Adaptive Quotient Filters
Richard Wen, Hunter McCoy, David Tench, Guido Tagliavini, Michael A., Bender, Alex Conway, Martin Farach-Colton, Rob Johnson, Prashant Pandey

TL;DR
AdaptiveQF is a practical adaptive filter that maintains strong false-positive guarantees and low overhead, making it suitable for real-world systems and adversarial workloads.
Contribution
We introduce AdaptiveQF, the first adaptive quotient filter with minimal overhead and strong guarantees, improving practicality and robustness over prior adaptive filters.
Findings
AdaptiveQF achieves low adaptivity overhead.
It maintains false-positive guarantees under adversarial workloads.
It preserves cache efficiency and mergeability of quotient filters.
Abstract
Adaptive filters, such as telescoping and adaptive cuckoo filters, update their representation upon detecting a false positive to avoid repeating the same error in the future. Adaptive filters require an auxiliary structure, typically much larger than the main filter and often residing on slow storage, to facilitate adaptation. However, existing adaptive filters are not practical and have seen no adoption in real-world systems due to two main reasons. Firstly, they offer weak adaptivity guarantees, meaning that fixing a new false positive can cause a previously fixed false positive to come back. Secondly, the sub-optimal design of the auxiliary structure results in adaptivity overheads so substantial that they can actually diminish the overall system performance compared to a traditional filter. In this paper, we design and implement AdaptiveQF, the first practical adaptive filter…
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Taxonomy
TopicsFuzzy Logic and Control Systems
