Db2une: Tuning IBM Db2 with Deep Learning

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.