On a new approach to the index selection problem using mining algorithms

Considering the wide usage of databases and their ever growing size, it is crucial to improve the query processing performance. Selection of an appropriate set of indexes for the workload processed by the database system is an important part of physical design and performance tuning. This selection is a non-trivial tasks, especially considering possible number of native indexes in modern databases. We introduce a new approach to the index selection problem using data mining. The method recommends the creation of indexes as well as the type of each index. This results in more precise index recommendations that allows not only to create ascending and descending indexes, but also special indexes supported by the database system. Mining of queries results in candidate indexes for which virtual indexes get created. As the approach does not require modifications of the database system, it is generically applicable. Evaluations of the scalability are given for different workloads for the document-based NoSQL database MongoDB.