We'll explore the intricate world of PostgreSQL parameter tuning, where the database management system reveals a multitude of configurable parameters that govern its runtime behavior. As the system evolves, the increasing number, the non-linear relationship and the complexity of these parameters highlight the importance of their correct configuration in optimizing application performance and cloud resource utilization.

This talk delves into various performance tuning approaches for PostgreSQL. We'll start with the traditional, meticulous method of manual tuning, relying on expert analysis of metrics like query response times and resource usage. We'll then explore heuristic-based tools, offering one-size-fits-all solutions. Finally, we'll set our sights on the future: auto-tuning with machine learning. We'll showcase how these more sophisticated methods can be customized to a specific workload and machine resources.

We'll highlight the challenges and the advantages of transforming PostgreSQL into a more self-driving database management system. We'll share valuable lessons learned while developing DBtune, a machine-learning-powered optimization service designed to unlock the full potential of your PostgreSQL instance.