Cleaning Antipatterns in an SQL Query Log

Today, many scientific data sets are open to the public. For their operators, it is important to know what the users are interested in. In this paper, we study the problem of extracting and analyzing patterns from the query log of a database. We focus on design errors (antipatterns), which typically lead to unnecessary SQL statements. Such antipatterns do not only have a negative effect on performance. They also introduce bias on any subsequent analysis of the SQL log. We propose a framework designed to discover patterns and antipatterns in arbitrary SQL query logs and to clean antipatterns. To study the usefulness of our approach and to reveal insights regarding the existence of antipatterns in real-world systems, we examine the SQL log of the SkyServer project, containing more than 40 million queries. Among the top 15 patterns, we have found six antipatterns. This result as well as other ones gives way to the conclusion that antipatterns might falsify refactoring and any other downstream analyses.