Abstract: A database query optimizer for a computer system having dynamically configurable logical partitions generates an optimized query strategy which is dependent on a logical partition configuration. When the query is executed, the configuration of the logical partition in which the query is executed is compared to the logical partition configuration for which the query was optimized. If the configurations are different, a new query can be automatically generated. Optimizing database queries to the current system configuration of a dynamic, logically partitioned system potentially offers greater efficiency in the execution of database queries for complex, logically partitioned systems.

Abstract: A database query optimizer for a computer system having dynamically configurable logical partitions generates an optimized query strategy which is dependent on a logical partition configuration. When the query is executed, the configuration of the logical partition in which the query is executed is compared to the logical partition configuration for which the query was optimized. If the configurations are different, a new query can be automatically generated. Optimizing database queries to the current system configuration of a dynamic, logically partitioned system potentially offers greater efficiency in the execution of database queries for complex, logically partitioned systems.

Abstract: A database query optimizer for a computer system having dynamically configurable logical partitions generates an optimized query strategy which is dependent on a logical partition configuration. When the query is executed, the configuration of the logical partition in which the query is executed is compared to the logical partition configuration for which the query was optimized. If the configurations are different, a new query can be automatically generated. Optimizing database queries to the current system configuration of a dynamic, logically partitioned system potentially offers greater efficiency in the execution of database queries for complex, logically partitioned systems.

Abstract: A metadata manager is used in a database management system to collect and maintain metadata associated with a database. Multiple optimizer instances are permitted to access the metadata maintained by the metadata manager, often eliminating the need for individual optimizer instances to retrieve and process metadata directly from the database. As such, the overhead associated with operations such as collecting, refining, retrieving and/or maintaining of metadata can be off-loaded from the optimizer instances, often accelerating individual cost estimation calculations by optimizer instances, facilitating reuse of metadata calculations and refinements, and improving metadata consistency between multiple related cost estimates.

Abstract: A database query optimizer for a computer system having dynamically configurable logical partitions generates an optimized query strategy which is dependent on a logical partition configuration. When the query is executed, the configuration of the logical partition in which the query is executed is compared to the logical partition configuration for which the query was optimized. If the configurations are different, a new query can be automatically generated. Optimizing database queries to the current system configuration of a dynamic, logically partitioned system potentially offers greater efficiency in the execution of database queries for complex, logically partitioned systems.

Abstract: A metadata manager is used in a database management system to collect and maintain metadata associated with a database. Multiple optimizer instances are permitted to access the metadata maintained by the metadata manager, often eliminating the need for individual optimizer instances to retrieve and process metadata directly from the database. As such, the overhead associated with operations such as collecting, refining, retrieving and/or maintaining of metadata can be off-loaded from the optimizer instances, often accelerating individual cost estimation calculations by optimizer instances, facilitating reuse of metadata calculations and refinements, and improving metadata consistency between multiple related cost estimates.

Abstract: A database query optimizer for a computer system having dynamically configurable logical partitions generates an optimized query strategy which is dependent on a logical partition configuration. When the query is executed, the configuration of the logical partition in which the query is executed is compared to the logical partition configuration for which the query was optimized. If the configurations are different, a new query can be automatically generated. Optimizing database queries to the current system configuration of a dynamic, logically partitioned system potentially offers greater efficiency in the execution of database queries for complex, logically partitioned systems.

Abstract: An apparatus and method to significantly improve performance of SQL function processing through the use of an encoded vector index (EVI). An EVI provides the data necessary to count the number of records in a database that match criteria provided by a SQL function. The resulting count can then be used to determine, from among two or more candidate approaches, an approach to use in processing an SQL function. By processing the EVI symbol table in lieu of more traditional database indexes, and/or in lieu of the database table itself, statistics for SQL functions are generated significantly faster.

Abstract: Method and system aspects for efficiently searching an encoded vector index are provided. The aspects include the translation of a search query into a candidate bitmap, and the mapping of data from the candidate bitmap into a search result bitmap according to entry values in the encoded vector index. Further, the translation includes the setting of a bit in the candidate bitmap for each entry in a symbol table that corresponds to candidate of the search query. Also included in the mapping is the identification of a bit value in the candidate bitmap pointed to by an entry in an encoded vector.

Abstract: A method and computer program product are provided for implementing skip key processing for database grouping queries involving aggregate operations using one or more indices in a computer system. First checking for a selected entry in a current group in a primary file is performed. Responsive to finding the selected entry in the current group, the set of records of the current group is ordered to provide an aggregate value in an aggregate field of the current group. Then checking for an excluded value in the aggregate field of the current group is performed. Responsive to not finding the excluded value in the aggregate field, a current entry is saved as a result for the group. Then checking for more groups in the primary file is performed. Responsive to finding more groups in the primary file, a unique key or prefix to the query index is obtained to position to a next group. When the selected entry in the next group is not found, then checking for more groups in the primary file is performed.

Abstract: A method and computer program product are provided for processing subquery join composites using hash join in a computer system. A hash table is built for a first table to be joined with a second table. A cursor is built for the second table. For each tuple in the second table, the hash table for the first table is probed. If a match is found, then any non-equal join predicate is evaluated. A unique fanout join is processed to return an error if a tuple or row from the second table joins to more than one row in the first table where the result of the basic predicate subquery can only be one row. A distinct fanout join is processed when the tuple or row from the second table joins to a row in the first table, the row is returned, advancing to a next tuple in the second table without checking for any more matches on the first table.

Abstract: A method is provided for parallel and cooperative processing of data in a system wherein a coordinator process cooperates with one or more agent processes to which portions of the data processing function is off loaded. The agent processes read and process the data and accumulate a partial result. Each agent process, responsive to statistics collected on the content of the data processed, returns a partial result of the processing to the coordinator process. These steps are repeated iteratively until the processing has been completed. In a specific application, the performance of data processing systems is improved by speeding up database group-by queries. The group-by operation processing is distributed between the host central processing unit (CPU) and the input/output (I/O) processors (IOPs). Essentially, the IOPs are sent group-by requests to be performed on a set of disk blocks (extents), along with a predicate for tuples to be selected for query.