Abstract: A system and method for monitoring resource usage in a software system such as a database system is described. The user selects an application to monitor through a user-interface. On selection, one or more first-level statement groups associated with the application are displayed and run-time data for at least one of the displayed first-level statement groups is displayed. Illustrative run-time data indicating resource usage include the amount of processor time used to execute a first-level statement group. The user may then select a first-level statement group so that at least one second-level statement group associated with the selected first-level statement group is displayed, including various run-time data. When the selected application is a database application, a user may review run-time information associated with the execution of database packages, sections and individual SQL statements, including dynamic SQL statements.

Abstract: A system providing precise determination of resource use within a software system such as a database system. A user, such as a database administrator, operates a user interface to select and analyze portions of an application for resource use. In one embodiment in a DB2 database system individual SQL statement resource use can be determined. The invention is capable of uniquely identifying even dynamic SQL statements (i.e., Equalized I.D). Prediction information is presented for when a query, or other database task, will end based on selectable parameters such as optimizer cost, estimated cardinality, and delta delivery rates of data. A user can create or select event monitors to acquire statistical data on a database task. The results of activating the event monitor are presented to the user such that the user can progressively examine portions of an application, package, section and even individual statements.

Abstract: The present invention is a method for automating database bufferpool tuning for optimized performance that employs certain heuristic algorithms to achieve its goals. Over a period of time, memory (bufferpool) performance is measured and accumulated in a repository. The repository becomes a knowledge base that is accessed by the algorithms and the ideal memory (bufferpool) configurations, which optimize database performance, are learned and implemented. The sampling of performance continues at regular intervals and the knowledge base continues to grow. As knowledge continues to accumulate, the algorithms are forbidden from becoming complacent. The ideal bufferpool memory configurations are regularly reevaluated to ensure they continue to be optimal given potential changes in the database's use or access patterns.