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Probabilistic Filters by Example
By Example Probablistic filters are high-speed, space-efficient data structures that support set-membership tests with a one-sided error. These filters can claim that a given entry is definitely not represented in a set of entries, or might be represented in the set.
Probablistic filters are high-speed, space-efficient data structures that support set-membership tests with a one-sided error. Probabilistic filters are used in a variety of applications where slow or expensive operations can be avoided prior to execution by a consulting comparitavely fast or cheap set membership test. - Michael Mitzenmacher (2014) Cuckoo FilterStandard Bloom FilterCounting Bloom FilterInsertO(1), amortizedvariable, longer as load factor approaches capacityO(1)fixed, performs constant k hashesO(1)fixed, performs constant k hashesAs load increasesFPP trends toward desired max
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