Rosetta: A Robust Space-Time Optimized Range Filter for Key-Value Stores
Summary: Rosetta: probabilistic range filter for LSM-tree KV stores; space-time optimized to reduce FPR. Hierarchical Bloom filters index all key prefixes; ranges map to multiple probes; workload-aware tuning; RocksDB integration; up to 40x faster range lookups and net performance gains. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Siqiang Luo (Harvard University)
- 2. Subarna Chatterjee (Harvard University)
- 3. Rafael Ketsetsidis (Harvard University)
- 4. Niv Dayan (Harvard University)
- 5. Wilson Qin (Harvard University)
- 6. Stratos Idreos (Harvard University)
BibTeX Citation
@inproceedings{luo_sigmod20,
title = {{Rosetta: A Robust Space-Time Optimized Range Filter for Key-Value Stores}},
author = {Luo, Siqiang and Chatterjee, Subarna and Ketsetsidis, Rafael and Dayan, Niv and Qin, Wilson and Idreos, Stratos},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3389731},
url = {https://dl.acm.org/doi/10.1145/3318464.3389731},
year = {2020}
}
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