Quancurrent: A Concurrent Quantiles Sketch
Shaked Elias-Zada, Arik Rinberg, and Idit Keidar

TL;DR
Quancurrent is a highly scalable concurrent Quantiles sketch that significantly improves throughput and query freshness in streaming data analytics by enabling parallel updates and queries.
Contribution
It introduces Quancurrent, a novel concurrent Quantiles sketch that scales linearly with threads and enhances real-time data distribution estimation.
Findings
Linear throughput increase with threads
12x update speedup with 32 threads
30x query speedup with 32 threads
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 threads, it reaches an update speedup of x and a query speedup of x 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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Taxonomy
TopicsData Management and Algorithms · Advanced Database Systems and Queries · Data Stream Mining Techniques
