LANNS: A Web-Scale Approximate Nearest Neighbor Lookup System
This addresses the scalability bottleneck in nearest neighbor search for applications in information retrieval, computer vision, and machine learning, though it is incremental over existing methods like HNSW.
The paper tackles the problem of scaling approximate nearest neighbor search to web-scale datasets, proposing LANNS, which achieves a latency of a few milliseconds per query and a throughput of 2.5k QPS on a single node for datasets with up to 180M high-dimensional points.
Nearest neighbor search (NNS) has a wide range of applications in information retrieval, computer vision, machine learning, databases, and other areas. Existing state-of-the-art algorithm for nearest neighbor search, Hierarchical Navigable Small World Networks(HNSW), is unable to scale to large datasets of 100M records in high dimensions. In this paper, we propose LANNS, an end-to-end platform for Approximate Nearest Neighbor Search, which scales for web-scale datasets. Library for Large Scale Approximate Nearest Neighbor Search (LANNS) is deployed in multiple production systems for identifying topK ($100 \leq topK \leq 200$) approximate nearest neighbors with a latency of a few milliseconds per query, high throughput of 2.5k Queries Per Second (QPS) on a single node, on large ($\sim$180M data points) high dimensional (50-2048 dimensional) datasets.