论文标题
学习的基于Google尺寸磁盘数据库的索引
Learned Indexes for a Google-scale Disk-based Database
论文作者
论文摘要
关于学习的索引结构令人兴奋，但人们对一种新方法的实用性可以理解的怀疑，将几十年来对BTrees进行研究。在本文中，我们通过证明如何将学习索引集成到分布式的，基于磁盘的数据库系统中：Google的Bigtable来消除一些不确定性。我们详细介绍了我们做出的几项设计决策，以集成在Boogtable中的学习索引。我们的结果表明，集成学习的指数可显着改善端到端的读取延迟和吞吐量。
There is great excitement about learned index structures, but understandable skepticism about the practicality of a new method uprooting decades of research on B-Trees. In this paper, we work to remove some of that uncertainty by demonstrating how a learned index can be integrated in a distributed, disk-based database system: Google's Bigtable. We detail several design decisions we made to integrate learned indexes in Bigtable. Our results show that integrating learned index significantly improves the end-to-end read latency and throughput for Bigtable.
