Towards Enhancing Database Education: Natural Language Generation Meets Query Execution Plans

The database systems course is offered as part of an undergraduate computer science degree program in many major universities. A key learning goal of learners taking such a course is to understand how sql queries are processed in a rdbms in practice. Since aquery execution plan (qep ) describes the execution steps of a query, learners can acquire the understanding by perusing the qep s generated by a rdbms. Unfortunately, in practice, it is often daunting for a learner to comprehend these qep s containing vendor-specific implementation details, hindering her learning process. In this paper, we present a novel, end-to-end,generic system called lantern that generates a natural language description of a qep to facilitate understanding of the query execution steps. It takes as input an sql query and its qep, and generates a natural language description of the execution strategy deployed by the underlying rdbms. Specifically, it deploys adeclarative framework called pool that enablessubject matter experts to efficiently create and maintain natural language descriptions of physical operators used in qep s. Arule-based framework called rule-lantern is proposed that exploits pool to generate natural language descriptions of qep s. Despite the high accuracy of rule-lantern, our engagement with learners reveal that, consistent with existing psychology theories, perusing such rule-based descriptions lead toboredom due to repetitive statements across different qep s. To address this issue, we present a noveldeep learning-based language generation framework called neural -lantern that infuses language variability in the generated description by exploiting a set ofparaphrasing tools andword embedding. Our experimental study with real learners shows the effectiveness of lantern in facilitating comprehension of qep s.