bloomRF: On Performing Range-Queries with Bloom-Filters based on Piecewise-Monotone Hash Functions and Dyadic Trace-Trees

TL;DR

bloomRF is a novel data structure that extends Bloom-Filters with range-query capabilities using dyadic interval schemes, Trace-Trees, and piecewise-monotone hash functions, enabling efficient approximate membership testing for points and ranges.

Contribution

The paper introduces bloomRF, combining dyadic interval schemes, Trace-Trees, and new hash functions to support range queries within Bloom-Filters, a novel approach for approximate membership testing.

Findings

bloomRF outperforms existing point-range-filters by up to 4x.

It efficiently supports multiple data types including integers, strings, and floats.

The structure is effective in both RocksDB and standalone implementations.

Abstract

We introduce bloomRF as a unified method for approximate membership testing that supports both point- and range-queries on a single data structure. bloomRF extends Bloom-Filters with range query support and may replace them. The core idea is to employ a dyadic interval scheme to determine the set of dyadic intervals covering a data point, which are then encoded and inserted. bloomRF introduces Dyadic Trace-Trees as novel data structure that represents those covering intervals implicitly. A Trace-Tree encoding scheme represents the set of covering intervals efficiently, in a compact bit representation. Furthermore, bloomRF introduces novel piecewise-monotone hash functions that are locally order-preserving and thus support range querying. We present an efficient membership computation method for range-queries. Although, bloomRF is designed for integers it also supports string and floating-point data types. It can also handle multiple attributes and serve as multi-attribute filter. We evaluate bloomRF in RocksDB and in a standalone library. bloomRF is more efficient and outperforms existing point-range-filters by up to 4x across a range of settings.