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Quancurrent: A Concurrent Quantiles Sketch

2022/08/19 by Elias-Zada, Shaked, Rinberg, Arik, Keidar, Idit
#Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.2208.09265

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 32 threads, it reaches an update speedup of 12x and a query speedup of 30x 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.

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