An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning Ji Zhangx, Yu Liux, Ke Zhoux , Guoliang Liz, Zhili Xiaoy, Bin Chengy, Jiashu Xingy, Yangtao Wangx, Tianheng Chengx, Li Liux, Minwei Ranx, and Zekang Lix xWuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, China zTsinghua University, China, yTencent Inc., China {jizhang, liu_yu, k.zhou, ytwbruce, vic, lillian_hust, mwran, zekangli}@hust.edu.cn
[email protected]; {tomxiao, bencheng, flacroxing}@tencent.com ABSTRACT our model and improves efficiency of online tuning. We con- Configuration tuning is vital to optimize the performance ducted extensive experiments under 6 different workloads of database management system (DBMS). It becomes more on real cloud databases to demonstrate the superiority of tedious and urgent for cloud databases (CDB) due to the di- CDBTune. Experimental results showed that CDBTune had a verse database instances and query workloads, which make good adaptability and significantly outperformed the state- the database administrator (DBA) incompetent. Although of-the-art tuning tools and DBA experts. there are some studies on automatic DBMS configuration tuning, they have several limitations. Firstly, they adopt a 1 INTRODUCTION pipelined learning model but cannot optimize the overall The performance of database management systems (DBMSs) performance in an end-to-end manner. Secondly, they rely relies on hundreds of tunable configuration knobs. Supe- on large-scale high-quality training samples which are hard rior knob settings can improve the performance for DBMSs to obtain. Thirdly, there are a large number of knobs that (e.g., higher throughput and lower latency). However, only a are in continuous space and have unseen dependencies, and few experienced database administrators (DBAs) master the they cannot recommend reasonable configurations in such skills of setting appropriate knob configurations.