Stretching Your Data With Taffy Filters

TL;DR

This paper introduces new filter designs that can grow dynamically without failing or increasing false positive rates, addressing limitations of traditional Bloom and cuckoo filters.

Contribution

It proposes and implements scalable filters that expand seamlessly, maintaining false positive probabilities, with open-source code available.

Findings

Filters can grow without insert failures

False positive rates remain stable during growth

Open-source implementation provided

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 inserted into the structure; it also returns true with some probability $0 < \varepsilon < 1$ on elements not inserted into 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 inserts to be performed, and cannot grow larger - inserts beyond this number fail or significantly increase the false positive probability. This paper 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.