SOSD: A Benchmark for Learned Indexes
A groundswell of recent work has focused on improving data management systems with learned components. Specifically, work on learned index structures has proposed replacing traditional index structures, such as B trees, with learned models. Given the decades of research committed to improving index structures, there is significant skepticism about whether learned indexes actually outperform state of the art implementations of traditional structures on real world data. To answer this question, we propose a new benchmarking framework that comes with a variety of real world datasets and baseline implementations to compare against. We also show preliminary results for selected index structures, and find that learned models indeed often outperform state of the art implementations, and are therefore a promising direction for future research.
ArXiv