3 research outputs found
Roq: Robust Query Optimization Based on a Risk-aware Learned Cost Model
Query optimizers in relational database management systems (RDBMSs) search
for execution plans expected to be optimal for a given queries. They use
parameter estimates, often inaccurate, and make assumptions that may not hold
in practice. Consequently, they may select execution plans that are suboptimal
at runtime, when these estimates and assumptions are not valid, which may
result in poor query performance. Therefore, query optimizers do not
sufficiently support robust query optimization. Recent years have seen a surge
of interest in using machine learning (ML) to improve efficiency of data
systems and reduce their maintenance overheads, with promising results obtained
in the area of query optimization in particular. In this paper, inspired by
these advancements, and based on several years of experience of IBM Db2 in this
journey, we propose Robust Optimization of Queries, (Roq), a holistic framework
that enables robust query optimization based on a risk-aware learning approach.
Roq includes a novel formalization of the notion of robustness in the context
of query optimization and a principled approach for its quantification and
measurement based on approximate probabilistic ML. It also includes novel
strategies and algorithms for query plan evaluation and selection. Roq also
includes a novel learned cost model that is designed to predict query execution
cost and the associated risks and performs query optimization accordingly. We
demonstrate experimentally that Roq provides significant improvements to robust
query optimization compared to the state-of-the-art.Comment: 13 pages, 9 figures, submitted to SIGMOD 202