A Quick and Exact Method for Distributed Quantile Computation
Abstract
Quantile computation is a core primitive in large-scale data analytics. In Spark, practitioners typically rely on the Greenwald-Khanna (GK) Sketch, an approximate method. When exact quantiles are required, the default option is an expensive global sort. We present GK Select, an exact Spark algorithm that avoids full-data shuffles and completes in a constant number of actions. GK Select leverages GK Sketch to identify a near-target pivot, extracts all values within the error bound around this pivot in each partition in linear time, and then tree-reduces the resulting candidate sets. We show analytically that GK Select matches the executor-side time complexity of GK Sketch while returning the exact quantile. Empirically, GK Select achieves sketch-level latency and outperforms Spark's full sort by approximately 10.5x on 10^9 values across 120 partitions on a 30-core AWS EMR cluster.
Cite
@article{arxiv.2511.12025,
title = {A Quick and Exact Method for Distributed Quantile Computation},
author = {Ivan Cao and Jaromir J. Saloni and David A. G. Harrison},
journal= {arXiv preprint arXiv:2511.12025},
year = {2026}
}
Comments
10 pages, 2 figures. Draft version for testing and feedback