Stretching your data with taffy filters. (2nd August 2022)

• Record Type: Journal Article
• Authors: Apple, Jim
• Is Part Of: Software, practice & experience. Volume 52:Number 11(2022)
• Page Start: 2349
• Page End: 2367
• Publication Date: 2022-08-02
• Subjects: Bloom filters -- dictionaries -- hash tables; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.3129
• Languages: English
• ISSNs: 0038-0644

Abstract

Abstract: Popular approximate membership query structures such as Bloom filters and cuckoo filters are widely used in databases, security, and networking. These structures represent sets approximately, and support at least two operations—insert and lookup; lookup always returns true on elements in the structure; it also returns true with some probability ε $$ \varepsilon $$ on elements not in the structure. These latter elements are called false positives. Compensatory for these false positives, filters can be much smaller than hash tables that represent the same set. However, unlike hash tables, cuckoo filters, and Bloom filters must be initialized with the intended number of maximum entries, and cannot grow larger—inserts beyond this number fail or significantly increase the false positive probability. This article presents designs and implementations of filters than can grow without inserts failing and without meaningfully increasing the false positive probability, even if the filters are created with a small initial size. The resulting code is available on GitHub under a permissive open source license.