Bloom filter is an efficient probabilistic data structure based on Hashing strategy and it utilizes bit arrays to succinctly store each element in the set without having to consider the type and size of the elements in the stored set.Through the analysis and derivation of the misjudgment rate of Bloom filter algorithm,we obtain the minimum misjudgment rate and the optimal number of Hash functions,and then obtain the relationship between the Bloom filter influencing factors,and also introduce the principle of counting Bloom filters,cuckoo filters,and other variants of filters from the perspective of optimizing the structure,and the perspective of the optimization of the Hash strategy.Research optimization for Bloom filters remains an important direction in big data technology.
Bloom filterHashing strategybig data technologyfalse positive rate
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