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)
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Outgoing Citations (Sorted by Pagerank)
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