LANNS
Ishita Doshi, Dhritiman Das, Ashish Bhutani, Rajeev Kumar, Rushi Bhatt, Niranjan Balasubramanian
Abstract
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 top-K (100 ≤ k ≤ 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 (e.g., ~ 180M data points) high dimensional (50-2048 dimensional) datasets.