Abstract: An improved normalization methodology enabling better optimization of SQL queries is described. The normalization methodology effectively handles three-valued SQL search conditions and operates in reduced memory environments. A query is examined and selectively normalized by examining each segment of the query to determine whether it is advantageous to fully convert that segment to conjunctive normal form (CNF). If full conversion of the segment to CNF is not advantageous, then only the useful conjunctive conditions (prime implicates) that can be derived from the original input are saved. These useful conjuncts (prime implicates) are unioned with the original statement, resulting in a search condition that is semantically equivalent to the original expression, but that can be better exploited by a query optimizer. This enables better optimization of the original query without the system overhead that would result from full normalization of the query.

Abstract: A small-footprint relational database system providing a deterministic join enumeration methodology for left-deep processing trees is described. By providing a deterministic branch-and-bound join enumeration method for left-deep processing trees, the invention is able to efficiently optimize complex queries with high join degree by employing a novel approach to cost-based pruning of the search space. For each subquery, plan generation involves the following four distinct steps. First, the system adjusts predicate selectivities to account for disjuncts, Between predicates, and user estimates of selectivities. Next, the system constructs a join graph for the query that models inner and outer equijoin predicates, sargable single-variable predicates on single quantifiers, and Cartesian products. The system then enumerates join strategies and prune the search space using a branch-and-bound heuristic. Finally, the system recalls the cheapest strategy and constructs the detailed access plan for that strategy.

Abstract: A small-footprint relational database system providing a deterministic join enumeration methodology for left-deep processing trees is described. By providing a deterministic branch-and-bound join enumeration method for left-deep processing trees, the invention is able to efficiently optimize complex queries with high join degree by employing a novel approach to cost-based pruning of the search space. For each subquery, plan generation involves the following four distinct steps. First, the system adjusts predicate selectivities to account for disjuncts, Between predicates, and user estimates of selectivities. Next, the system constructs a join graph for the query that models inner and outer equijoin predicates, sargable single-variable predicates on single quantifiers, and Cartesian products. The system then enumerates join strategies and prune the search space using a branch-and-bound heuristic. Finally, the system recalls the cheapest strategy and constructs the detailed access plan for that strategy.