HyperLogLogLog: Cardinality Estimation With One Log More
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Accepted author manuscript, 1.97 MB, PDF document
We present HyperLogLogLog, a practical compression of the HyperLogLog sketch that compresses the sketch from O(mogog n) bits down to m log2log2log2 m + O(m+loglog n) bits for estimating the number of distinct elements∼n using m∼registers. The algorithm works as a drop-in replacement that preserves all estimation properties of the HyperLogLog sketch, it is possible to convert back and forth between the compressed and uncompressed representations, and the compressed sketch maintains mergeability in the compressed domain. The compressed sketch can be updated in amortized constant time, assuming n is sufficiently larger than m. We provide a C++ implementation of the sketch, and show by experimental evaluation against well-known implementations by Google and Apache that our implementation provides small sketches while maintaining competitive update and merge times. Concretely, we observed approximately a 40% reduction in the sketch size. Furthermore, we obtain as a corollary a theoretical algorithm that compresses the sketch down to mlog2log2log2log2 m+O(mlogloglog m/loglog m+loglog n) bits.
Original language | English |
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Title of host publication | KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining |
Publisher | Association for Computing Machinery, Inc. |
Publication date | 2022 |
Pages | 753-761 |
ISBN (Electronic) | 9781450393850 |
DOIs | |
Publication status | Published - 2022 |
Event | 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022 - Washington, United States Duration: 14 Aug 2022 → 18 Aug 2022 |
Conference
Conference | 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022 |
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Land | United States |
By | Washington |
Periode | 14/08/2022 → 18/08/2022 |
Sponsor | ACM SIGKDD, ACM SIGMOD |
Bibliographical note
Publisher Copyright:
© 2022 ACM.
- cardinality estimation, distinct elements, hashing, hyperloglog
Research areas
ID: 340691957