Towards Selecting the Informative Alternative Relational Query Plans for Database Education

TL;DR

This paper introduces a method to select the most informative alternative query plans in relational databases, enhancing educational tools by making optimizer choices more understandable for learners.

Contribution

It formulates the informative plan selection problem (TIPS), proposes approximation algorithms with theoretical guarantees, and validates effectiveness through extensive experiments and educational evaluations.

Findings

Effective selection of informative AQPs improves understanding of query optimization.

Proposed algorithms outperform baseline methods in plan informativeness.

Educational impact shown through positive learner feedback and academic outcomes.

Abstract

Off-the-shelf RDBMS typically expose only the query execution plan (QEP) of an SQL query, without presenting information about representative alternative query plans (AQPs) considered during plan selection in a user-friendly manner. Providing easy access to representative AQPs is valuable in database education, as it helps learners understand the plan choices made by a query optimizer, one of the several important components related to relational query processing. In this paper, we present a novel problem called the informative plan selection problem (TIPS), which aims to discover a set of k informative AQPs from the underlying plan space so that the plan informativeness of the set is maximized. Specifically, we explore two variants of the problem, batch TIPS and incremental TIPS, to cater to diverse learners. Due to the computational hardness of the problem, we present an approximation algorithm to address it efficiently while providing theoretical guarantees for the results. An extensive experimental study, including feedback from real-world learners and a three-year in-class evaluation of academic outcomes, demonstrates the effectiveness of our solutions for database education.