Bloom filters are used to perform approximate membership checking in a wide range of applications in both computing and networking, but the recently introduced cuckoo filter is also gaining popularity. Therefore, it is of interest to compare both filters and provide insights into their features so that designers can make an informed decision when implementing approximate membership checking in a given application. This article first compares Bloom and cuckoo filters focusing on a packet classification application. The analysis identifies a shortcoming of cuckoo filters in terms of false positive rate when they do not operate close to full occupancy. Based on that observation, this article also proposes the use of a configurable bucket to improve the scaling of the false positive rate of the cuckoo filter with occupancy.

中文翻译：

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布谷鸟过滤器和布隆过滤器：数据包分类的比较和应用

布隆过滤器用于在计算和网络的广泛应用中执行近似成员资格检查，但最近推出的布谷鸟过滤器也越来越受欢迎。因此，比较这两种过滤器并深入了解它们的特性是很有意义的，这样设计人员就可以在给定应用程序中实施近似成员资格检查时做出明智的决定。本文首先比较 Bloom 和 cuckoo 过滤器，重点是一个数据包分类应用程序。分析确定了布谷鸟过滤器在误报率方面的缺点，当它们没有接近满员运行时。基于这一观察，本文还建议使用可配置的桶来提高布谷鸟过滤器的误报率与占用率的缩放比例。