English

Quancurrent: A Concurrent Quantiles Sketch

Data Structures and Algorithms 2022-08-22 v1

Abstract

Sketches are a family of streaming algorithms widely used in the world of big data to perform fast, real-time analytics. A popular sketch type is Quantiles, which estimates the data distribution of a large input stream. We present Quancurrent, a highly scalable concurrent Quantiles sketch. Quancurrent's throughput increases linearly with the number of available threads, and with 3232 threads, it reaches an update speedup of 1212x and a query speedup of 3030x over a sequential sketch. Quancurrent allows queries to occur concurrently with updates and achieves an order of magnitude better query freshness than existing scalable solutions.

Keywords

Cite

@article{arxiv.2208.09265,
  title  = {Quancurrent: A Concurrent Quantiles Sketch},
  author = {Shaked Elias-Zada and Arik Rinberg and Idit Keidar},
  journal= {arXiv preprint arXiv:2208.09265},
  year   = {2022}
}
R2 v1 2026-06-25T01:49:07.633Z