Benchmarking learned indexes
© 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 struct...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
VLDB Endowment
2021
|
| Online Access: | https://hdl.handle.net/1721.1/132293 |
| _version_ | 1826199691226775552 |
|---|---|
| author | Marcus, R Stoian, M Kipf, A Misra, S van Renen, A Kemper, A Neumann, T Kraska, T |
| author_facet | Marcus, R Stoian, M Kipf, A Misra, S van Renen, A Kemper, A Neumann, T Kraska, T |
| author_sort | Marcus, R |
| collection | MIT |
| description | © 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. |
| first_indexed | 2024-09-23T11:24:30Z |
| format | Article |
| id | mit-1721.1/132293 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:24:30Z |
| publishDate | 2021 |
| publisher | VLDB Endowment |
| record_format | dspace |
| spelling | mit-1721.1/1322932021-09-21T03:42:42Z Benchmarking learned indexes Marcus, R Stoian, M Kipf, A Misra, S van Renen, A Kemper, A Neumann, T Kraska, T © 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. 2021-09-20T18:21:42Z 2021-09-20T18:21:42Z 2021-01-11T18:28:17Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/132293 en 10.14778/3421424.3421425 Proceedings of the VLDB Endowment Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf VLDB Endowment VLDB Endowment |
| spellingShingle | Marcus, R Stoian, M Kipf, A Misra, S van Renen, A Kemper, A Neumann, T Kraska, T Benchmarking learned indexes |
| title | Benchmarking learned indexes |
| title_full | Benchmarking learned indexes |
| title_fullStr | Benchmarking learned indexes |
| title_full_unstemmed | Benchmarking learned indexes |
| title_short | Benchmarking learned indexes |
| title_sort | benchmarking learned indexes |
| url | https://hdl.handle.net/1721.1/132293 |
| work_keys_str_mv | AT marcusr benchmarkinglearnedindexes AT stoianm benchmarkinglearnedindexes AT kipfa benchmarkinglearnedindexes AT misras benchmarkinglearnedindexes AT vanrenena benchmarkinglearnedindexes AT kempera benchmarkinglearnedindexes AT neumannt benchmarkinglearnedindexes AT kraskat benchmarkinglearnedindexes |