Hasso-plattner-institut Für Digital Engineering Ggmbh

Rainer Schlosser

Hasso Plattner

Matthias Uflacker

Martin Boissier

Jan Kossmann

Michael Perscheid

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

Computer-implemented method for allocating columns of a table stored in a database system into at least two sets of columns, a first set and a second set, the data of the columns allocated to the first set being stored on a primary storage medium using a column-oriented data structure and the data of columns allocated to the second set being stored on a secondary storage medium using a row-oriented data structure, wherein the method comprises the step of allocating the columns of the table to the first and the second set autonomously on the basis of a performance-cost model.

Growing community of inventors

Berlin, Germany

Rainer Schlosser