Query-Aware Data Systems Tuning via Machine Learning

Modern data systems have hundreds of system configuration parameters which heavily influence the performance of business queries. Manual configuration by experts is painstaking and time consuming. We propose a query-informed tuning system called BLUTune which uses deep reinforcement learning based on advantage actor-critic neural networks to tune configurations within defined resource constraints. We translate high-dimensional query execution plans into a low-dimensional embedding space and illustrate the usefulness of query embeddings for the downstream task of data systems tuning. We train our model based on the estimated cost of queries then fine-tune it using query execution times. We present an experimental study over various synthetic and real-world workloads. One model uses TPC-DS queries such that there are tables from the schema that are not seen during training time. The second is trained under resource constraints to show how the model performs when we limit the memory the system has access to.