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HyperLogLog

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HyperLogLog is an algorithm for the count-distinct problem, approximating the number of distinct elements in a multiset. Calculating the exact cardinality of the distinct elements of a multiset requires an amount of memory proportional to the cardinality, which is impractical for very large data sets. Probabilistic cardinality estimators, such as the HyperLogLog algorithm, use significantly less memory than this, but can only approximate the cardinality. The HyperLogLog algorithm is able to estimate cardinalities of > 109 with a typical accuracy (standard error) of 2%, using 1.5 kB of memory. HyperLogLog is an extension of the earlier LogLog algorithm, itself deriving from the 1984 Flajolet–Martin algorithm.

HyperLogLog - Wikipedia Jump to content From Wikipedia, the free encyclopedia Approximate distinct counting algorithm Part of a series on Probabilistic data structures Bloom filter Count sketch Count–min sketch Quotient filter Skip list Random trees Random binary tree Treap Rapidly exploring random tree Related Randomized algorithm HyperLogLog v t e HyperLogLog is an algorithm for the count-distinct problem , approximating the number of distinct elements in a multiset . [ 1 ] Calculating the exact cardinality of the distinct elements of a multiset requires an amount of memory proportio

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