Database management system (DBMS) configuration tuning is an essential aspect of any data-intensive application effort. But this is historically a difficult task because DBMSs have hundreds of configuration “knobs” that control everything in the system, such as the amount of memory to use for caches and how often data is written to storage. The problem with these knobs is that they are not standardized (i.e., two DBMSs use a different name for the same knob), not independent (i.e., changing one knob can impact others), and not universal (i.e., what works for one application may be sub-optimal for another).  Worse, information about the effects of the knobs typically comes only from (expensive) experience.

To overcome these challenges, we present an automated approach that leverages knowledge gained from previous tuning efforts to tune DBMS configurations.  We implemented our techniques in a new tool called OtterTune and tested it on two DBMSs. Our evaluation shows that OtterTune recommends configurations that are as good as or better than ones generated by existing tools or a human expert.