Learning an Index Advisor with Deep Reinforcement Learning

摘要: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.