International Business Machines Corporation

Dattatreya Govindappanavar

Samar T Desai

Gaurav Saxena

Uttam Setty

Apoorv Kapse

Prashant N Kapse

Mahadev Khapali

Mohan Narayanswamy

One or more processors provide selective and piecemeal data loading and utilization. One or more processors prioritize, for loading, a data of a first data type and a data of a second data type. One or more processors generate a partially loaded data table by partially loading the data of the first data type and the data of the second data type into a table within a relational database. One or more processors utilize the partially loaded data table to execute a plurality of computing tasks.

Selective and piecemeal data loading for computing efficiency

Upgrading a database while the database is accessible to applications. Specifically, the database upgrade is performed selectively as per demand from system catalog tables. With this on-demand technique, the upgrade of a database is run on a need basis to upgrade, over time, all the catalog objects.

On-demand database catalog upgrade

A method for updating a plurality of database statistics during a query execution is provided. The method may include receiving a compiled query. The method may also include determining if the received compiled query requires at least one table scan operation on a full table to resolve the received compiled query. The method may further include determining if a plurality of table statistics associated with the full table has not been updated within a pre-defined time-period, whereby the determining is based on the determination that the received compiled query requires at least one table scan operation on the full table to resolve the received compiled query. The method may additionally include collecting a plurality of statistics associated with the full table based on the determination that the plurality of table statistics associated with the full table has not been updated within the pre-defined time-period.

Updating database statistics during query execution

One or more processors determine a level of data usage for two or more types of data to be loaded in a relational database. One or more processors prioritize data of the two or more types for loading. One or more processors load data of the two or more types in an order based, at least in part, on the level of data usage for the two or more types of data. One or more processors load data of the two or more types into a table within the relational database, the data being loaded in two or more portions and at times based, at least in part, on an availability of computer resources.

One or more processors determine a level of data usage for two or more types of data to be loaded in a database. One or more processors prioritize data of the two or more types for loading. Data of a first data type has a higher priority for loading than data of a second data type when the first data type has a higher level of data usage than the second data type. One or more processors load data of the two or more types in an order based, at least in part, on the level of data usage for the two or more types of data.

A method for determining and providing aggregate functions is provided. The method may include scanning database tables. The method may further include determining the aggregate functions. Additionally, the method may include storing the determined aggregate functions on system catalog tables. The method may also include providing indications that the stored determined aggregate functions are valid. The method may further include receiving database queries. The method may also include, determining whether the stored determined aggregate functions are valid. The method may further include, in response to the stored determined aggregate functions being valid, retrieving the stored determined aggregate functions from the system catalog tables. The method may also include, in response to the stored determined aggregate functions being invalid, re-scanning the database tables. The method may further include determining the aggregate functions based on the re-scanned database tables. The method may further include presenting the aggregate functions to clients.

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Dattatreya Govindappanavar