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arXiv · 2207.04789

bloomRF: On Performing Range-Queries in Bloom-Filters with Piecewise-Monotone Hash Functions and Prefix Hashing

Bernhard Mößner·Christian Riegger·Arthur Bernhardt·Ilia Petrov
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Abstract

We introduce bloomRF as a unified method for approximate membership testing that supports both point- and range-queries. As a first core idea, bloomRF introduces novel prefix hashing to efficiently encode range information in the hash-code of the key itself. As a second key concept, bloomRF proposes novel piecewise-monotone hash-functions that preserve local order and support fast range-lookups with fewer memory accesses. bloomRF has near-optimal space complexity and constant query complexity. Although, bloomRF is designed for integer domains, it supports floating-points, and can serve as a multi-attribute filter. The evaluation in RocksDB and in a standalone library shows that it is more efficient and outperforms existing point-range-filters by up to 4x across a range of settings and distributions, while keeping the false-positive rate low.

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Bernhard Mößner, Christian Riegger, Arthur Bernhardt, Ilia Petrov. 2022-07-11. bloomRF: On Performing Range-Queries in Bloom-Filters with Piecewise-Monotone Hash Functions and Prefix Hashing. https://arxiv.org/abs/2207.04789

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