# LANNS: A Web-Scale Approximate Nearest Neighbor Lookup System

arXiv: https://arxiv.org/abs/2010.09426 · HTML: https://mlsanity.com/paper/2010.09426/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [LiGNN: Graph Neural Networks at LinkedIn](https://mlsanity.com/paper/2402.11139/) (2024)
- [CAGRA: Highly Parallel Graph Construction and Approximate Nearest Neighbor Search for GPUs](https://mlsanity.com/paper/2308.15136/) (2023)
- [HAKES: Scalable Vector Database for Embedding Search Service](https://mlsanity.com/paper/2505.12524/) (2025)
- [Billion-scale Similarity Search Using a Hybrid Indexing Approach with Advanced Filtering](https://mlsanity.com/paper/2501.13442/) (2025)

## Abstract (arXiv)

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 ≤ topK ≤ 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 (~180M data points) high dimensional (50-2048 dimensional) datasets.