Vacuum Filters: More Space-Efficient and Faster Replacement for Bloom and Cuckoo Filters

We present vacuum filters, a type of data structures to support approximate membership queries. Vacuum filters cost the smallest space among all known AMQ data structures and provide higher insertion and lookup throughput in most situations. Hence they can be used as the replacement of the widely used Bloom filters and cuckoo filters. Similar to cuckoo filters, vacuum filters also store item fingerprints in a table. The memory-efficiency and throughput improvements are from the innovation of a table insertion and fingerprint eviction strategy that achieves both high load factor and data locality without any restriction of the table size. In addition, we propose a new update framework to resolve two difficult problems for AMQ structures under dynamics, namely duplicate insertions and set resizing. The experiments show that vacuum filters can achieve 25% less space in average and similar throughput compared to cuckoo filters, and 15% less space and >10x throughput compared to Bloom filters, with same false positive rates. AMQ data structures are widely used in various layers of computer systems and networks and are usually hosted in platforms where memory is limited and precious. Hence the improvements brought by vacuum filters can be considered significant. PVLDB Reference Format: Minmei Wang, Mingxun Zhou, Shouqian Shi, Chen Qian. Vacuum Filters: More Space-Efficient and Faster Replacement for Bloom and Cuckoo Filters. PVLDB, 13(2): 197-210, 2019. DOI: https://doi.org/10.14778/3364324.3364333