A key learning goal of learners taking database systems course is to understand how SQL queries are processed in an RDBMS in practice. To this end, comprehension of the cost-based comparison of different plan choices to select the query execution plan (QEP) of a query is paramount. Unfortunately, off-the-shelf RDBMS typically only expose the selected QEP to users without revealing information about representative alternative query plans considered during QEP selection in a learner-friendly manner, hindering the learning process. In this paper, we present a novel end-to-end and generic framework called ARENA that facilitates exploration of informative alternative query plans of a given SQL query to aid the comprehension of QEP selection. Under the hood, ARENA addresses a novel problem called alternative plan selection problem (TIPS) which aims to discover a set of k alternative plans from the underlying plan space so that the plan interestingness of the set is maximized. Specifically, we explore two variants of the problem, namely batch TIPS and incremental TIPS, to cater to diverse set of learners. Due to the computational hardness of the problem, we present a 2 approximation algorithm to address it efficiently. Exhaustive experimental study with real-world learners demonstrates the effectiveness of arena in enhancing learners' understanding of the alternative plan choices considered during QEP selection.
翻译:学习数据库系统课程的学习者的关键学习目标是了解在数据库管理系统区域数据库管理系统实际中如何处理SQL查询。为此,理解对选择查询执行计划的不同计划选择进行基于成本的比较至关重要。不幸的是,现成的RDBMS通常只在不透露有关选定的QEP信息的情况下,向用户披露选定的QEP,而不披露在选择QEP时以对学习者友好的方式考虑的有代表性的替代查询计划的信息,从而妨碍学习进程。在本文件中,我们提出了一个名为ARENA的新式端到端和通用框架,即“ARENA”的“ARENA”为探索特定SQL查询的信息性替代查询计划,以帮助理解QEP选择。在引擎下,ARENA处理一个叫“替代计划选择问题”的新问题,即“TIPS”,目的是从基本计划空间中发现一套替代查询计划,以便尽可能扩大这套计划的兴趣。具体地,我们探讨这一问题的两个变式,即批量TIPS和递增的TIPS,以满足各类学生的需要。由于问题的计算困难,我们在现实的计算中,我们展示了现实学习者们对现实的学习者们的有效理解。