SF-Sketch: A Two-Stage Sketch for Data Streams

Abstract

Sketches are probabilistic data structures designed for recording frequencies of items in a multi-set. They are widely used in various fields, especially for gathering Internet statistics from distributed data streams in network measurements. In a distributed streaming application with high data rates, a sketch in each monitoring node “fills up” very quickly and then its content is transferred to a remote collector responsible for answering queries. Thus, the size of the contents transferred must be kept as small as possible while meeting the desired accuracy requirement. To obtain significantly higher accuracy while keeping the same update and query speed as the best prior sketches, in this article, we propose a new sketch - the Slim-Fat (SF) sketch. The key idea behind the SF-sketch is to maintain two separate sketches: a larger sketch, the Fat-subsketch, and a smaller sketch, the Slim-subsketch. The Fat-subsketch is used for updating and periodically producing the Slim-subsketch, which is then transferred to the remote collector for answering queries quickly and accurately. We also present the error bound as well as an accurate model of the correct rate of the SF-sketch, and verify their correctness through experiments. We implemented and extensively evaluated the SF-sketch along with several prior sketches. Our results show that when the size of our Slim-subsketch and of the widely used Count-Min (CM) sketch are kept the same, our SF-sketch outperforms the CM-sketch by up to 33.1 times in terms of accuracy (when the ratio of the sizes of the Fat-subsketch and the Slim-subsketch is 16:1). We have made all source codes publicly available at Github [“Source code of SF sketches,” [Online]. Available: https://github.com/paper2017/SF-sketch].