Multidimensional repositories for problem discovery and capacity planning of database applications

• US 7,617,312 B2
• Filed: 06/18/2004
• Issued: 11/10/2009
• Est. Priority Date: 04/16/1999
• Status: Active Grant

• Current Assignee: Oracle International Corporation (Oracle Corporation)
• Original Assignee: Oracle International Corporation (Oracle Corporation)
• Inventors: Tummalapalli, Venkat Ranga Reddy
• Primary Examiner(s): Winder; Patrice
• Assistant Examiner(s): CHOUDHURY, AZIZUL Q
• Application Number:
  US10/871,074
• Publication Number:
  US 20040243607A1
• Time in Patent Office: 1,971 Days
• Field of Search: None
• US Class Current: 709/224
• CPC Class Codes: H04L 41/06 Management of faults, events, alarms or notifications
• CPC Class Codes: H04L 41/5009 Determining service level performance parameters or violations of service level contracts, e.g. violations of agreed response time or mean time between failures [MTBF]
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching

Abstract

A distributed decision support system is described for use in the area of problem discovery and capacity planning for database applications. The use of data navigation, particularly drill down, roll up, and pivot for application management tasks are described. The system presents data collected from components of an application server system by various cartridges to users in a way that facilitates the identification of problems in the application server system and provides for an efficient way to do capacity planning of such systems.

Claims

1. A computer implemented method, comprising the steps of:

sampling operational characteristics of a component to obtain data reflecting performance of the component;

storing the data into a storage according to an organization including at least a time dimension and a component dimension;

wherein the time dimension is a list of time intervals that comprises first time intervals that correspond to a coarser level of granularity and second time intervals that correspond to a finer level of granularity, wherein the first time intervals correspond to longer time periods than the second time intervals;

wherein each time interval of the first time intervals corresponds to a plurality of the second time intervals;

wherein all of the second time intervals have a same fixed length;

wherein the time dimension is a hierarchical dimension in which the first time intervals and the second time intervals have a hierarchical relationship with respect to each other;

wherein the component dimension is a list of values that comprises first values that correspond to a coarser level of component granularity and second values that correspond to a finer level of component granularity, wherein the first values correspond to first component elements that include second component elements corresponding to the second values;

wherein each value of the first values corresponds to a plurality of the second values;

wherein the component dimension is a hierarchical dimension in which the first values and the second values have a hierarchical relationship with respect to each other; and

performing an operation on the data in the storage, wherein the operation is one of a drill down operation that involves transforming the data at the coarser level of granularity to the data at the finer level of granularity, or a rollup operation that involves transforming the data at the finer level of granularity to the data at the coarser level of granularity;

wherein performing the operation on the data in the storage further comprises performing one of a drill down operation that involves transforming the data at the coarser level of component granularity to the data at the finer level of component granularity, or a rollup operation that involves transforming the data at the finer level of component granularity to the data at the coarser level of component granularity;

wherein the steps of the method are performed by one or more computing devices.

2. A computer implemented method as recited in claim 1, wherein the step of sampling operational characteristics of the component to obtain data reflecting performance of the component further comprises the step of:

making a measurement dataspace sample at a time t1.

3. A computer implemented method as recited in claim 1, wherein the step of storing the data into a storage according to an organization including at least a time dimension and a component dimension further comprises the steps of:

comparing a first measurement dataspace sampled at a time t1 with a second measurement dataspace sampled at a time t2, and if the second measurement dataspace deviates from the first measurement dataspace by a deviation threshold, generating a report.

4. A computer implemented method as recited in claim 1, wherein the step of performing the operation on the data in the storage further comprises the steps of:

comparing a first measurement dataspace sampled at a time t1 with a second measurement dataspace sampled at a time t2, and if the second measurement dataspace deviates from the first measurement dataspace by a deviation threshold, generating a report.

5. A computer implemented method as recited in claim 1, further comprising the step of:

dynamically generating a web page comprising a particular view of performance data responsive to a request.

6. A computer implemented method as recited in claim 1, further comprising the steps of:

comparing a measurement dataspace sampled at a time of processing a request with a level of service, and if the measurement dataspace deviates from the level of service by a deviation threshold, confirming that a subscription guarantee is not met.

