Quancurrent: A Concurrent Quantiles Sketch

August 19, 2022 Β· Declared Dead Β· πŸ› ACM Symposium on Parallelism in Algorithms and Architectures

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Authors Shaked Elias-Zada, Arik Rinberg, Idit Keidar arXiv ID 2208.09265 Category cs.DS: Data Structures & Algorithms Citations 1 Venue ACM Symposium on Parallelism in Algorithms and Architectures Last Checked 4 months ago
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 $12$x and a query speedup of $30$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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