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Proteus: A Self-Designing Range Filter

Authors:

Baptiste Lemaire,

Huanchen Zhang,

Stratos Idreos,

Michael MitzenmacherAuthors Info & Claims

SIGMOD '22: Proceedings of the 2022 International Conference on Management of Data

Pages 1670 - 1684

https://doi.org/10.1145/3514221.3526167

Published: 11 June 2022 Publication History

Abstract

We introduce Proteus, a novel self-designing approximate range filter, which configures itself based on sampled data in order to optimize its false positive rate (FPR) for a given space requirement. Proteus unifies the probabilistic and deterministic design spaces of state-of-the-art range filters to achieve robust performance across a larger variety of use cases. At the core of Proteus lies our Contextual Prefix FPR (CPFPR) model - a formal framework for the FPR of prefix-based filters across their design spaces. We empirically demonstrate the accuracy of our model and Proteus' ability to optimize over both synthetic workloads and real-world datasets. We further evaluate Proteus in RocksDB and show that it is able to improve end-to-end performance by as much as 5.3x over more brittle state-of-the-art methods such as SuRF and Rosetta. Our experiments also indicate that the cost of modeling is not significant compared to the end-to-end performance gains and that Proteus is robust to workload shifts.

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Cited By

Wang HOu JZhao MQiu SJiao YWang YMao QYang ZLiu YZhang JHu JZhang JLiu JChen JShen YCao LZhang HLi HLi MMa YZhang LLiu JZhang GLiu FChen J(2024)LavaStore: ByteDance's Purpose-Built, High-Performance, Cost-Effective Local Storage Engine for Cloud ServicesProceedings of the VLDB Endowment10.14778/3685800.368580717:12(3799-3812)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.14778/3685800.3685807
Zhang WLim WButrovich MPavlo A(2024)The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-ActionsProceedings of the VLDB Endowment10.14778/3681954.368200717:11(3373-3387)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.14778/3681954.3682007
Chen GHe ZLi MLuo S(2024)Oasis: An Optimal Disjoint Segmented Learned Range FilterProceedings of the VLDB Endowment10.14778/3659437.365944717:8(1911-1924)Online publication date: 31-May-2024
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Index Terms

Proteus: A Self-Designing Range Filter
1. Information systems
  1. Data management systems
    1. Data structures
      1. Data access methods
        Unidimensional range search
    2. Database design and models
2. Theory of computation
  1. Design and analysis of algorithms
    1. Streaming, sublinear and near linear time algorithms
      1. Bloom filters and hashing

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cover image ACM Conferences

SIGMOD '22: Proceedings of the 2022 International Conference on Management of Data

June 2022

2597 pages

ISBN:9781450392495

DOI:10.1145/3514221

General Chair:
Zachary Ives
University of Pennsylvania (USA)
,
Program Chairs:
Angela Bonifati
Lyon 1 University (France)
,
Amr El Abbadi
University of California, Santa Barbara (USA)

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Published: 11 June 2022

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Wang HOu JZhao MQiu SJiao YWang YMao QYang ZLiu YZhang JHu JZhang JLiu JChen JShen YCao LZhang HLi HLi MMa YZhang LLiu JZhang GLiu FChen J(2024)LavaStore: ByteDance's Purpose-Built, High-Performance, Cost-Effective Local Storage Engine for Cloud ServicesProceedings of the VLDB Endowment10.14778/3685800.368580717:12(3799-3812)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.14778/3685800.3685807
Zhang WLim WButrovich MPavlo A(2024)The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-ActionsProceedings of the VLDB Endowment10.14778/3681954.368200717:11(3373-3387)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.14778/3681954.3682007
Chen GHe ZLi MLuo S(2024)Oasis: An Optimal Disjoint Segmented Learned Range FilterProceedings of the VLDB Endowment10.14778/3659437.365944717:8(1911-1924)Online publication date: 31-May-2024
https://dl.acm.org/doi/10.14778/3659437.3659447
Yu WLuo SYu ZCong G(2024)CAMAL: Optimizing LSM-trees via Active LearningProceedings of the ACM on Management of Data10.1145/36771382:4(1-26)Online publication date: 30-Sep-2024
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Liu JWang FMo DLuo S(2024)Structural Designs Meet Optimality: Exploring Optimized LSM-tree Structures in a Colossal Configuration SpaceProceedings of the ACM on Management of Data10.1145/36549782:3(1-26)Online publication date: 30-May-2024
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Wang HGuo TYang JZhang H(2024)GRF: A Global Range Filter for LSM-Trees with Shape EncodingProceedings of the ACM on Management of Data10.1145/36549442:3(1-27)Online publication date: 30-May-2024
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Costa MFerragina PVinciguerra G(2024)Grafite: Taming Adversarial Queries with Optimal Range FiltersProceedings of the ACM on Management of Data10.1145/36392582:1(1-23)Online publication date: 26-Mar-2024
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Pandey PFarach-Colton MDayan NZhang HBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)Beyond Bloom: A Tutorial on Future Feature-Rich FiltersCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3654681(636-644)Online publication date: 9-Jun-2024
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Han SLiu XLi J(2024)MulRF: A Multi-Dimensional Range Filter for Sublinear Time Range Query ProcessingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.339731336:11(6600-6613)Online publication date: Nov-2024
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