InfiniFilter: Expanding Filters to Infinity and Beyond

Author:

Dayan Niv1ORCID,Bercea Ioana2ORCID,Reviriego Pedro3ORCID,Pagh Rasmus4ORCID

Affiliation:

1. University of Toronto, Toronto, Denmark

2. IT University of Copenhagen, Copenhagen, Denmark

3. Universidad Politecnica de Madrid, Madrid, Denmark

4. University of Copenhagen, Copenhagen, Denmark

Abstract

Filter data structures have been used ubiquitously since the 1970s to answer approximate set-membership queries in various areas of computer science including architecture, networks, operating systems, and databases. Such filters need to be allocated with a given capacity in advance to provide a guarantee over the false positive rate. In many applications, however, the data size is not known in advance, requiring filters to dynamically expand. This paper shows that existing methods for expanding filters exhibit at least one of the following flaws: (1) they entail an expensive scan over the whole data set, (2) they require a lavish memory footprint, (3) their query, delete and/or insertion performance plummets, (4) their false positive rate skyrockets, and/or (5)~they cannot expand indefinitely. We introduce InfiniFilter, a new method for expanding filters that addresses these shortcomings. InfiniFilter is a hash table that stores a fingerprint for each entry. It doubles in size when it reaches capacity, and it sacrifices one bit from each fingerprint to map it to the expanded hash table. The core novelty is a new and flexible hash slot format that sets longer fingerprints to newer entries. This keeps the average fingerprint length long and thus the false positive rate stable. At the same time, InfiniFilter provides stable insertion/query/delete performance as it is comprised of a unified hash table. We implement InfiniFilter on top of Quotient Filter, and we demonstrate theoretically and empirically that it offers superior cost properties compared to existing methods: it better scales performance, the false positive rate, and the memory footprint, all at the same time.

Funder

Agencia Estatal de Investigación

Madrid Community research project TAPIR-CM

Villum Fonden

Publisher

Association for Computing Machinery (ACM)

Reference92 articles.

1. Paulo Sé rgio Almeida , Carlos Baquero , Nuno Preguicc a, and David Hutchison . 2007 . Scalable Bloom Filters. Inform. Process. Lett . (2007). Paulo Sé rgio Almeida, Carlos Baquero, Nuno Preguicc a, and David Hutchison. 2007. Scalable Bloom Filters. Inform. Process. Lett. (2007).

2. FAWN

3. Apache. 2023 a. Cassandra. http://cassandra.apache.org (2023). Apache. 2023 a. Cassandra. http://cassandra.apache.org (2023).

4. Apache. 2023 b. HBase. http://hbase.apache.org/ (2023). Apache. 2023 b. HBase. http://hbase.apache.org/ (2023).

5. LinkBench

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. On the Security of Quotient Filters: Attacks and Potential Countermeasures;IEEE Transactions on Computers;2024-09

2. Optimizing Collections of Bloom Filters within a Space Budget;Proceedings of the VLDB Endowment;2024-07

3. Wayfinder: Speeding up Key-Value Separation by Avoiding I/O Based Indirection;Proceedings of the 2nd Workshop on Simplicity in Management of Data;2024-06-14

4. Beyond Bloom: A Tutorial on Future Feature-Rich Filters;Companion of the 2024 International Conference on Management of Data;2024-06-09

5. GRF: A Global Range Filter for LSM-Trees with Shape Encoding;Proceedings of the ACM on Management of Data;2024-05-29

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3