21. A computer implemented method as recited in claim 1, wherein the component hosts at least one application.

22. A computer implemented method as recited in claim 1, wherein the operation on the data is the drill down operation.

23. A computer implemented method as recited in claim 1, further comprising performing a pivot operation on the data in the storage.

24. A computer implemented method as recited in claim 1, wherein the operation on the data is the rollup operation.

7. A computer readable storage medium storing one or more sequences of instructions which instructions, when executed by one or more processors, cause the one or more processors to carry out the steps of:

sampling operational characteristics of a component to obtain data reflecting performance of the component;

storing the data into a storage according to an organization including at least a time dimension and a component dimension;

wherein the time dimension is a list of time intervals that comprises first time intervals that correspond to a coarser level of granularity and second time intervals that correspond to a finer level of granularity, wherein the first time intervals correspond to longer time periods than the second time intervals;

wherein each time interval of the first time intervals corresponds to a plurality of the second time intervals;

wherein all of the second time intervals have a same fixed length;

wherein the time dimension is a hierarchical dimension in which the first time intervals and the second time intervals have a hierarchical relationship with respect to each other;

wherein the component dimension is a list of values that comprises first values that correspond to a coarser level of component granularity and second values that correspond to a finer level of component granularity, wherein the first values correspond to first component elements that include second component elements corresponding to the second values;

wherein each value of the first values corresponds to a plurality of the second values;

wherein the component dimension is a hierarchical dimension in which the first values and the second values have a hierarchical relationship with respect to each other; and

performing an operation on the data in the storage, wherein the operation is one of a drill down operation that involves transforming the data at the coarser level of granularity to the data at the finer level of granularity, or a rollup operation that involves transforming the data at the finer level of granularity to the data at the coarser level of granularity;

wherein performing the operation on the data in the storage further comprises performing one of a drill down operation that involves transforming the data at the coarser level of component granularity to the data at the finer level of component granularity, or a rollup operation that involves transforming the data at the finer level of component granularity to the data at the coarser level of component granularity.

8. A computer readable storage medium as recited in claim 7, wherein the instructions for carrying out the step of sampling operational characteristics of the component to obtain data reflecting performance of the component further comprises instructions for carrying out the step of:

making a measurement dataspace sample at a time t1.

9. A computer readable storage medium as recited in claim 7, wherein the instructions for carrying out the step of storing the data into a storage according to an organization including at least a time dimension and a component dimension further comprises instructions for carrying out the step of:

comparing a first measurement dataspace sampled at a time t1 with a second measurement dataspace sampled at a time t2, and if the second measurement dataspace deviates from the first measurement dataspace by a deviation threshold, generating a report.

10. A computer readable storage medium as recited in claim 7, wherein the instructions for carrying out the step of performing the operation on the data in the storage further comprises instructions for carrying out the step of:

comparing a first measurement dataspace sampled at a time t1 with a second measurement dataspace sampled at a time t2, and if the second measurement dataspace deviates from the first measurement dataspace by a deviation threshold, generating a report.

11. A computer readable storage medium as recited in claim 7, further comprising instructions for carrying out the step of:

dynamically generating a web page comprising a particular view of performance data responsive to a request.

12. A computer readable storage medium as recited in claim 7, further comprising instructions for carrying out the step of:

comparing a measurement dataspace sampled at a time of processing a request with a level of service, and if the measurement dataspace deviates from the level of service by a deviation threshold, confirming that a subscription guarantee is not met.

25. A computer readable storage medium as recited in claim 7, wherein the component hosts at least one application.

26. A computer readable storage medium as recited in claim 7, wherein the operation on the data is the drill down operation.

27. A computer readable storage medium as recited in claim 7, wherein the one or more sequences of instructions further comprise instructions which, when executed by the one or more processors, cause the one or more processors to carry out the step of performing a pivot operation on the data in the storage.

