AQP++: Connecting approximate query processing with aggregate precomputation for interactive analytics

Interactive analytics requires database systems to be able to answer aggregation queries within interactive response times. As the amount of data is continuously growing at an unprecedented rate, this is becoming increasingly challenging. In the past, the database community has proposed two separate ideas, sampling-based approximate query processing (AQP) and aggregate precomputation (AggPre) such as data cubes, to address this challenge. In this paper, we argue for the need to connect these two separate ideas for interactive analytics. We propose AQP++, a novel framework to enable the connection. The framework can leverage both a sample as well as a precomputed aggregate to answer user queries. We discuss the advantages of having such a unified framework and identify new challenges to fulfill this vision. We conduct an in-depth study of these challenges for range queries and explore both optimal and heuristic solutions to address them. Our experiments using two public benchmarks and one real-world dataset show that AQP++ achieves a more flexible and better trade-off among preprocessing cost, query response time, and answer quality than AQP or AggPre.

Duke Scholars

Author Jian Pei Computer Science