Drum: A rhythmic approach to interactive analytics on large data
Jianfeng Jia, Chen Li, Michael J. Carey · 2017
In this paper, we study how to progressively answer a time-consuming query on a large data set by generating a sequence of mini-queries. We formulate an optimization problem to produce the predicates of mini-queries by considering both their total running time as well as the smoothness of result delivery in order to show the incremental results at a rhythmic pace to improve the user experience. We develop an adaptive framework called Drum that can collect the runtime behavioral statistics of the database system to decide the predicate of the next mini-query appropriately. The framework is a general middleware solution without any changes to the underlying database system. We have conducted extensive experiments on a large, real data set, and the results show that Drum can reduce the delay of delivering intermediate results to the user without sacrificing much total time.