Abstract: A method, apparatus and program product are provided for performing a query of a database. A database query is received, which includes first and second operations respectively configured to generate first and second disjoint results sets from a dataspace. The database query is analyzed to identify a set of attributes from the dataspace that are used by at least one of the first and second operations in the database query. During execution of the database query, a plurality of records from the dataspace is iteratively processed in a single pass, including, for each of the plurality of records, processing such record by retrieving the plurality of attributes for such record from the dataspace and performing each of the first and second operations on the record using the retrieved attributes for such record to build the first and second disjoint results sets.

Abstract: A query optimizer optimizes a query to a partitioned database table by determining common characteristics of the partitions and generating a virtual maintained temporary index that spans multiple partitions. Using the virtual maintained temporary index allows the query optimizer to generate an access plan based on the virtual maintained temporary index, which relieves the optimizer from having to individually optimize access to each partition for partitions that share common characteristics.

Abstract: A method, apparatus and program product to optimize a database query. A query plan is generated for the database query where the query plan includes first and second subplans. Cached optimization data associated with the first subplan and generated during evaluation of another query plan for a previous query that uses the first subplan is retrieved. The first subplan is evaluated for resource cost using the retrieved optimization data. The second subplan is also evaluated for resource cost. The query plan is then evaluated for resource cost based upon the evaluations of resource cost for the first and second subplans. A query plan is selected based on evaluations of resource cost and the plan is executed to generate a result set.

Abstract: A method, apparatus and program product are provided for performing a query of a database. A database query is received, which includes first and second operations respectively configured to generate first and second disjoint results sets from a dataspace. The database query is analyzed to identify a set of attributes from the dataspace that are used by at least one of the first and second operations in the database query. During execution of the database query, a plurality of records from the dataspace is iteratively processed in a single pass, including, for each of the plurality of records, processing such record by retrieving the plurality of attributes for such record from the dataspace and performing each of the first and second operations on the record using the retrieved attributes for such record to build the first and second disjoint results sets.

Abstract: Methods, systems, and computer program products are provided for executing database rollup queries. Methods can include iterating through a database table which has been grouped and ordered on the different columns which are in the ROLLUP clause. In some embodiments, a GROUP BY ROLLUP construct can be executed while only requiring an additional one storage location per ordered column per each aggregate function to be performed on each database row. The higher level aggregate functions can be executed without relying on accessing any lower level aggregate results in some embodiments. A suitably grouped and ordered database table can have a multiple level hierarchical ROLLUP function executed in a single pass without having to retrieve lower level aggregate results.

Abstract: A method, apparatus and program product to optimize a database query. A query plan is generated for the database query where the query plan includes first and second subplans. Cached optimization data associated with the first subplan and generated during evaluation of another query plan for a previous query that uses the first subplan is retrieved. The first subplan is evaluated for resource cost using the retrieved optimization data. The second subplan is also evaluated for resource cost. The query plan is then evaluated for resource cost based upon the evaluations of resource cost for the first and second subplans. A query plan is selected based on evaluations of resource cost and the plan is executed to generate a result set.

Abstract: A query optimizer optimizes a query to a partitioned database table by determining common characteristics of the partitions and generating a virtual maintained temporary index that spans multiple partitions. Using the virtual maintained temporary index allows the query optimizer to generate an access plan based on the virtual maintained temporary index, which relieves the optimizer from having to individually optimize access to each partition for partitions that share common characteristics.

Abstract: An apparatus, program product and method identify a range of values in a table and advise the generation of a maintained index over the identified range of values. Additionally, a method that determines a range of values and generates a maintained temporary index is also provided. By doing so, the maintained index that was advised may be generated over the range of values in the column and used to access data in the table.

Abstract: An apparatus, program product and method utilize a query optimizer that identifies Join types in a query plan that can be replaced to facilitate optimization of the plan. If the query plan is considered to have a tree-graph representation, then the optimizer locates Join nodes to be replaced and substitutes an Inner Join node and additional nodes that do not inhibit optimization strategies and algorithms. As a result, all Outer Joins, whether left or right, and all Exception Joins, whether left or right, can be removed from the query plan before performing optimizations such as predicate movement, GROUP BY movement, ORDER BY movement, and Join enumeration.

Abstract: A database query optimizer constructs a graph comprising nodes, relations, and expressions. The query optimizer then constructs execution plans for sub-parts of the graph. The combination of execution plans make up the overall execution plan for the query. The execution plan information is appended to the graph itself, allowing changing an execution plan in one portion of the graph without necessarily changing execution plans in other portions of the graph. By representing a query using the graph of the preferred embodiments that includes execution plan information, the query optimizer is able to evaluate the execution plans of different options quickly and efficiently, thereby enhancing the performance of the query optimizer.

Abstract: An apparatus, program product and method utilize a query optimizer that relies upon a plurality of dynamically-dispatchable active strategy entities to generate an optimized execution plan for a database query. The plurality of active strategy entities form a optimizer framework from which the knowledge available to the optimizer, and the intelligence utilized by the optimizer in generating execution plans, may be represented in a structured, flexible, extensible and easily-modifiable manner.

Abstract: An apparatus, program product and method utilize a query optimizer that relies upon a plurality of dynamically-dispatchable active strategy entities to generate an optimized execution plan for a database query. The plurality of active strategy entities form a optimizer framework from which the knowledge available to the optimizer, and the intelligence utilized by the optimizer in generating execution plans, may be represented in a structured, flexible, extensible and easily-modifiable manner.

Abstract: A database query optimizer constructs a graph comprising nodes, relations, and expressions. The query optimizer then constructs execution plans for sub-parts of the graph. The combination of execution plans make up the overall execution plan for the query. The execution plan information is appended to the graph itself, allowing changing an execution plan in one portion of the graph without necessarily changing execution plans in other portions of the graph. By representing a query using the graph of the preferred embodiments that includes execution plan information, the query optimizer is able to evaluate the execution plans of different options quickly and efficiently, thereby enhancing the performance of the query optimizer.