SplinterDB and Maplets: Improving the Tradeoffs in Key-Value Store Compaction Policy
Summary: Replaces filters with quotient maplets, lossy metadata that resizes without touching data, decoupling data compaction from metadata. Matches SplinterDB insertion and beats RocksDB throughput, enabling scalable RAM-based queries via quotient maplets and targeted reclamation. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Alex Conway (VMware)
- 2. Martín Farach-Colton (Rutgers University)
- 3. Rob Johnson (VMware)
BibTeX Citation
@inproceedings{conway_sigmod23,
title = {{SplinterDB and Maplets: Improving the Tradeoffs in Key-Value Store Compaction Policy}},
author = {Conway, Alex and Farach-Colton, Martín and Johnson, Rob},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3588726},
url = {https://dl.acm.org/doi/10.1145/3588726},
year = {2023}
}
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