Query-Aware Vector Indexing for Real-Time Business Intelligence at Petabyte Scale

Abstract

In enterprise settings, the advent of petabyte-scale analytical workloads has highlighted the need to bridge the gap between traditional relational data warehousing and the semantic query requirements of today’s enterprise business intelligence (BI) pipelines. In this paper, we provide a thorough overview of a set of query-aware vector indexing strategies that can meet the conflicting demands of sub-millisecond response time and near-perfect recall in a petabyte-scale distributed BI data architecture. The seven benchmark algorithms (including locality-sensitive hashing (LSH), hierarchical navigable small world (HNSW) graphs, navigating spread-out graphs (NSG), disk-resident approximate nearest neighbor (DiskANN) structures, GPU-accelerated FAISS with inverted file indices (IVF), and anisotropic vector quantization (ScaNN)) are then systematically analyzed on a range of datasets from 1 million to 1 billion high-dimensional vectors. Experimental results based on the ANN-Benchmarks framework prove that HNSW is able to return a recall@10 score of 0.995 in 1.2 ms and mean latency on SIFT-1M, while ScaNN returns a recall score of 0.997 with a mean latency of 0.9 ms, reducing the memory overhead over HNSW by 75.3% using anisotropic quantization. Different approaches are discussed for integrating these indices into Apache Spark-based BI stacks, Procella, and Mesa, such as query routing policies and dynamic quantization. According to cost-benefit analysis, the hybrid cloud deployment offers a cost savings of $440 per 10,000 QPS on comparison with the cost of the GPU cluster alternatives. The synthesis provides the rules for building query-aware, petabyte-tolerant vector indices that combine the benefits of analytical throughput with semantic search precision.