Benchmarking learned indexes
Name
3421424.3421425.pdf
Description
Published version
Size
736.07 KB
Format
Unknown
Checksum (MD5)
1983fc38b800eec5859727401b09e87e
Author(s)
Marcus, Ryan
Stoian, Mihail
Kipf, Andreas
Misra, Sanchit
van Renen, Alexander
Kemper, Alfons
Neumann, Thomas
Kraska, Tim
Date Issued
2020
Journal
Proceedings of the VLDB Endowment
Publisher
VLDB Endowment
Version
Final published version
Abstract
© 2020, VLDB Endowment. All rights reserved. Recent advancements in learned index structures propose replacing existing index structures, like B-Trees, with approximate learned models. In this work, we present a unified benchmark that compares well-tuned implementations of three learned index structures against several state-of-the-art "traditional" baselines. Using four real-world datasets, we demonstrate that learned index structures can indeed outperform non-learned indexes in read-only in-memory workloads over a dense array. We investigate the impact of caching, pipelining, dataset size, and key size. We study the performance profile of learned index structures, and build an explanation for why learned models achieve such good performance. Finally, we investigate other important properties of learned index structures, such as their performance in multi-threaded systems and their build times.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.14778/3421424.3421425
