Learning an Index Advisor with Deep Reinforcement Learning

Abstract

Indexes are crucial for the efficient processing of database workloads and an appropriately selected set of indexes can drastically improve query processing performance. However, the selection of beneficial indexes is a non-trivial problem and still challenging. Recent work in deep reinforcement learning (DRL) may bring a new perspective on this problem. In this paper, we studied the index selection problem in the context of reinforcement learning and proposed an end-to-end DRL-based index selection framework. The framework poses the index selection problem as a series of 1-step single index recommendation tasks and can learn from data. Unlike most existing DRL-based index selection solutions that focus on selecting single-column indexes, our framework can recommend both single-column and multi-column indexes for the database. A set of comparative experiments with existing solutions was conducted to demonstrate the effectiveness of our proposed method.