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.
@article{arxiv.1911.13014,
title = {SOSD: A Benchmark for Learned Indexes},
author = {Andreas Kipf and Ryan Marcus and Alexander van Renen and Mihail Stoian and Alfons Kemper and Tim Kraska and Thomas Neumann},
journal= {arXiv preprint arXiv:1911.13014},
year = {2019}
}
Comments
NeurIPS 2019 Workshop on Machine Learning for Systems