Outer-Tuning
Rafael Pereira de Oliveira, Fernanda Baião, Ana Carolina Almeida, Daniel Schwabe, Sérgio Lifschitz · 2019
Database tuning is a crucial task to address the performance of information systems that deal with a considerable amount of information stored in databases. Current tuning tools are very platform-specific and do not provide adequate support for the database administrator to reason about performance improvement suggestions. In this paper, we discuss several architectural and implementation decisions of Outer-Tuning, our framework that supports database tuning. Outer-Tuning follows a model-driven development and a modular architecture design, which enabled several benefits. This paper contributes with: (i) the architectural design model adopted in Outer-Tuning, which combines imperative and declarative programming; (ii) the discussions and steps to integrate several software components; and (iii) the actual framework implementation. We assess our framework with an experiment using the TPC-H benchmark. The results evidence that Outer-Tuning infers useful tuning actions and supports the DBA by providing a more semantic environment to create and adapt tuning heuristics using concepts closer to his/her domain, and also relevant information on the rationale of the tuning actions through a friendly web interface.