Counting with Tinytable: Every Bit Counts!

Gil Einziger; Roy Friedman

doi:10.1109/ACCESS.2019.2925030

Counting with Tinytable: Every Bit Counts!

Gil Einziger, Roy Friedman

Department of Computer Science

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

Abstract

Bloom filters are space efficient data structures that support approximate membership queries. They are easily extensible but incur significant overheads when extended to support additional functionality, such as removals or counting. This paper shows that fingerprint-based hash tables offer a much better tradeoff between accuracy and space. We present TinyTable that supports set membership, removals, and multiplicity queries. TinyTable reduces the required memory by as much as 28% compared to Bloom filter-based variants for the set membership and by as much as 60% for counting and statistics. It is more compact than Bloom filters as long as the false positive ratio is less than 1%.

Original language	English
Article number	8746264
Pages (from-to)	166292-166309
Number of pages	18
Journal	IEEE Access
Volume	7
DOIs	https://doi.org/10.1109/ACCESS.2019.2925030
State	Published - 1 Jan 2019

Keywords

Bloom filters
approximation algorithms
compact hash tables
database
datastructure
distributed networks
networks
storage systems

ASJC Scopus subject areas

General Computer Science
General Materials Science
General Engineering

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10.1109/ACCESS.2019.2925030

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title = "Counting with Tinytable: Every Bit Counts!",

abstract = "Bloom filters are space efficient data structures that support approximate membership queries. They are easily extensible but incur significant overheads when extended to support additional functionality, such as removals or counting. This paper shows that fingerprint-based hash tables offer a much better tradeoff between accuracy and space. We present TinyTable that supports set membership, removals, and multiplicity queries. TinyTable reduces the required memory by as much as 28% compared to Bloom filter-based variants for the set membership and by as much as 60% for counting and statistics. It is more compact than Bloom filters as long as the false positive ratio is less than 1%.",

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AB - Bloom filters are space efficient data structures that support approximate membership queries. They are easily extensible but incur significant overheads when extended to support additional functionality, such as removals or counting. This paper shows that fingerprint-based hash tables offer a much better tradeoff between accuracy and space. We present TinyTable that supports set membership, removals, and multiplicity queries. TinyTable reduces the required memory by as much as 28% compared to Bloom filter-based variants for the set membership and by as much as 60% for counting and statistics. It is more compact than Bloom filters as long as the false positive ratio is less than 1%.

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KW - approximation algorithms

KW - compact hash tables

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KW - datastructure

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KW - storage systems

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Counting with Tinytable: Every Bit Counts!

Abstract

Keywords

ASJC Scopus subject areas

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