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arXiv · 2504.01206

SplineSketch: Even More Accurate Quantiles with Error Guarantees

Aleksander Łukasiewicz·Jakub Tětek·Pavel Veselý
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Abstract

Space-efficient streaming estimation of quantiles in massive datasets is a fundamental problem with numerous applications in data monitoring and analysis. While theoretical research led to optimal algorithms, such as the Greenwald-Khanna algorithm or the KLL sketch, practitioners often use other sketches that perform significantly better in practice but lack theoretical guarantees. Most notably, the widely used $t$-digest has unbounded worst-case error. In this paper, we seek to get the best of both worlds. We present a new quantile summary, SplineSketch, for numeric data, offering near-optimal theoretical guarantees, namely uniformly bounded rank error, and outperforming $t$-digest by a factor of 2-20 on a range of synthetic and real-world datasets. To achieve such performance, we develop a novel approach that maintains a dynamic subdivision of the input range into buckets while fitting the input distribution using monotone cubic spline interpolation. The core challenge is implementing this method in a space-efficient manner while ensuring strong worst-case guarantees.

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Aleksander Łukasiewicz, Jakub Tětek, Pavel Veselý. 2025-04-01. SplineSketch: Even More Accurate Quantiles with Error Guarantees. https://doi.org/10.1145/3769827

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