Enhancing an Extensible Query Optimizer with Support for Multiple Equivalence Types

Database management systems are continuously being extended with support for new types of data and more advanced querying capabilities. In large part because of this, query optimization has remained a very active area of research throughout the past two decades. At the same time, current commercial optimizers are hard to modify, to incorporate desired changes in, e.g., query algebras, transformation rules, search strategies. This has led to a number of research contributions that aim at creating extensible query optimizers. Examples include Starburst, Volcano, and OPT++. This paper reports on a study that has enhanced Volcano to support a relational algebra with added temporal operators, such as temporal join and aggregation. This includes the handling of algorithms and cost formulas for these new operators, six types of query equivalences, and accompanying query transformation rules. The paper shows how the Volcano search-space generation and plan-search algorithms were extended to support the six equivalence types, describes other key implementation tasks, and evaluates the extensibility of Volcano. Keywords—Query optimization; extensible query optimizers; extended relational algebra; implementation experiences; sets, multisets, and lists