Abstract: A method and system for managing execution of a query that provides an avenue for runtime optimization, or more specifically runtime re-optimization. This allows for more efficient execution plans to be generated, and reduces the risk of major complications due to inadvertently poor estimates. In other cases such a query management system is used for alternate purposes, such as throttling system resource consumption for demanding queries.

Abstract: A method and system for managing indexes used to retrieve data from a database stored on a computer, includes determining whether any marginal indexes exist in the database, and removing one or more of the marginal indexes from the database. An index is considered marginal when: (1) it is an access path for one or more queries but the index's performance is matched or nearly matched by one or more other alternative access paths, and/or (2) its overhead has become a concern. These steps or functions are invoked by one or more statements or instructions that cause the computer to perform a Drop Index Analysis on a specified list of tables in the database for a specified workload. The Drop Index Analysis allows users to specify: (1) how many indexes to consider dropping, and/or (2) how much disk space that dropped indexes should free up.

Abstract: A system and method for identifying a selection of index candidates for a relational database. The system includes a Predicate Analyzer for examining database queries in workload and recording column and predicate information; a Search Engine for performing a combinatorial search of index candidates; an Index Emulator for emulating the existence of a set of candidate indexes by making necessary “fake” entries into the relational database management system data dictionary; and a Query Optimizer for estimating the cost of a given query within a given index configuration. In accordance with the method, data indicative of a workload comprising one or more queries is received, and predicates are then analysed and organized into column groups for each basic predicate type. Statistics are collected on each of these groups. Non-predicate portions of each query are scanned to obtain additional covering columns. A combinational search is performed on the column groups to identify covering and non-covering candidates.