Abstract: Machine-learning-based, adaptive updating of quantitative data in a database system is provided, which includes training one or more machine learning models to facilitate adaptively updating quantitative data in the database system, and ascertaining an update urgency index for updating the quantitative data for one or more data structures of the database system. The update urgency index is representative of an urgency for updating the quantitative data for the data structure(s) and is based, at least in part, on real-time query metrics. The machine learning model(s) is used to adaptively update the quantitative data, where the adaptively updating is based, at least in part, on the ascertained update urgency index. Processing of a database query is optimized in the database system using the adaptively updated quantitative data.

Abstract: Machine-learning-based, adaptive updating of quantitative data in a database system is provided, which includes training one or more machine learning models to facilitate adaptively updating quantitative data in the database system, and ascertaining an update urgency index for updating the quantitative data for one or more data structures of the database system. The update urgency index is representative of an urgency for updating the quantitative data for the data structure(s) and is based, at least in part, on real-time query metrics. The machine learning model(s) is used to adaptively update the quantitative data, where the adaptively updating is based, at least in part, on the ascertained update urgency index. Processing of a database query is optimized in the database system using the adaptively updated quantitative data.

Abstract: Computer technology for use in a computer system including a database system including a database management sub-system, a primary database and a secondary database, with the secondary database being maintained at least approximately in synchronization with the primary database over time as changes are made to data of the primary database (for example, a database system that includes disaster recovery (DR) features). A statistics update is performed first on a standby database to determine whether query efficiency and/or query optimization really will be substantially improved by performance of the statistics update. If not, then statistics update on the primary database is postponed until a future time.

Abstract: Machine-learning-based, adaptive updating of quantitative data in a database system is provided, which includes training one or more machine learning models to facilitate adaptively updating quantitative data in the database system, and ascertaining an update urgency index for updating the quantitative data for one or more data structures of the database system. The update urgency index is representative of an urgency for updating the quantitative data for the data structure(s) and is based, at least in part, on real-time query metrics. The machine learning model(s) is used to adaptively update the quantitative data, where the adaptively updating is based, at least in part, on the ascertained update urgency index. Processing of a database query is optimized in the database system using the adaptively updated quantitative data.

Abstract: Embodiments relate to an eigenvalue-based data query. An aspect includes receiving a query request that includes a query statement. Another aspect includes calculating eigenvalues of key component elements in the query statement. Another aspect includes matching eigenvalues of nodes in an execution plan of a historical query statement to the eigenvalues of the key component elements. Yet another aspect includes based on determining success of matching the eigenvalues of the key component elements to the eigenvalues of the nodes in an execution plan of the historical query statement, generating an execution plan of the query statement.

Abstract: Embodiments relate to an eigenvalue-based data query. An aspect includes receiving a query request that includes a query statement. Another aspect includes calculating eigenvalues of key component elements in the query statement. Another aspect includes matching eigenvalues of nodes in an execution plan of a historical query statement to the eigenvalues of the key component elements. Yet another aspect includes based on determining success of matching the eigenvalues of the key component elements to the eigenvalues of the nodes in an execution plan of the historical query statement, generating an execution plan of the query statement.

Abstract: Embodiments relate to an eigenvalue-based data query. An aspect includes receiving a query request that includes a query statement. Another aspect includes calculating eigenvalues of key component elements in the query statement. Another aspect includes matching eigenvalues of nodes in an execution plan of a historical query statement to the eigenvalues of the key component elements. Yet another aspect includes based on determining success of matching the eigenvalues of the key component elements to the eigenvalues of the nodes in an execution plan of the historical query statement, generating an execution plan of the query statement.

Abstract: Embodiments relate to an eigenvalue-based data query. An aspect includes receiving a query request that includes a query statement. Another aspect includes calculating eigenvalues of key component elements in the query statement. Another aspect includes matching eigenvalues of nodes in an execution plan of a historical query statement to the eigenvalues of the key component elements. Yet another aspect includes based on determining success of matching the eigenvalues of the key component elements to the eigenvalues of the nodes in an execution plan of the historical query statement, generating an execution plan of the query statement.

Abstract: Embodiments relate to an eigenvalue-based data query. An aspect includes receiving a query request that includes a query statement. Another aspect includes calculating eigenvalues of key component elements in the query statement. Another aspect includes matching eigenvalues of nodes in an execution plan of a historical query statement to the eigenvalues of the key component elements. Yet another aspect includes based on determining success of matching the eigenvalues of the key component elements to the eigenvalues of the nodes in an execution plan of the historical query statement, generating an execution plan of the query statement.

Abstract: Embodiments relate to an eigenvalue-based data query. An aspect includes receiving a query request that includes a query statement. Another aspect includes calculating eigenvalues of key component elements in the query statement. Another aspect includes matching eigenvalues of nodes in an execution plan of a historical query statement to the eigenvalues of the key component elements. Yet another aspect includes based on determining success of matching the eigenvalues of the key component elements to the eigenvalues of the nodes in an execution plan of the historical query statement, generating an execution plan of the query statement.