Looking Deeply into the Magic Mirror: An Interactive Analysis of Database Index Selection Approaches

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Abstract

Indexes are important data structures for database tuning. However, finding the best indexes for a given workload is challenging. In this demonstration, we present our extensible open-source index selection evaluation platform and the corresponding interactive result analysis tool. The platform provides an automatic setup of the database, workload, and cost evaluation, which is otherwise often tedious work when evaluating index selection approaches. Users can also connect the platform to their own existing database and evaluate indexes for custom workloads. Our platform comprises multiple state-of-the-art index selection approaches, which can be used as baselines for new index selection proposals. Further, we present an application for thoroughly analyzing the selected database indexes. One can observe which indexes are used for which queries and their effect on processing costs. Also, it is possible to adapt the resulting index selections (i.e., add, remove, or change an index) and observe the impact. In this process, the application helps to understand the effects of indexes, improve index selections, and craft new index selection approaches.