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.

The Volcano project, which provides efficient, extensible tools for query and request processing, particularly for object-oriented and scientific database systems, is reviewed. In particular, one of its tools, the optimizer generator, is discussed. The data model, logical algebra, physical algebra, and optimization rules are translated by the optimizer generator into optimizer source code. It is shown that, compared with the EXODUS optimizer generator prototype, the search engine of the Volcano optimizer generator is more extensible and powerful. It provides effective support for non-trivial cost models and for physical properties such as sorting order. At the same time, it is much more efficient, as it combines dynamic programming with goal-directed searching and branch-and-bound pruning. Compared with other rule-based optimization systems, it provides complete data model independence and more natural extensibility.>

This paper reports our experiences building the query optimizer for TI's Open OODB system. To the best of our knowledge, it is the first working object query optimizer to be based on a complete extensible optimization framework including logical algebra, execution algorithms, property enforcers, logical transformation rules, implementation rules, and selectivity and cost estimation. Our algebra incorporates a new materialize operator with its corresponding logical transformation and implementation rules that enable the optimization of path expressions. Initial experiments on queries obtained from the object query optimization literature demonstrate that our optimizer is able to derive plans that are as efficient as, and often substantially more efficient than, the plans generated by other query optimization strategies. These experiments demonstrate that our initial choices for populating each part of our optimization framework are reasonable. Our experience also shows that having a complete optimization framework is crucial for two reasons. First, it allows the optimizer to discover plans that cannot be revealed by exploring only the alternatives provided by the logical algebra and its transformations. Second, it helps and forces the database system designer to consider all parts of the framework and to maintain a good balance of choices when incorporating a new logical operator, execution algorithm, transformation rule, or implementation rule. The Open OODB query optimizer was constructed using the Volcano Optimizer Generator, demonstrating that this second-generation optimizer generator enables rapid development of efficient and effective query optimizers for non-standard data models and systems.