Vacuum 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
factors and data locality without any restriction of the table size. The
experiments show that vacuum filters can achieves 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.