Does machine learning for database optimization work in the real world

Database Management Systems (DBMSs) are complex software that require precise tuning to achieve optimal performance on specific hardware and workloads. However, manual tuning by experienced administrators becomes impractical for large-scale DBMS deployments. To address this challenge, there has been a growing trend in both academia and industry to employ machine learning (ML) for automatic database optimization. OtterTune is a notable example of such an approach.

However, how effective is machine learning for database tuning, and does it work in real-world scenarios? In this talk, I will explore the challenges and insights gained from the OtterTune journey, covering both technical and business aspects.