DBEst: Revisiting approximate query processing engines with machine learning models

In the era of big data, computing exact answers to analyt- ical queries becomes prohibitively expensive. This greatly increases the value of approaches that can compute effi- ciently approximate, but highly-accurate, answers to analyt- ical queries. Alas, the state of the art still suffers from many shortcomings: Errors are still high, unless large memory in- vestments are made. Many important analytics tasks are not supported. Query response times are too long and thus ap- proaches rely on parallel execution of queries atop large big data analytics clusters, in-situ or in the cloud, whose acqui- sition/use costs dearly. Hence, the following questions are crucial: Can we develop AQP engines that reduce response times by orders of magnitude, ensure high accuracy, and support most aggregate functions? With smaller memory footprints and small overheads to build the state upon which they are based? With this paper, we show that the answers to all questions above can be positive. The paper presents DBEst1 , a system based on Machine Learning models (re- gression models and probability density estimators). It will discuss its limitations, promises, and how it can complement existing systems. It will substantiate its advantages using queries and data from the TPC-DS benchmark and real-life datasets, compared against state of the art AQP engines.

Recommended citation: Ma, Qingzhi, and Peter Triantafillou. "Dbest: Revisiting approximate query processing engines with machine learning models." In Proceedings of the 2019 International Conference on Management of Data, pp. 1553-1570. 2019. https://wrap.warwick.ac.uk/115961/7/WRAP-DBEst-revisiting-approximate-query-processing-engines-machine-learning-models-Triantafillou-2019.pdf