The pressing need for achieving and maintaining high performance in database systems has made database administration one of the most stressful jobs in information technology. On the other hand, the increasing complexity of database systems has made qualified database administrators (DBAs) a scarce resource. DBAs are now responsible for an array of demanding tasks; they need to (i) provision and tune their database according to their application requirements, (ii) constantly monitor their database for any performance failures or slowdowns, (iii) diagnose the root cause of the performance problem in an accurate and timely fashion, and (iv) take prompt actions that can restore acceptable database performance.

However, much of the research in the past years has focused on improving the raw performance of the database systems, rather than improving their manageability. Besides sophisticated consoles for monitoring performance and a few auto-tuning wizards, DBAs are not provided with any help other than their own many years of experience. Typically, their only resort is trial-and-error, which is a tedious, ad-hoc and often sub-optimal solution.

In this demonstration, we present DBSeer, a workload intelligence framework that exploits advanced machine learning and causality techniques to aid DBAs in their various responsibilities. DBSeer analyzes large volumes of statistics and telemetry data collected from various log files to provide the DBA with a suite of rich functionalities including performance prediction, performance diagnosis, bottleneck explanation, workload insight, optimal admission control, and what-if analysis. In this demo, we showcase various features of DBSeer by predicting and analyzing the performance of a live database system. Will also reproduce a number of realistic performance problems in the system, and allow the audience to use DBSeer to quickly diagnose and resolve their root cause.