Paper Info
Title
SQLCheck: Automated Detection and Diagnosis of SQL Anti-Patterns
Abstract
The emergence of database-as-a-service platforms has made deploying database applications easier than before. Now, developers can quickly create scalable applications. However, designing performant, maintainable, and accurate applications is challenging. Developers may unknowingly introduce anti-patterns in the application's SQL statements. These anti-patterns are design decisions that are intended to solve a problem but often lead to other problems by violating fundamental design principles. In this paper, we present SQLCheck, a holistic toolchain for automatically finding and fixing anti-patterns in database applications. We introduce techniques for automatically (1) detecting anti-patterns with high precision and recall, (2) ranking the anti-patterns based on their impact on performance, maintainability, and accuracy of applications, and (3) suggesting alternative queries and changes to the database design to fix these anti-patterns. We demonstrate the prevalence of these anti-patterns in a large collection of queries and databases collected from open-source repositories. We introduce an anti-pattern detection algorithm that augments query analysis with data analysis. We present a ranking model for characterizing the impact of frequently occurring anti-patterns. We discuss how SQLCheck suggests fixes for high-impact anti-patterns using rule-based query refactoring techniques. Our experiments demonstrate that SQLCheck enables developers to create more performant, maintainable, and accurate applications.
Year
DOI
Venue
2020
10.1145/3318464.3389754
SIGMOD/PODS '20: International Conference on Management of Data Portland OR USA June, 2020
Keywords
DocType
ISBN
Anti-Patterns, Database Applications
Conference
978-1-4503-6735-6
Citations 
PageRank 
References 
1
0.37
13
Authors
3
Name
Order
Citations
PageRank
Dintyala Visweswara Sai Prashanth110.37
Arpit Narechania292.84
Joy Arulraj318312.17