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Limousine: Blending Learned and Classical Indexes to Self-Design Larger-than-Memory Cloud Storage Engines
Summary: Limousine is a self-designing key-value engine that morphs to near-optimal architecture for a workload by blending learned and classical data structures across clouds. A distribution-aware I/O model navigates a quindecillion design space to yield Pareto-optimal configs and auto-generates Rust, beating RocksDB/WiredTiger/FASTER/Cosine on YCSB by up to 1000x.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
h84fc705745c9d182
Venue
SIGMOD
Year
2024
Pagerank
5.2825944e-05
Overall Rank
8,638 | 42.10%
DOI
10.1145/3639302
Incoming Non-self Citations Over Time
BibTeX Citation
Copy BibTeX
@inproceedings{chatterjee_sigmod24,
title = {{Limousine: Blending Learned and Classical Indexes to Self-Design Larger-than-Memory Cloud Storage Engines}},
author = {Chatterjee, Subarna and Pekala, Mark F. and Kruglyak, Lev and Idreos, Stratos},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3639302},
url = {https://dl.acm.org/doi/10.1145/3639302},
year = {2024}
}
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 36 of 36 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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