JAPO: learning join and pushdown order for cloud-native join optimization

Abstract

In this paper, we introduce JAPO which learn the join and pushdown order through DRL. The main idea is that the DRL agent learns better decisions based on the experiences by monitoring the rewards and latencies via trying different actions. The results show that our method can generate good plans both on join order and pushdown order. We also show that our method can select the well-performed distributed index placement via experiments. In the future, we plan to deploy JAPO to real systems execution and consider more factors in JAPO, such as different join types.