Index Selection for NoSQL Database with Deep Reinforcement Learning
This work addresses database performance optimization for users of NoSQL systems, but it appears incremental as it applies an existing method (deep reinforcement learning) to a specific domain.
The paper tackles the problem of NoSQL database index selection by using deep reinforcement learning to choose optimal indexes and parameters for different workloads, showing improved performance over traditional single index structures.
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.