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
- 2. Andreas Kipf
- 3. Alexander van Renen
- 4. Mihail Stoian
- 5. Sanchit Misra
- 6. Alfons Kemper
- 7. Thomas Neumann
- 8. Tim Kraska
Incoming Citations (Sorted by Pagerank)
Showing 5 of 55 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,484 | Femur: A Flexible Framework for Fast and Secure Querying from Public Key-Value Store | 2025 | SIGMOD | 4.1945683e-05 |
| 10,712 | DobLIX: A Dual-Objective Learned Index for Log-Structured Merge Trees | 2025 | VLDB | 4.1945683e-05 |
| 10,824 | LETIndex: A Secure Learned Index with TEE | 2025 | VLDB | 4.1945683e-05 |
| 10,949 | SWIX: A Memory-efficient Sliding Window Learned Index | 2024 | SIGMOD | 4.1945683e-05 |
| 10,960 | FairHash: A Fair and Memory/Time-efficient Hashmap | 2024 | SIGMOD | 4.1945683e-05 |
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.
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