Database management systems (DBMS) expose dozens of configurable knobs that control their runtime behavior.
Setting these knobs correctly for an application’s workload can improve the performance and efficiency of the DBMS. But such tuning requires considerable efforts from experienced administrators, which is not scalable for large DBMS fleets. This problem has led to research on using machine learning (ML) to devise strategies to optimize DBMS knobs for any application automatically. The OtterTune database tuning service from Carnegie Mellon uses ML to generate and install optimized DBMS configurations. OtterTune observes the DBMS’s workload through its metrics and then trains recommendation models that select better knob values. It then reuses these models to tune other DBMSs more quickly.
In this talk, I will present an overview of OtterTune and discuss the challenges one must overcome to deploy an ML-based service for DBMSs. I will also highlight the insights we learned from real-world installations of OtterTune to tune MySQL, PostgreSQL, and Oracle.
Speaker: Andy Pavlo – OtterTune