Relative Error Streaming Quantiles with Seamless Mergeability via Adaptive Compactors

TL;DR

This paper introduces adaptive compactors to simplify the mergeability proof of ReqSketch, a space-efficient quantile summary, while maintaining its accuracy, efficiency, and near-optimal space bounds in distributed data processing.

Contribution

The paper develops adaptive compactors that simplify the mergeability proof of ReqSketch without sacrificing its space efficiency or accuracy guarantees.

Findings

Simplified proof of mergeability for ReqSketch.

Maintains original space bounds and efficiency.

Achieves near-optimal space bounds in merging scenarios.

Abstract

Quantile summaries provide a scalable way to estimate the distribution of individual attributes in large datasets that are often distributed across multiple machines or generated by sensor networks. ReqSketch (arXiv:2004.01668) is currently the most space-efficient summary with two key properties: relative error guarantees, offering increasingly higher accuracy towards the distribution's tails, and mergeability, allowing distributed or parallel processing of datasets. Due to these features and its simple algorithm design, ReqSketch has been adopted in practice, via implementation in the Apache DataSketches library. However, the proof of mergeability in ReqSketch is overly complicated, requiring an intricate charging argument and complex variance analysis. In this paper, we provide a refined version of ReqSketch, by developing so-called adaptive compactors. This enables a significantly simplified proof of relative error guarantees in the most general mergeability setting, while retaining the original space bound, update time, and algorithmic simplicity. Moreover, the adaptivity of our sketch, together with the proof technique, yields near-optimal space bounds in specific scenarios - particularly when merging sketches of comparable size.