Nowadays, data systems, including IBM Db2 have dozens of knobs (configuration parameters). These knobs significantly affect the runtime of queries. We present the design of and a demonstration plan for a query-informed, efficient tuning system, BLUTune, which utilizes deep reinforcement learning to tune configurations. Using synthetic and real workloads, we demonstrate how BLUTune can help users to understand the semantics of analytical queries, including their execution plans, and interactively tune data systems to improve performance.

Modern data systems such as IBM Db2 have hundreds of system configuration parameters, ''knobs", which heavily influence the performance of business queries. Manual configuration, ''tuning," by experts is painstaking and time consuming. We propose a query informed tuning system called BLUTune which uses machine learning (ML)-deep reinforcement learning based on advantage actor critic neural networks-to tune configurations within defined resource constraints. We translate high-dimensional query execution plans (QEPs) into a low-dimensional embedding space (QEP2Vec) for input into the ML models. To scale to complex and large workloads, we bootstrap the training process through transfer learning. We first train our model based on the estimated cost of queries; we then fine-tune it based on actual query execution times. We demonstrate by an experimental study over various synthetic and real-world workloads BLUTune's efficiency and effectiveness.