RadixSpline: a single-pass learned index

© 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...

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Main Authors: Kipf, Andreas, Marcus, Ryan, van Renen, Alexander, Stoian, Mihail, Kemper, Alfons, Kraska, Tim, Neumann, Thomas
Format: Article
Language:English
Published: ACM 2021
Online Access:https://hdl.handle.net/1721.1/132294
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author Kipf, Andreas
Marcus, Ryan
van Renen, Alexander
Stoian, Mihail
Kemper, Alfons
Kraska, Tim
Neumann, Thomas
author_facet Kipf, Andreas
Marcus, Ryan
van Renen, Alexander
Stoian, Mihail
Kemper, Alfons
Kraska, Tim
Neumann, Thomas
author_sort Kipf, Andreas
collection MIT
description © 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.
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spelling mit-1721.1/1322942021-09-21T03:49:19Z RadixSpline: a single-pass learned index Kipf, Andreas Marcus, Ryan van Renen, Alexander Stoian, Mihail Kemper, Alfons Kraska, Tim Neumann, Thomas © 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. 2021-09-20T18:21:43Z 2021-09-20T18:21:43Z 2021-01-11T18:20:43Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/132294 en 10.1145/3401071.3401659 Proceedings of the 3rd International Workshop on Exploiting Artificial Intelligence Techniques for Data Management, aiDM 2020 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf ACM arXiv
spellingShingle Kipf, Andreas
Marcus, Ryan
van Renen, Alexander
Stoian, Mihail
Kemper, Alfons
Kraska, Tim
Neumann, Thomas
RadixSpline: a single-pass learned index
title RadixSpline: a single-pass learned index
title_full RadixSpline: a single-pass learned index
title_fullStr RadixSpline: a single-pass learned index
title_full_unstemmed RadixSpline: a single-pass learned index
title_short RadixSpline: a single-pass learned index
title_sort radixspline a single pass learned index
url https://hdl.handle.net/1721.1/132294
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AT stoianmihail radixsplineasinglepasslearnedindex
AT kemperalfons radixsplineasinglepasslearnedindex
AT kraskatim radixsplineasinglepasslearnedindex
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