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.

Query performance problem determination is usually performed manually in consultation with experts through the analysis of query plans. However, this is an excessively time consuming, human error-prone, and costly process. GALO is a novel system that automates this process. The tool automatically learns recurring problem patterns in query plans over workloads in an offline learning phase to build a knowledge base of plan rewrite remedies. GALO's knowledge base is built on RDF and SPARQL, which is well-suited for manipulating and querying over SQL query plans, which are graphs themselves. It then uses the knowledge base online to re-optimize queries queued for execution to improve performance, often quite dramatically.