Modern database systems such as IBM Db2 have many configurable parameters (“knobs”) which must be precisely adjusted (“tuned”) to ensure optimal workload performance. Manual tuning of these settings is challenging, even for seasoned experts. We introduce the design of, and a demonstration plan for, Db2une, an automated, query-aware tuning system employing deep-learning techniques to enhance performance while also conserving resources. We showcase how Db2une interactively tunes data systems to boost performance, and how its query representation model, QBERT, aids users in understanding the query plans generated from complex analytical workloads.

Modern database systems, such as IBM Db2, rely on cost-based optimizers to improve workload performance. However, their decision-making processes are difficult to interpret. Tuning them for specific workloads remains challenging due to their complexity and numerous configuration options. Automatic tuning tools often rely on black-box machine-learning models, which lack interpretability, hindering expert trust and debugging. We present GEX, a system that provides interpretable insights into database optimizer behavior using explainable AI techniques. By employing saliency maps generated from surrogate models, GEX guides experts in system tuning tasks such as statistical view creation, configuration parameter adjustment, and query rewrite. Our experimental results demonstrate that GEX enhances performance and ensures transparency, addressing key challenges in data systems tuning.

Modern database systems including IBM Db2 have numerous parameters, "knobs," that require precise configuration to achieve optimal workload performance. Even for experts, manually "tuning" these knobs is a challenging process. We present Db2une, an automatic query-aware tuning system that leverages deep learning to maximize performance while minimizing resource usage. Via a specialized transformer-based query-embedding pipeline we name QBERT, Db2une generates context-aware representations of query workloads to feed as input to a stability-oriented, on-policy deep reinforcement learning model. In Db2une, we introduce a multi-phased, database meta-data driven training approach---which incorporates cost estimates, interpolation of these costs, and database statistics---to efficiently discover optimal tuning configurations without the need to execute queries. Thus, our model can scale to very large workloads, for which executing queries would be prohibitively expensive. Through experimental evaluation, we demonstrate Db2une's efficiency and effectiveness over a variety of workloads. We compare it against the state-of-the-art query-aware tuning systems and show that the system provides recommendations that surpass those of IBM experts.