Abstract: Methods, apparatuses, and computer program products for materialized query table matching are provided that include receiving a database query, retrieving a materialized query table (MQT) from data storage accessible to a database management system, selecting a join in the materialized query table between a table that is not invoked by the query and a table that is invoked in the query, determining whether the selected join is lossless, and expanding the query to include the lossless join if the selected join is lossless, expanding the query to include the lossless join. Determining whether the selected join is lossless may be carried out by losslessly joining the table that is not invoked by the query to the query, and determining whether the lossless join to the query matches the selected join of the materialized query 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: Optimizers must work with numeric data from a variety of sources including column statistics, estimated filter factors, record counts estimated costs and the like. Embodiments provided herein define and represent any such numeric measurements as a Vector of N dimensions, where n is a number of aspects of measurements that a particular optimizer is configured to consider. A particular embodiment provides a 4-dimensional vector where the dimensions represent magnitude, confidence, variance interval, and penalty. Examples of measurements considered by the optimizer, and which may be represented as a vector, include cost, estimated selectivity of a predicate, estimated number of records returned from part or all of a query, estimated record fanout when joining one file to another, etc.

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.

Abstract: Optimizers must work with numeric data from a variety of sources including column statistics, estimated filter factors, record counts estimated costs and the like. Embodiments provided herein define and represent any such numeric measurements as a Vector of N dimensions, where n is a number of aspects of measurements that a particular optimizer is configured to consider. A particular embodiment provides a 4-dimensional vector where the dimensions represent magnitude, confidence, variance interval, and penalty. Examples of measurements considered by the optimizer, and which may be represented as a vector, include cost, estimated selectivity of a predicate, estimated number of records returned from part or all of a query, estimated record fanout when joining one file to another, etc.

Abstract: At the most general level, the I/O framework mechanism of the present invention is made up of three interdependent controllers. These controllers are referred to herein as the hardware resource administrator, the information controller, and the device controller. The hardware resource administrator is responsible for organizing information about I/O devices and for making the organized information available to the other controllers. The information controller is responsible for gathering information about I/O devices and for changing and/or updating certain I/O device information. Accordingly, the information controller is made up of individual objects that each represent the characteristics of a particular I/O device. The device controller is responsible for controlling the actual operation of the individual devices, and for performing statistical and diagnostic analysis on the individual I/O devices.