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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)

Paper ID
12291
Venue
VLDB
Year
2021
Pagerank
0.00011965956
Overall Rank
1,438 | 90.01%
DOI
10.14778/3421424.3421425

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