The ParAccel Analytic Database is a fast shared-nothing parallel relational database system with a columnar orientation, adaptive compression, memory-centric design, and an enhanced query optimizer. This modern object-oriented optimizer and its optimizer framework, known as Volt, provide efficient bulk and instance level query expression representation, multiple expression managers, and rule and cost-based expression transformation organized via multiple optimizer instances. Volt has been applied to the problem of ordering very large numbers of joins by partially ordering them for subsequent optimization using standard dynamic programming. Performance analyses show the framework's utility and the optimizer's effectiveness.

Aggregate query processing in large data warehouses is computationally intensive. Precomputation is an approach that can be used to speed up aggregate queries. However, in order to make precomputation a truly viable solution to the aggregate query processing problem, it is important to identify the best set of aggregates to precompute and to use these precomputed aggregates effectively. The Red Brick aggregate computation and management system (Red Brick Vista) provides a complete server integrated solution to these problems.

Universal quantification is not supported directly in most database systems despite the fact that it adds significant power to a system's query processing and inference capabilities, in particular for the analysis of many-to-many relationships and of set-valued attributes. One of the main reasons for this omission has been that universal quantification algorithms and their performance have not been explored for large databases. In this article, we describe and compare three known algorithms and one recently proposed algorithm for relational division, the algebra operator that embodies universal quantification. For each algorithm, we investigate the performance effects of explicit duplicate removal and referential integrity enforcement, variants for inputs larger than memory, and parallel execution strategies. Analytical and experimental performance comparisons illustrate the substantial differences among the algorithms. Moreover, comparisons demonstrate that the recently proposed division algorithm evaluates a universal quantification predicate over two relations as fast as hash (semi-) join evaluates an existential quantification predicate over the same relations. Thus, existential and universal quantification can be supported with equal efficiency by adding the recently proposed algorithm to a query evaluation system. A second result of our study is that universal quantification should be expressed directly in a database query language, because most query optimizers do not recognize the rather indirect formulations available in SQL as relational division and therefore produce very poor evaluation plans for many universal quantification queries.

Traditional query optimizers assume accurate knowledge of run-time parameters such as selectivities and resource availability during plan optimization, i.e., at compile time. In reality, however, this assumption is often not justified. Therefore, the “static” plans produced by traditional optimizers may not be optimal for many of their actual run-time invocations. Instead, we propose a novel optimization model that assigns the bulk of the optimization effort to compile-time and delays carefully selected optimization decisions until run-time. Our previous work defined the run-time primitives, “dynamic plans” using “choose-plan” operators, for executing such delayed decisions, but did not solve the problem of constructing dynamic plans at compile-time. The present paper introduces techniques that solve this problem. Experience with a working prototype optimizer demonstrates (i) that the additional optimization and start-up overhead of dynamic plans compared to static plans is dominated by their advantage at run-time, (ii) that dynamic plans are as robust as the “brute-force” remedy of run-time optimization, i.e., dynamic plans maintain their optimality even if parameters change between compile-time and run-time, and (iii) that the start-up overhead of dynamic plans is significantly less than the time required for complete optimization at run-time. In other words, our proposed techniques are superior to both techniques considered to-date, namely compile-time optimization into a single static plan as well as run-time optimization. Finally, we believe that the concepts and technology described can be transferred to commercial query optimizers in order to improve the performance of embedded queries with host variables in the query predicate and to adapt to run-time system loads unpredictable at compile time.