PROCESSING WINDOW PARTITIONING AND ORDERING FOR ON-LINE ANALYTICAL PROCESSING (OLAP) FUNCTIONS

• US 20170060953A1
• Filed: 08/28/2015
• Published: 03/02/2017
• Est. Priority Date: 08/28/2015
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Lin, Eileen T., Misra, Shivin S., Zabka, Michael R., Ackerman, William B., Meyers, David L.
• Granted Patent: US 10,089,356 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/2379 Updates performed during online database operations; commit processing
• CPC Class Codes: G06F 16/2455 Query execution
• CPC Class Codes: G06F 16/24554 Unary operations; Data partitioning operations
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP

Abstract

Provided are techniques for processing window partitioning and ordering for OLAP functions. A prior compare operation is performed by: receiving an input vector for each of one or more attributes of input data that represent one of a partition-by column and an order-by column in a database query; generating a per-attribute comparison vector for each input vector; and producing a single output vector using each per-attribute comparison vector, wherein each value of the single output vector is a Boolean attribute whose value for a given tuple is true if a current value and a most recent prior value of any of the one or more attributes are different.

Claims

1-6. -6. (canceled)

7. A computer program product, the computer program product comprising a computer readable storage medium having program code embodied therewith, the program code executable by at least one processor to perform:

performing a prior compare operation by;

receiving an input vector for each of one or more attributes of input data that represent one of a partition-by column and an order-by column in a database query;

generating a per-attribute comparison vector for each input vector; and

producing a single output vector using each per-attribute comparison vector, wherein each value of the single output vector is a Boolean attribute whose value for a given tuple is true if a current value and a most recent prior value of any of the one or more attributes are different.

8. The computer program product of claim 7, wherein the prior compare operation is used to 1) identify a beginning of a new partition and 2) determine whether values of the one or more attributes have changed.

9. The computer program product of claim 7, wherein each value of the single output vector is a Boolean attribute whose value for a given tuple is false if the current value and the most recent prior value of all of the one or more attributes are identical.

10. The computer program product of claim 7, wherein the database query references multiple On-line Analytical Processing (OLAP) functions that each have different partitioning and ordering specifications and that share a sort.

11. The computer program product of claim 10, wherein the program code is executable by the at least one processor to perform:

identifying a minimal set of prior compare building blocks with a minimal number of scans over the input data for use by the multiple OLAP functions; and

computing remaining prior compare functions using the minimal set of the prior compare building blocks.

12. The computer program product of claim 7, wherein a Software as a Service (SaaS) is configured to perform the computer program product operations.

13. A computer system, comprising:

one or more processors, one or more computer-readable memories and one or more computer-readable, tangible storage devices; and

program instructions, stored on at least one of the one or more computer-readable, tangible storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to perform;

performing a prior compare operation by;

receiving an input vector for each of one or more attributes of input data that represent one of a partition-by column and an order-by column in a database query;

generating a per-attribute comparison vector for each input vector; and

producing a single output vector using each per-attribute comparison vector, wherein each value of the single output vector is a Boolean attribute whose value for a given tuple is true if a current value and a most recent prior value of any of the one or more attributes are different.

14. The computer system of claim 13, wherein the prior compare operation is used to 1) identify a beginning of a new partition and 2) determine whether values of the one or more attributes have changed.

15. The computer system of claim 13, wherein each value of the single output vector is a Boolean attribute whose value for a given tuple is false if the current value and the most recent prior value of all of the one or more attributes are identical.

16. The computer system of claim 13, wherein the database query references multiple On-line Analytical Processing (OLAP) functions that each have different partitioning and ordering specifications and that share a sort.

17. The computer system of claim 16, wherein the operations further comprise:

identifying a minimal set of prior compare building blocks with a minimal number of scans over the input data for use by the multiple OLAP functions; and

computing remaining prior compare functions using the minimal set of the prior compare building blocks.

18. The computer system of claim 13, wherein a Software as a Service (SaaS) is configured to perform the system operations.