Index Selection for NoSQL Database with Deep Reinforcement Learning
Databases
2020-06-17 v1 Artificial Intelligence
Abstract
We propose a new approach of NoSQL database index selection. For different workloads, we select different indexes and their different parameters to optimize the database performance. The approach builds a deep reinforcement learning model to select an optimal index for a given fixed workload and adapts to a changing workload. Experimental results show that, Deep Reinforcement Learning Index Selection Approach (DRLISA) has improved performance to varying degrees according to traditional single index structures.
Keywords
Cite
@article{arxiv.2006.08842,
title = {Index Selection for NoSQL Database with Deep Reinforcement Learning},
author = {Shun Yao and Hongzhi Wang and Yu Yan},
journal= {arXiv preprint arXiv:2006.08842},
year = {2020}
}