MANTIS: Multiple Type and Attribute Index Selection using Deep Reinforcement Learning
Vishal Sharma, Curtis Dyreson, Nicholas S. Flann · 2021
DBMS performance is dependent on many parameters, such as index selection, cache size, physical layout, and data partitioning. Some combinations of these parameters can lead to optimal performance for a given workload but selecting an optimal or near-optimal combination is challenging, especially for large databases with complex workloads. Among the hundreds of parameters, index selection is arguably the most critical parameter for performance. We propose a self-administered framework, called the Multiple Type and Attribute Index Selector (MANTIS), that automatically selects near-optimal indexes. The framework advances the state-of-the-art index selection by considering both multi-attribute and multiple types of indexes within a bounded storage size constraint, a combination not previously addressed. MANTIS combines supervised and reinforcement learning, a Deep Neural Network recommends the type of index for a given workload while a Deep Q-Learning network recommends the multi-attribute aspect. MANTIS is sensitive to storage cost constraints and incorporates noisy rewards in its reward function for better performance. Our experimental evaluation shows that MANTIS outperforms the current state-of-art methods by an average of 9.53% [email protected]