Hasso-plattner-institut Für Digital Engineering Ggmbh

Jan Kossmann

Hasso Plattner

Rainer Schlosser

Matthias Uflacker

Michael Perscheid

Martin Boissier

Alexander Kastius

The inventors have implemented in a columnar in-memory database and studied access patterns of a large production enterprise system. To obtain accurate cost estimates for a configuration, the inventors have used the what-if capabilities of modern query optimizers. What-if calls, however, are the major bottleneck for most index selection approaches. Hence, a major constraint is to limit the number of what-if optimizer calls. And even though the inventive approach does not limit the index candidate set, it decreases the number of what-if calls because in each iteration step the number of possible (index) extensions is comparably small which results in a limited number of what-if calls.

Iterative multi-attribute index selection for large database systems

Embodiments relate to a method for training an index selection agent for iteratively determining an index set I to be used when running queries against a database, the method being implemented in a computer system. Preferably, the index selection agent is based on Reinforcement Learning.

Training an agent for iterative multi-attribute index selection

Growing community of inventors

Berlin, Germany

Jan Kossmann