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RadixSpline: a single-pass learned index
Name
2004.14541.pdf
Description
Submitted version
Size
612.33 KB
Format
Adobe PDF
Checksum (MD5)
5544c2e34b68c6fa93700b099c2b87e3
Author(s)
Kipf, Andreas
Marcus, Ryan
van Renen, Alexander
Stoian, Mihail
Kemper, Alfons
Kraska, Tim
Neumann, Thomas
Journal
Proceedings of the 3rd International Workshop on Exploiting Artificial Intelligence Techniques for Data Management, aiDM 2020
Publisher
ACM
Version
Original manuscript
Abstract
© 2020 ACM. Recent research has shown that learned models can outperform state-of-the-art index structures in size and lookup performance. While this is a very promising result, existing learned structures are often cumbersome to implement and are slow to build. In fact, most approaches that we are aware of require multiple training passes over the data. We introduce RadixSpline (RS), a learned index that can be built in a single pass over the data and is competitive with state-of-the-art learned index models, like RMI, in size and lookup performance. We evaluate RS using the SOSD benchmark and show that it achieves competitive results on all datasets, despite the fact that it only has two parameters.
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1145/3401071.3401659
