传递火炬：高吞吐量分布式磁盘基向量搜索与BatANN
Passing the Baton: High Throughput Distributed Disk-Based Vector Search with BatANN
AI总结：
本文提出BatANN，一种基于磁盘的分布式近似最近邻搜索系统，通过在远程机器上执行查询以提升局部性，实现高吞吐量和低延迟的向量搜索。
摘要：
向量搜索是现代信息检索系统的基础，包括检索增强生成（RAG）流水线和无结构文本和图像的搜索引擎。随着数据集扩展到数十亿个向量，基于磁盘的向量搜索已成为实用解决方案。然而，未来需要处理超出单个服务器容量的数据集和超出本地SSD限制的吞吐量需求。我们提出了BatANN，一种分布式磁盘基近似最近邻（ANN）系统，保留了单个全局图的对数搜索效率，同时在服务器数量上实现近线性吞吐量扩展。我们的核心创新是当访问存储在另一台机器上的邻域时，将查询的完整状态发送到另一台机器继续执行以提升局部性。在10台服务器上使用0.95召回率的10亿点数据集上，BatANN在散装-聚集基准上实现了3.5-5.59倍的提升，在DistributedANN上实现了1.44-2.09倍的吞吐量，同时保持平均延迟低于3毫秒。此外，我们使用标准TCP实现了这些结果。据我们所知，BatANN是首个基于单个全局图运行的开源分布式磁盘基向量搜索系统。
英文摘要：
Vector search underpins modern information-retrieval systems, including retrieval-augmented generation (RAG) pipelines and search engines over unstructured text and images. As datasets scale to billions of vectors, disk-based vector search has emerged as a practical solution. However, looking to the future, we must anticipate datasets too large for any single server and throughput demands that exceed the limits of locally attached SSDs. We present BatANN, a distributed disk-based approximate nearest neighbor (ANN) system that retains the logarithmic search efficiency of a single global graph while achieving near-linear throughput scaling in the number of servers. Our core innovation is that when accessing a neighborhood which is stored on another machine, we send the full state of the query to the other machine to continue executing there for improved locality. On 1B-point datasets at 0.95 recall using 10 servers, BatANN achieves 3.5-5.59x of the scatter-gather baseline and 1.44-2.09x the throughput of DistributedANN, respectively, while maintaining mean latency below 3 ms. Moreover, we get these results on standard TCP. To our knowledge, BatANN is the first open-source distributed disk-based vector search system to operate over a single global graph.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
