Index Selection for NoSQL Database with Deep Reinforcement Learning

TL;DR

This paper introduces DRLISA, a deep reinforcement learning-based method for selecting optimal indexes in NoSQL databases, improving performance across different workloads and adapting to workload changes.

Contribution

It presents a novel DRL-based approach for dynamic index selection in NoSQL databases, outperforming traditional single index strategies.

Findings

DRLISA improves database performance across various workloads.

The approach adapts to changing workload patterns effectively.

Experimental results demonstrate significant performance gains.

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.