Access efficient Bloom Filters with TinySet

Gil Einziger, Roy Friedman

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Bloom filters are a very popular and efficient data structure for approximate set membership queries. However, Bloom filters have several key limitations as they require 44% more space than the lower bound, their operations access multiple memory words, and they do not support removals. This work presents TinySet, an alternative Bloom filter construction that is more space efficient than Bloom filters for false-positive rates smaller than 2.8% and accesses only a single memory word and partially supports removals. TinySet is mathematically analyzed and extensively tested and is shown to be fast and more space efficient than a variety of Bloom filter variants. TinySet also has low sensitivity to configuration parameters and is therefore more flexible than a Bloom filter.

Original languageEnglish
Title of host publicationAdvances in Computer Communications and Networks From Green, Mobile, Pervasive Networking to Big Data Computing
PublisherRiver Publishers
Number of pages30
ISBN (Electronic)9788793379886
ISBN (Print)9788793379879
StatePublished - 1 Feb 2017
Externally publishedYes


  • Bloom filter
  • Compact hash table
  • Network services approximate set membership

ASJC Scopus subject areas

  • Computer Science (all)
  • Engineering (all)


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