A demonstration of the ottertune automatic database management system tuning service

AbstractDatabase management systems (DBMSs) have a plethora of tunable knobs that control almost everything in the system. The performance of a DBMS is highly dependent on these configuration knobs, however, getting this tuning right is hard. Many organizations resort to hiring experts to configure these knobs, but this is prohibitively expensive. As databases grow in both size and complexity, optimizing a DBMS has surpassed the abilities of even the best human experts. We recently introduced OtterTune, a tuning service that is able to automatically find good settings for a DBMS's configuration knobs. OtterTune leverages data collected from previous tuning efforts to train machine learning models, and recommends new configurations that are as good as or better than ones generated by existing tools or a human expert. In this demonstration, we showcase OtterTune's ability to automatically select a configuration that improves a DBMS's performance.