The Sybase ASE RDBMS version 15 was subject to major enhancements, including semantic partitions and a full QP rewrite. The new ASE QP supports horizontal and vertical parallel processing over semantically partitioned tables, and many other modern QP techniques, as cost-based eager aggregation and cost-based join relocation DQP. In the new query optimizer, the ordering, distinctness, aggregation, partitioning, and DQP optimizations were based on a common framework: plan fragment equivalence classes and logical properties. Our main outcomes are a) an eager enforcement policy for ordering, partitioning and DQP location; b) a distinctness and aggregation optimization policy, opportunistically based on the eager ordering enforcement, and which has an optimization-time computational complexity similar to join processing; c) support for the user to force all of the above optimizer decisions, still guaranteeing a valid plan, based on the Abstract Plan technology. We describe the implementation of this solution in the ASE 15 optimizer. Finally, we give our experimental results: the generation of such complex plans comes with a small increase of the optimizer's SS size, hence within an acceptable optimization time; at execution, we have obtained performance improvements of orders of magnitude for some queries.

We develop a data integration approach for the efficient evaluation of queries over autonomous source databases. The approach is based on some novel applications and extensions of constraint database techniques. We assume the existence of a global database schema. The contents of each data source are described using a set of constraint tuples over the global schema; each such tuple indicates possible contributions from the source. The "source description catalog" (SDC) of a global relation consists of its associated constraint tuples. Such a method of description is advantageous since it is flexible to add new sources and to modify existing ones. In our framework, to evaluate a conjunctive query over the global schema, a plan generator first identifies relevant data sources by "evaluating" the query against the SDCs using techniques of constraint query evaluation; it then formulates an evaluation plan, consisting of some specialized queries over different paths. The evaluation of a query associated with a path is done by a sequence of partial evaluations at data sources along the path, similar to sideways information passing of Datalog; the partially evaluated queries travel along their associated paths. Our SDC based query planning is efficient since it avoids the NP-complete query rewriting process. We can achieve further optimization using techniques such as emptiness test.