct extensive experiments with real-world data that offer evidence that FARGO is capable of outperforming existing proposals in terms of both accuracy and efficiency.

With the development of learning-based embedding models, embedding vectors are widely used for analyzing and searching unstructured data. As vector collections exceed billion-scale, fully managed and horizontally scalable vector databases are necessary. In the past three years, through interaction with our 1200+ industry users, we have sketched a vision for the features that next-generation vector databases should have, which include long-term evolvability, tunable consistency, good elasticity, and high performance. We present Manu, a cloud native vector database that implements these features. It is difficult to integrate all these features if we follow traditional DBMS design rules. As most vector data applications do not require complex data models and strong data consistency, our design philosophy is to relax the data model and consistency constraints in exchange for the aforementioned features. Specifically, Manu firstly exposes the write-ahead log (WAL) and binlog as backbone services. Secondly, write components are designed as log publishers while all read-only analytic and search components are designed as independent subscribers to the log services. Finally, we utilize multi-version concurrency control (MVCC) and a delta consistency model to simplify the communication and cooperation among the system components. These designs achieve a low coupling among the system components, which is essential for elasticity and evolution. We also extensively optimize Manu for performance and usability with hardware-aware implementations and support for complex search semantics. Manu has been used for many applications, including, but not limited to, recommendation, multimedia, language, medicine and security. We evaluated Manu in three typical application scenarios to demonstrate its efficiency, elasticity, and scalability.