LEARNING CAPACITY BY USING EXCESS CAPACITY IN A CAPACITY CONTROLLED ENVIRONMENT

• US 20130110773A1
• Filed: 12/20/2012
• Published: 05/02/2013
• Est. Priority Date: 09/30/2011
• Status: Active Grant

• Current Assignee: Teradata Corporation
• Original Assignee: Teradata Corporation
• Inventors: Burger, Louis, Julien, Thomas, Vandervort, Frank Roderic
• Granted Patent: US 9,767,148 B2
• Time in Patent Office: Days
• US Class Current: 707/609
• CPC Class Codes: G06F 16/2453 Query optimisation
• CPC Class Codes: G06F 16/24547 Optimisations to support specific applications; Extensibility of optimisers

Abstract

Excess capacity of a database system can be used for learning activities in a controlled computing environment. In this context, excess capacity of a database system can be used as needed and/or on a temporary basis. Furthermore, learning activities can be performed without requiring the use of the capacity that is configured or has been allotted for various other database operations, including those deemed, especially by the users of database system, to serve a main function or a more important purpose. As a result, learning can be performed without adversely affecting other operations deemed to be more critical, especially by the users of databases. Learning activities associated with a database environment can, for example, include learning operations directed to optimization of database queries, for example, by using a basic feedback or an expanded or active learning.

Claims

1. A method, implemented at least partly by a device, for learning by using excess capacity of a database system configured to operate at a limited capacity below its full capacity, wherein the database system includes one or more processors operable to process data stored in the database in a database environment, and wherein the method comprises:

allowing at least a portion of the excess capacity available to the database system to be used to perform one or more learning activities associated with learning about the database environment.

2. The method of claim 1, wherein the one or more learning operations include one or more operations associated with learning based analysis for optimizing database queries of the database.

3. The method of claim 2, wherein the one or more operations associated with learning based analysis are associated with one or more learning steps associated with execution of one or more database queries, wherein the one or more learning steps are not required for completion of the execution of the database query and generation of output of the database query.

4. The method claim 2, wherein the method further comprises:

selecting one or more database queries as candidates for finding potentially a better execution plan that a current plan known for execution of the one or more database queries; and

performing the one or more learning operations to learn about the cost of one or more other execution plans, different than the current execution plan, for executing the one or more selected database queries.

5. The method of claim 4, wherein the selected one or more database queries are selected as one or more database queries determined not to meet one or more criteria associated with a Service Level by using a current execution plan.

6. The method of claim 1, wherein the one or more learning operations include one or more operations for calculating one or more costs associated with one or more execution plans that are not currently identified as optimal execution plans for execution of one or more database queries of the database.

7. The method of claim 6, wherein the one or more costs include at least one of:

one or more results, one or more intermediate results, and one or more cardinalities.

8. The method of claim 1, wherein the method further comprises:

determining one or more parameters to configure the execution of the one or more learning operations by using the excess capacity.

9. The method of claim 8, wherein the determining of one or more parameters to configure one or more data management operations associated with the management of the data storage of the database system comprises one or more of the following:

configuring the excess capacity to complete the one or more learning operations;

determining an amount time to complete the one or more learning operations; and

determining one more priorities associated with the one or more one or mere learning operations.

10. The method of claim 9, wherein at least the determining of whether to perform one or more learning operation using the excess capacity automatically without requiring input from one or more users or administrators of the database system.

11. The method of claim 1, wherein the method further comprises:

not allowing one or more data operations, not associated with the learning of the database environment, to use the excess capacity of the database system.

12. The method of claim 1, wherein the method further comprises:

configuring at least a portion of the excess capacity only for the learning

not allowing any database queries associated with one or more users of the database system to use the configured excess capacity.

13. The method of claim 1, wherein the method further comprises:

configuring at least a portion of the excess capacity only for the learning to the extent needed to complete one or learning operations in a dynamic manner when it is determined to perform complete one or more data management and when the database system is operational and processing one or more database requests using the capacity which is not part of the excess capacity.

14. The method of claim 1, wherein the learning includes at least one of:

a basic learning technique, and an expended or active learning technique.

15. A device that includes one or more processors configured to:

Learn by using excess capacity of a database system configured to operate at a limited capacity below its full capacity, wherein the database system is operable to process data stored in the database in a database environment; and

allow at least a portion of the excess capacity available to the database system to be used to perform one or more learning activities associated with learning about the database environment.

16. A non-transitory computer readable storage medium storing at least executable code for learning by using excess capacity of a database system configured to operate at a limited capacity below its full capacity, wherein the database system includes one or more processors operable to process data stored in the database in a database environment, and wherein the executable code includes:

executable code that allows at least a portion of the excess capacity available to the database system to be used to perform one or more learning activities associated with learning about the database environment.