Abstract: Disclosed aspects relate to database object management for a shared pool of configurable computing resources. A set of database object profile data is collected. The set of database object profile data is for a set of database objects. Based on the set of database object profile data, a set of database object priority values is determined. The set of database object priority values is for the set of database objects. Based on the set of database object priority values, a management action is performed. The management action is performed with respect to the set of database objects.

Abstract: In one embodiment, a method for improving cardinality estimation of a join predicate between a fact table and an overloaded dimension table is provided. The method includes receiving a dimension table and a fact table in a join predicate of one or more SQL statements. The method further includes identifying a majority of records in the fact table that refer to a subset of records in the dimension table. The method further includes computing a filter factor of the join predicate between the dimension table and the fact table. The method further includes creating a statistical view using one or more relevant portions of the dimension table that are referred to by the fact table.

Abstract: In one embodiment, a method for improving cardinality estimation of a join predicate between a fact table and an overloaded dimension table is provided. The method includes receiving a dimension table and a fact table in a join predicate of one or more SQL statements. The method further includes identifying a majority of records in the fact table that refer to a subset of records in the dimension table. The method further includes computing a filter factor of the join predicate between the dimension table and the fact table. The method further includes creating a statistical view using one or more relevant portions of the dimension table that are referred to by the fact table.

Abstract: In one embodiment, a method for improving cardinality estimation of a join predicate between a fact table and an overloaded dimension table is provided. The method includes receiving a dimension table and a fact table in a join predicate of one or more SQL statements. The method further includes identifying a majority of records in the fact table that refer to a subset of records in the dimension table. The method further includes computing a filter factor of the join predicate between the dimension table and the fact table. The method further includes creating a statistical view using one or more relevant portions of the dimension table that are referred to by the fact table.

Abstract: In one embodiment, a method for improving cardinality estimation of a join predicate between a fact table and an overloaded dimension table is provided. The method includes receiving a dimension table and a fact table in a join predicate of one or more SQL statements. The method further includes identifying a majority of records in the fact table that refer to a subset of records in the dimension table. The method further includes computing a filter factor of the join predicate between the dimension table and the fact table. The method further includes creating a statistical view using one or more relevant portions of the dimension table that are referred to by the fact table.

Abstract: In one embodiment, a method for improving cardinality estimation of a join predicate between a fact table and an overloaded dimension table is provided. The method includes receiving a dimension table and a fact table in a join predicate of one or more SQL statements. The method further includes identifying a majority of records in the fact table that refer to a subset of records in the dimension table. The method further includes computing a filter factor of the join predicate between the dimension table and the fact table. The method further includes creating a statistical view using one or more relevant portions of the dimension table that are referred to by the fact table.

Abstract: In one embodiment, a method for improving cardinality estimation of a join predicate between a fact table and an overloaded dimension table is provided. The method includes receiving a dimension table and a fact table in a join predicate of one or more SQL statements. The method further includes identifying a majority of records in the fact table that refer to a subset of records in the dimension table. The method further includes computing a filter factor of the join predicate between the dimension table and the fact table. The method further includes creating a statistical view using one or more relevant portions of the dimension table that are referred to by the fact table.

Abstract: Disclosed aspects relate to database object management for a shared pool of configurable computing resources. A set of database object profile data is collected. The set of database object profile data is for a set of database objects. Based on the set of database object profile data, a set of database object priority values is determined. The set of database object priority values is for the set of database objects. Based on the set of database object priority values, a management action is performed. The management action is performed with respect to the set of database objects.

Abstract: In one embodiment, a method for improving cardinality estimation of a join predicate between a fact table and an overloaded dimension table is provided. The method includes receiving a dimension table and a fact table in a join predicate of one or more SQL statements. The method further includes identifying a majority of records in the fact table that refer to a subset of records in the dimension table. The method further includes computing a filter factor of the join predicate between the dimension table and the fact table. The method further includes creating a statistical view using one or more relevant portions of the dimension table that are referred to by the fact table.

Abstract: In one embodiment, a method for improving cardinality estimation of a join predicate between a fact table and an overloaded dimension table is provided. The method includes receiving a dimension table and a fact table in a join predicate of one or more SQL statements. The method further includes identifying a majority of records in the fact table that refer to a subset of records in the dimension table. The method further includes computing a filter factor of the join predicate between the dimension table and the fact table. The method further includes creating a statistical view using one or more relevant portions of the dimension table that are referred to by the fact table.

Abstract: A method for managing access to data in a database system includes receiving requests to update data stored in one or more fields of a record in a database table. The method further identifies lock information of the one or more fields from a look-up table associated with the record. The look-up table associated with the record includes lock information of each field of the record. The method then handles the requests based on the identified lock information of the one or more fields.

Abstract: A method for managing access to data in a database system includes receiving requests to update data stored in one or more fields of a record in a database table. The method further identifies lock information of the one or more fields from a look-up table associated with the record. The look-up table associated with the record includes lock information of each field of the record. The method then handles the requests based on the identified lock information of the one or more fields.

Abstract: A trace file providing details of an execution of a software application experiencing unexpected behavior can be identified. The trace file can be converted into a graph structure (e.g., Function Call Graph), which details functions called during the execution, a calling relationship, and errors encountered during the execution. The converted graph structure can be programmatically matched against a set of stored graph structures to determine matched results. Each stored graph structure can correspond to unique record of a symptom database. Each unique record can be associated with a determined problem. The matched results can be provided as possible problems causing the unexpected behavior of the software application.