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GRF: A Global Range Filter for LSM-Trees with Shape Encoding
Summary: GRF: a RocksDB global range filter that cuts per-query filter probes from many to one, targeting CPU bottlenecks in LSM read paths. Novel shape encoding makes global run-ID filters correct under MVCC and efficient to update, outperforming prior point/range filters.
(summarized by gpt-5.4-mini on May 24 2026)
Paper ID
hba6beccbec495108
Venue
SIGMOD
Year
2024
Pagerank
6.4334982e-05
Overall Rank
4,753 | 68.06%
DOI
10.1145/3654944
Incoming Non-self Citations Over Time
BibTeX Citation
@inproceedings{wang_sigmod24,
title = {{GRF: A Global Range Filter for LSM-Trees with Shape Encoding}},
author = {Wang, Hengrui and Guo, Te and Yang, Junzhao and Zhang, Huanchen},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3654944},
url = {https://dl.acm.org/doi/10.1145/3654944},
year = {2024}
}
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Showing 27 of 27 cited papers.
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SuRF: Practical Range Query Filtering with Fast Succinct Tries 2018
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Rosetta: A Robust Space-Time Optimized Range Filter for Key-Value Stores 2020
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Constructing and Analyzing the LSM Compaction Design Space 2021
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SNARF: A Learning-Enhanced Range Filter 2022
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Spooky: Granulating LSM-Tree Compactions Correctly 2022
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Performance-Optimal Filtering: Bloom Overtakes Cuckoo at High Throughput 2019
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Vector Quotient Filters: Overcoming the Time/Space Trade-Off in Filter Design 2021
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Stacked Filters: Learning to Filter by Structure 2021
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