Abstract: While databases are widely-used in commercial user-facing services that have stringent quality-of-service (QoS) requirement, it is crucial to ensure their good performance and minimize the hardware usage at the same time. Our investigation shows that the optimal DBMS (database management system) software configuration varies for different user request patterns (i.e., workloads) and hardware configurations. It is challenging to identify the optimal software and hardware configurations for a database workload, because DBMSs have hundreds of tunable knobs, the effect of tuning a knob depends on other knobs, and the dependency relationship changes under different hardware configurations. In this paper, we propose SHA, a software and hardware auto-tuning system for DBMSs. SHA is comprised of a scaling-based performance predictor, a reinforcement learning (RL) based software tuner, and a QoS-aware resource reallocator. The performance predictor predicts its optimal performance with different hardware configurations and identifies the minimum amount of resources for satisfying its performance requirement. The software tuner fine-tunes the DBMS software knobs to optimize the performance of the workload. The resource reallocator assigns the saved resources to other applications to improve resource utilization without incurring QoS violation of the database workload. Experimental results show that SHA improves the performance of database workloads by 9.9% on average compared with a state-of-the-art solution when the hardware configuration is fixed, and improves 43.2% of resource utilization while ensuring the QoS.