LIPIcs.AofA.2024.5.pdf
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Bit-Array-Based Alternatives to HyperLogLog
Authors
Svante Janson 
,
Jérémie Lumbroso ,
Robert Sedgewick
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35th International Conference on Probabilistic, Combinatorial and Asymptotic Methods for the Analysis of Algorithms (AofA 2024)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: International Conference on Probabilistic, Combinatorial and Asymptotic Methods for the Analysis of Algorithms (AofA) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2024-07-18
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Acknowledgements
This work is dedicated to the memory of Philippe Flajolet. We would like to thank Martin Pépin and two anonymous reviewers for their helpful comments on our initial submission; and Seth Pettie and Jelani Nelson for feedback on this paper. We would also like to thank our colleagues, Conrado Martínez, Sampath Kannan, Val Tannen, and Pedro Paredes for their interest and feedback; and our students, Alex Iriza and Alex Baroody for their discussions and implementation work on earlier versions of these algorithms. Finally, we would like to thank the editors, Cécile Mailler and Sebastian Wild, for their service to the community.
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Svante Janson, Jérémie Lumbroso, and Robert Sedgewick. Bit-Array-Based Alternatives to HyperLogLog. In 35th International Conference on Probabilistic, Combinatorial and Asymptotic Methods for the Analysis of Algorithms (AofA 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 302, pp. 5:1-5:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.AofA.2024.5
Abstract
We present a family of algorithms for the problem of estimating the number of distinct items in an input stream that are simple to implement and are appropriate for practical applications. Our algorithms are a logical extension of the series of algorithms developed by Flajolet and his coauthors starting in 1983 that culminated in the widely used HyperLogLog algorithm. These algorithms divide the input stream into M substreams and lead to a time-accuracy tradeoff where a constant number of bits per substream are saved to achieve a relative accuracy proportional to 1/√M. Our algorithms use just one or two bits per substream. Their effectiveness is demonstrated by a proof of approximate normality, with explicit expressions for standard errors that inform parameter settings and allow proper quantitative comparisons with other methods. Hypotheses about performance are validated through experiments using a realistic input stream, with the conclusion that our algorithms are more accurate than HyperLogLog when using the same amount of memory, and they use two-thirds as much memory as HyperLogLog to achieve a given accuracy.
Subject Classification
ACM Subject Classification
- Theory of computation → Sketching and sampling
Keywords
- Cardinality estimation
- sketching
- Hyperloglog
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References
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