Quancurrent: A Concurrent Quantiles Sketch

Shaked Elias Zada,Arik Rinberg,Idit Keidar

PROCEEDINGS OF THE 35TH ACM SYMPOSIUM ON PARALLELISM IN ALGORITHMS AND ARCHITECTURES, SPAA 2023(2023)

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摘要
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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关键词
big data,streaming algorithms,sketches,quantiles,real-time analysis,concurrency
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