Research Challenges in Deep Reinforcement Learning-based Join Query Optimization

Abstract

The order in which relations are joined and the physical join operators used are two aspects of query plans which have a significant impact on the execution latency of join queries. However, the set of valid query plans grows exponentially with the number of relations to be joined. Hence, it becomes computationally expensive to enumerate all such plans for a complex join query. Recently, several deep reinforcement learning (DRL) based approaches propose using neural networks to construct a query plan. They demonstrate that efficient query plans can be found without exhaustively enumerating the search space. We integrated our implementation of a DRL-based solution to optimize join order and operators into the PostgreSQL query optimizer. In practice, we found limitations in the quality of the query plans chosen which are not addressed in existing approaches. In this paper we highlight some of these limitations and propose future research challenges along with potential solutions.