Quancurrent: A Concurrent Quantiles Sketch
August 19, 2022 Β· Declared Dead Β· π 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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