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.
Cite
@article{arxiv.2004.14541,
title = {RadixSpline: A Single-Pass Learned Index},
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:2004.14541},
year = {2020}
}
Comments
Third International Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM 2020)