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HyperLogLog: the analysis of a near-optimal cardinality estimation algorithm
Open accessAbstract
This extended abstract describes and analyses a near-optimal probabilistic algorithm, HYPERLOGLOG, dedicated to estimating the number of \emphdistinct elements (the cardinality) of very large data ensembles. Using an auxiliary memory of m units (typically, "short bytes''), HYPERLOGLOG performs a single pass over the data and produces an estimate of the cardinality such that the relative accuracy (the standard error) is typically about $1.04/\sqrt{m}$. This improves on the best previously known cardinality estimator, LOGLOG, whose accuracy can be matched by consuming only 64% of the original memory. For instance, the new algorithm makes it possible to estimate cardinalities well beyond $10^9$ with a typical accuracy of 2% while using a memory of only 1.5 kilobytes. The algorithm parallelizes optimally and adapts to the sliding window model.
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Metadata
- Authors
- Flajolet, P., Fusy, É., Gandouet, O., & Meunier, F.
- Venue
- Discrete Mathematics & Theoretical Computer Science
- Published
- 2007-01-01
- Volume
- DMTCS Proceedings vol. AH,...
- Issue
- Proceedings
- ISSN
- 1365-8050 (ISSN-L)
- OpenAlex
- W2144982963
- Cited by
- 695
- References
- 1
- FWCI
- 8.61
- Type
- journal article
- Language
- en
- Open access
- diamond
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