Estimating the ε-approximate quantiles or ranks of a stream is a fundamental task in data monitoring. Given a stream x_1,..., x_n from a universe \mathcalU with total order, an additive-error quantile sketch \mathcalM allows us to approximate the rank of any query y\in \mathcalU up to additive ε n error. In 2001, Greenwald and Khanna gave a deterministic algorithm (GK sketch) that solves the ε-approximate quantiles estimation problem using O(ε^-1 łog(ε n)) space \citegreenwald2001space ; recently, this algorithm was shown to be optimal by Cormode and Vesleý in 2020 \citecormode2020tight. However, due to the intricacy of the GK sketch and its analysis, over-simplified versions of the algorithm are implemented in practical applications, often without any known theoretical guarantees. In fact, it has remained an open question whether the GK sketch can be simplified while maintaining the optimal space bound. In this paper, we resolve this open question by giving a simplified deterministic algorithm that stores at most (2 + o(1))ε^-1 łog (ε n) elements and solves the additive-error quantile estimation problem; as a side benefit, our algorithm achieves a smaller constant factor than the \frac11 2 ε^-1 łog(ε n) space bound in the original GK sketch~\citegreenwald2001space. Our algorithm features an easier analysis and still achieves the same optimal asymptotic space complexity as the original GK sketch. Lastly, our simplification enables an efficient data structure implementation, with a worst-case runtime of O(łog(1/ε) + łog łog (ε n)) per-element for the ordinary ε-approximate quantile estimation problem. Also, for the related weighted'' quantile estimation problem, we give efficient data structures for our simplified algorithm which guarantee a worst-case per-element runtime of O(łog(1/ε) + łog łog (ε W_n/w_\textrmmin )), achieving an improvement over the previous upper bound of \citeassadi2023generalizing.
Learned Interpolation for Better Streaming Quantile Approximation with Worst-Case Guarantees
An ε-approximate quantile sketch over a stream of n inputs
approximates the rank of any query point q—that is, the
number of input points less than q—up to an additive error of
εn, generally with some probability of at least 1−1/ poly(n),
while consuming o(n) space. While the celebrated KLL
sketch of Karnin, Lang, and Liberty achieves a provably
optimal quantile approximation algorithm over worst-case
streams, the approximations it achieves in practice are
often far from optimal. Indeed, the most commonly used
technique in practice is Dunning’s t-digest, which often
achieves much better approximations than KLL on realworld
data but is known to have arbitrarily large errors in
the worst case. We apply interpolation techniques to the
streaming quantiles problem to attempt to achieve better
approximations on real-world data sets than KLL while
maintaining similar guarantees in the worst case.
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- Award ID(s):
- 2022448
- NSF-PAR ID:
- 10430249
- Date Published:
- Journal Name:
- SIAM Conference on Applied and Computational Discrete Algorithms
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1137/1.9781611977714.8
