Filtered ANN Search With Composite Vector Indexes (Part 4)

Part four of a series on composite vector indexing in Couchbase delves into the performance analysis of Composite Vector Indexes (CVI) for filtered Approximate Nearest Neighbor (ANN) workloads, addressing key metrics such as throughput and latency in large-scale datasets. The post highlights CVI's ability to handle large-scale indexing efficiently, demonstrated by an internal benchmark where 1 billion 128-dimensional vectors were indexed in 7 hours using modern hardware. CVI's performance is enhanced by features like order-aware scanning, parallel processing, SIMD-accelerated computations, and the HNSW routing layer, which collectively optimize vector search and reduce computational overhead. The performance benefits are illustrated by significant improvements in throughput and latency as selectivity narrows, with notable results from the 100M SIFT dataset achieving 75% recall@10. These advancements make CVI appealing for applications with inherent constraints, such as e-commerce recommendations and fraud detection, by combining scalar constraints with semantic similarity in a single index structure.

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