2025/11/15 by Ivan Cao, Cao, Ivan, Jaromir J. Saloni +2
Computer Science · #Stochastic Gradient Optimization Techniques #Cloud Computing and Resource Management #Parallel Computing and Optimization Techniques
paper · pdf · doi:10.48550/arxiv.2511.12025
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 109 values across 120 partitions on a 30-core AWS EMR cluster.