On the Choice of General Purpose Classifiers in Learned Bloom Filters: An Initial Analysis Within Basic Filters

TL;DR

This paper investigates the impact of different classifiers on Learned Bloom Filters, providing initial guidelines for selecting the most suitable classifier among five classic paradigms to optimize performance.

Contribution

It offers the first systematic analysis of classifier choices in Learned Bloom Filters and proposes initial guidelines for classifier selection based on performance considerations.

Findings

Analyzed five classic classifiers for Learned Bloom Filters

Provided initial guidelines for classifier selection

Highlighted the impact of classifier choice on filter performance

Abstract

Bloom Filters are a fundamental and pervasive data structure. Within the growing area of Learned Data Structures, several Learned versions of Bloom Filters have been considered, yielding advantages over classic Filters. Each of them uses a classifier, which is the Learned part of the data structure. Although it has a central role in those new filters, and its space footprint as well as classification time may affect the performance of the Learned Filter, no systematic study of which specific classifier to use in which circumstances is available. We report progress in this area here, providing also initial guidelines on which classifier to choose among five classic classification paradigms.