A core component of a database systems course at the undergraduate level is the design and implementation of the query optimizer in a rdbms. The query optimization process produces a query execution plan (qep) which represents an execution strategy for a sql query. Unfortunately, in practice, it is often difficult for a student to comprehend the query execution strategy by perusing the qep, hindering her learning process. In this demonstration, we present a novel system called neuron that facilitates natural language interaction with qeps to enhance its understanding. neuron accepts a sql query (which may include joins, aggregation, nesting, among other things) as input, executes it, and generates a simplified natural language-based description (both in text and voice form) of the execution strategy deployed by the underlying rdbms. Furthermore, it facilitates understanding of various features related to the qep through a natural language-based question answering framework.We advocate that such tool, world’s first of its kind, can greatly enhance students’ learning of the query optimization topic.
NEURON: Query Execution Plan Meets Natural Language Processing For Augmenting DB Education
Siyuan Liu, Sourav S, Wanlu Zhang, Shu Wang, Wanyi Huang, and Shafiq Joty. In Proceedings of 45th ACM SIGMOD International Conference on Management of Data (Demo) (SIGMOD'19 (Demo)) , pages 1953–1956, 2019.
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