PDL Abstract

Cuckoo Filter: Practically Better Than Bloom

Proceedings of CoNEXT (CoNEXT’14), December 2014.

Bin Fan, David G. Andersen, Michael Kaminsky*, Michael D. Mitzenmacher^

Carnegie Mellon University
*Intel Labs
^Harvard University


In many networking systems, Bloom filters are used for highspeed set membership tests. They permit a small fraction of false positive answers with very good space efficiency. However, they do not permit deletion of items from the set, and previous attempts to extend “standard” Bloom filters to support deletion all degrade either space or performance.

We propose a new data structure called the cuckoo filter that can replace Bloom filters for approximate set membership tests. Cuckoo filters support adding and removing items dynamically while achieving even higher performance than Bloom filters. For applications that store many items and target moderately low false positive rates, cuckoo filters have lower space overhead than space-optimized Bloom filters. Our experimental results also show that cuckoo filters outperform previous data structures that extend Bloom filters to support deletions substantially in both time and space.