  Among daily tasks of database administrators (DBAs), the analysis of query
workloads to identify schema issues and improving performances is crucial.
Although DBAs can easily pinpoint queries repeatedly causing performance
issues, it remains challenging to automatically identify subsets of queries
that share some properties only (a pattern) and simultaneously foster some
target measures, such as execution time. Patterns are defined on combinations
of query clauses, environment variables, database alerts and metrics and help
answer questions like what makes SQL queries slow? What makes I/O
communications high? Automatically discovering these patterns in a huge search
space and providing them as hypotheses for helping to localize issues and
root-causes is important in the context of explainable AI. To tackle it, we
introduce an original approach rooted on Subgroup Discovery. We show how to
instantiate and develop this generic data-mining framework to identify
potential causes of SQL workloads issues. We believe that such data-mining
technique is not trivial to apply for DBAs. As such, we also provide a
visualization tool for interactive knowledge discovery. We analyse a one week
workload from hundreds of databases from our company, make both the dataset and
source code available, and experimentally show that insightful hypotheses can
be discovered.
