The use of a Bloom filter is useful aqua solutions if it can be tested against a large amount of da

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A Bloom filter is a probabilistic data structure with low memory complexity that can be used to check whether an element is contained in an amount. The test can provide false positives, ie, with a certain probability, which is proportional to the number of elements contained in the amount of an item is incorrectly as to the amount declared aqua solutions belongs, but it will never be false-negative supplied. Operation
A Bloom filter is fundamentally based on an m-digit bit array that is initially filled with zeros and k hash functions which map the elements of the domain of definition of the m array indexes. To add an element to the filter aqua solutions element via the hash function is mapped onto k array indexes, and the bit-array is placed in each of the calculated indices to 1. To test whether an element in the filter is contained on the hash function k indexes are also calculated, aqua solutions the array now contains one of the indices aqua solutions of a value other than 1, the element is not included in the filter. The width m of the array and the number k of hash functions determines the probability of false-positive, the higher you choose this the lower this probability.
The use of a Bloom filter is useful aqua solutions if it can be tested against a large amount of data and false positives are to be represented. For example, the exclusion of duplicates in the generation of coupon codes for an already existing database.
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