Cascaded Learned Bloom Filter for Optimizing Model-Filter Size Balance and Fast Rejection

Abstract:Recent studies have demonstrated that learned Bloom filters (LBFs), which combine machine learning with the classical Bloom filter, can achieve superior memory efficiency. However, two challenges remain: (1) jointly optimizing the sizes of the machine learning model and Bloom filters, and (2) systematically minimizing reject time. We propose the Cascaded Learned Bloom Filter (CLBF), a unified architecture that generalizes existing LBF designs, including Sandwiched LBF and Partitioned LBF. Within this framework, we develop a dynamic programming-based optimizer that explores a discretized parameter space and identifies near-optimal configurations that balance model and filter sizes while achieving fast rejection. Experiments on real-world datasets show that CLBF reduces memory usage by up to 17% and decreases reject time by up to a factor of 65 compared to Partitioned LBF, the state-of-the-art LBF in terms of memory efficiency under a fixed machine learning model. Our code is publicly available at this https URL.

Data Structures and Algorithms (cs.DS); Computational Complexity (cs.CC); Machine Learning (cs.LG)

arXiv:2502.03696 [cs.DS]

(or arXiv:2502.03696v2 [cs.DS] for this version)

https://doi.org/10.48550/arXiv.2502.03696 Focus to learn more arXiv-issued DOI via DataCite

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