增强草图:更快,更准确的流处理

Pratanu Roy, Arijit Khan, G. Alonso
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引用次数: 135

摘要

近似算法通常用于估计大容量、快速数据流上项目的频率。最常见的是count - min草图的变体,它使用次线性空间进行计数,但可能在最频繁的项目计数中产生错误,并可能对低频项目进行错误分类。在本文中,我们通过提高最频繁项目的频率估计精度和减少低频项目可能的误分类来提高基于草图的算法的准确性,同时也提高了总体吞吐量。我们的解决方案,称为增强草图(assketch),是基于一个预过滤阶段,动态识别和聚合最频繁的项目。超出预过滤阶段的项目使用传统的草图算法进行处理,从而使解决方案具有通用性并适用于广泛的上下文。预滤波阶段可以在多核机器上使用SIMD指令有效地实现,并且可以通过管道并行进一步并行化,其中滤波阶段在一个核中运行,草图算法在另一个核中运行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Augmented Sketch: Faster and More Accurate Stream Processing
Approximated algorithms are often used to estimate the frequency of items on high volume, fast data streams. The most common ones are variations of Count-Min sketch, which use sub-linear space for the count, but can produce errors in the counts of the most frequent items and can misclassify low-frequency items. In this paper, we improve the accuracy of sketch-based algorithms by increasing the frequency estimation accuracy of the most frequent items and reducing the possible misclassification of low-frequency items, while also improving the overall throughput. Our solution, called Augmented Sketch (ASketch), is based on a pre-filtering stage that dynamically identifies and aggregates the most frequent items. Items overflowing the pre-filtering stage are processed using a conventional sketch algorithm, thereby making the solution general and applicable in a wide range of contexts. The pre-filtering stage can be efficiently implemented with SIMD instructions on multi-core machines and can be further parallelized through pipeline parallelism where the filtering stage runs in one core and the sketch algorithm runs in another core.
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