LiPTool: A tool for learning-based autonomous index placement in databases

Nowadays, the distributed tree-based index has been widely adopted to process queries on large-scale data. Index placement is an essential part of distributed tree-based index management. Current research on index placement relies on predefined rules, such as user-supplied functions, load balance, and data location. They ignore the distance between the index position and the query data (denoted as locality), which significantly determines the index placement efficiency. However, the locality relies on several factors related to unpredictable queries, and it is difficult to consider all of the factors. Measuring query performance can comprehensively consider the above query-related factors. Therefore, we propose a novel method that uses deep reinforcement learning (DRL) to solve the problem and adopts the query performance as evaluation feedback in DRL. Based on this idea, we design a tool called LiPTool that learns the optimal index placement. Specifically, LiPTool employs deep reinforcement learning (DRL) to find the response server for every query, maximizing the average query performance of the workload. Then, LiPTool builds the index on each server according to the corresponding queries. Finally, LiPTool gives the suggestion of index placement. Moreover, this paper compares LiPTool with the other two methods on multiple datasets, and the results show that LiPTool doubles the query performance. We believe that LiPTool is a good solution for dynamically adjusting index placement in autonomous database management.