HyperLogLogLog: Cardinality Estimation With One Log More
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Standard
HyperLogLogLog : Cardinality Estimation With One Log More. / Karppa, Matti; Pagh, Rasmus.
KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, Inc., 2022. s. 753-761.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - GEN
T1 - HyperLogLogLog
T2 - 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022
AU - Karppa, Matti
AU - Pagh, Rasmus
N1 - Publisher Copyright: © 2022 ACM.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - cardinality estimation
KW - distinct elements
KW - hashing
KW - hyperloglog
UR - http://www.scopus.com/inward/record.url?scp=85136954603&partnerID=8YFLogxK
U2 - 10.1145/3534678.3539246
DO - 10.1145/3534678.3539246
M3 - Article in proceedings
AN - SCOPUS:85136954603
SP - 753
EP - 761
BT - KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery, Inc.
Y2 - 14 August 2022 through 18 August 2022
ER -
ID: 340691957