We will demonstrate a prototype of sqlcheck, a holistic toolchain for automatically finding and fixing anti-patterns in database applications. The advent of modern database-as-a-service platforms has made it easy for developers to quickly create scalable applications. However, it is still challenging for developers to design performant, maintainable, and accurate applications. This is because 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. sqlcheck leverages techniques for automatically: (1) detecting anti-patterns with high accuracy, (2) ranking them 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 will demonstrate that sqlcheck enables developers to create more performant, maintainable, and accurate applications. We will show the prevalence of these anti-patterns in a large collection of queries and databases collected from open-source repositories.

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.