Benchmarking Learned Indexes
Summary: Introduces a unified benchmark for learned indexes, evaluating three learned index families against traditional baselines on four real-world datasets. Finds that learned indexes can outperform non-learned indexes in read-only in-memory dense arrays, and analyzes caching, pipelining, size effects, multi-threading, and build times to explain their performance. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ryan Marcus (Intel; Massachusetts Institute of Technology)
- 2. Andreas Kipf (Massachusetts Institute of Technology)
- 3. Alexander van Renen (Technical University of Munich)
- 4. Mihail Stoian (Technical University of Munich)
- 5. Sanchit Misra (Intel)
- 6. Alfons Kemper (Technical University of Munich)
- 7. Thomas Neumann (Technical University of Munich)
- 8. Tim Kraska (Massachusetts Institute of Technology)
BibTeX Citation
@article{marcus_vldb21,
title = {{Benchmarking Learned Indexes}},
author = {Marcus, Ryan and Kipf, Andreas and van Renen, Alexander and Stoian, Mihail and Misra, Sanchit and Kemper, Alfons and Neumann, Thomas and Kraska, Tim},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {1},
pages = {1--13},
doi = {10.14778/3421424.3421425},
url = {https://doi.org/10.14778/3421424.3421425},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 50 of 61 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 10,566 | On Self-Designing Learned Indexes | 2026 | SIGMOD |
| 2 | 10,649 | High Performance or Low Memory? An Updatable Learned Index Framework for Time-Space Tradeoff | 2026 | SIGMOD |
| 3 | 8,401 | Tuning Hierarchical Learned Indexes on Disk and Beyond | 2022 | SIGMOD |
| 4 | 5,397 | Updatable Learned Indexes Meet Disk-Resident DBMS - From Evaluations to Design Choices | 2023 | SIGMOD |
| 5 | 6,889 | Accelerating String-key Learned Index Structures via Memoization-based Incremental Training | 2024 | VLDB |
| 6 | 9,814 | Understanding Robustness Issues of Updatable Learned Indexes: [Experiments & Analysis] | 2026 | SIGMOD |
| 7 | 40 | The Case for Learned Index Structures | 2018 | SIGMOD |
| 8 | 6,622 | Making In-Memory Learned Indexes Efficient on Disk | 2024 | SIGMOD |
| 9 | 2,590 | Are Updatable Learned Indexes Ready? | 2022 | VLDB |
| 10 | 3,632 | Learned Index: A Comprehensive Experimental Evaluation | 2023 | VLDB |