论文标题
Quancurrent：并发分位数草图
Quancurrent: A Concurrent Quantiles Sketch
论文作者
论文摘要
草图是大数据世界中广泛使用的流算法系列，以执行快速，实时分析。流行的草图类型是分位数，它估计了大型输入流的数据分布。我们提出Quancurrent，这是一个高度可扩展的并发分位数草图。 Quancurrent的吞吐量随着可用线程的数量而线性增加，并且$ 32 $线程达到了$ 12 $ x的更新速度，并且在顺序草图上的查询速度为$ 30 $ x。 Quancurrent允许查询与更新同时发生，并获得比现有可扩展解决方案更好的查询新鲜度。
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.
