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Learning a Partitioning Advisor for Cloud Databases

Benjamin Hilprecht; Carsten Binnig; Uwe Röhm
In: David Maier; Rachel Pottinger; AnHai Doan; Wang-Chiew Tan; Abdussalam Alawini; Hung Q. Ngo (Hrsg.). Proceedings of the 2020 International Conference on Management of Data. ACM SIGMOD International Conference on Management of Data (SIGMOD-2020), June 14-19, Pages 143-157, ACM, 2020.


Cloud vendors provide ready-to-use distributed DBMS solutions as a service. While the provisioning of a DBMS is usually fully automated, customers typically still have to make important design decisions which were traditionally made by the database administrator such as finding an optimal partitioning scheme for a given database schema and workload. In this paper, we introduce a new learned partitioning advisor based on Deep Reinforcement Learning (DRL) for OLAP-style workloads. The main idea is that a DRL agent learns the cost tradeoffs of different partitioning schemes and can thus automate the partitioning decision. In the evaluation, we show that our advisor is able to find non-trivial partitionings for a wide range of workloads and outperforms more classical approaches for automated partitioning design.

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