28. A computer readable storage medium as recited in claim 7, wherein the operation on the data is the rollup operation.

13. An apparatus comprising:

a processor;

a memory, connected to the processor by a bus; and

one or more stored sequences of instructions which, when executed by the processor, cause the processor to carry out the steps of;

sampling operational characteristics of a component to obtain data reflecting performance of the component;

storing the data into a storage according to an organization including at least a time dimension and a component dimension;

wherein the time dimension is a list of time intervals that comprises first time intervals that correspond to a coarser level of granularity and second time intervals that correspond to a finer level of granularity, wherein the first time intervals correspond to longer time periods than the second time intervals;

wherein each time interval of the first time intervals corresponds to a plurality of the second time intervals;

wherein all of the second time intervals have a same fixed length;

wherein the time dimension is a hierarchical dimension in which the first time intervals and the second time intervals have a hierarchical relationship with respect to each other;

wherein the component dimension is a list of values that comprises first values that correspond to a coarser level of component granularity and second values that correspond to a finer level of component granularity, wherein the first values correspond to first component elements that include second component elements corresponding to the second values;

wherein each value of the first values corresponds to a plurality of the second values;

wherein the component dimension is a hierarchical dimension in which the first values and the second values have a hierarchical relationship with respect to each other; and

performing an operation on the data in the storage, wherein the operation is one of a drill down operation that involves transforming the data at the coarser level of granularity to the data at the finer level of granularity, or a rollup operation that involves transforming the data at the finer level of granularity to the data at the coarser level of granularity;

wherein performing the operation on the data in the storage further comprises performing one of a drill down operation that involves transforming the data at the coarser level of component granularity to the data at the finer level of component granularity, or a rollup operation that involves transforming the data at the finer level of component granularity to the data at the coarser level of component granularity.

14. An apparatus as recited in claim 13, wherein the instructions for carrying out the step of sampling operational characteristics of the component to obtain data reflecting performance of the component further comprises instructions for carrying out the step of:

making a measurement dataspace sample at a time t1.

15. An apparatus as recited in claim 13, wherein the instructions for carrying out the step of storing the data into a storage according to an organization including at least a time dimension and a component dimension further comprises instructions for carrying out the step of:

comparing a first measurement dataspace sampled at a time t1 with a second measurement dataspace sampled at a time t2, and if the second measurement dataspace deviates from the first measurement dataspace by a deviation threshold, generating a report.

16. An apparatus as recited in claim 13, wherein the instructions for carrying out the step of performing the operation on the data in the storage further comprises instructions for carrying out the step of:

comparing a first measurement dataspace sampled at a time t1 with a second measurement dataspace sampled at a time t2, and if the second measurement dataspace deviates from the first measurement dataspace by a deviation threshold, generating a report.

17. An apparatus as recited in claim 13, further comprising instructions for carrying out the step of:

dynamically generating a web page comprising a particular view of performance data responsive to a request.

18. An apparatus as recited in claim 13, further comprising instructions for carrying out the step of:

comparing a measurement dataspace sampled at a time of processing a request with a level of service, and if the measurement dataspace deviates from the level of service by a deviation threshold, confirming that a subscription guarantee is not met.

29. An apparatus as recited in claim 13, wherein the component hosts at least one application.

30. An apparatus as recited in claim 13, wherein the operation on the data is the drill down operation.

31. An apparatus as recited in claim 13, wherein the one or more stored sequences of instructions further comprise instructions which, when executed by the one or more processors, cause the one or more processors to carry out the step of performing a pivot operation on the data in the storage.

32. An apparatus as recited in claim 13, wherein the operation on the data is the rollup operation.

19. An apparatus, comprising:

a mechanism for sampling operational characteristics of a component hosting at least one application to obtain data reflecting performance of the component;

a mechanism for storing the data into a storage according to an organization including at least a time dimension and a component dimension;

wherein the time dimension is a list of time intervals that comprises first time intervals that correspond to a coarser level of granularity and second time intervals that correspond to a finer level of granularity, wherein the first time intervals correspond to longer time periods than the second time intervals;

wherein each time interval of the first time intervals corresponds to a plurality of the second time intervals;

wherein all of the second time intervals have a same fixed length;

wherein the time dimension is a hierarchical dimension in which the first time intervals and the second time intervals have a hierarchical relationship with respect to each other;

wherein the component dimension is a list of values that comprises first values that correspond to a coarser level of component granularity and second values that correspond to a finer level of component granularity, wherein the first values correspond to first component elements that include second component elements corresponding to the second values;

wherein each value of the first values corresponds to a plurality of the second values;

wherein the component dimension is a hierarchical dimension in which the first values and the second values have a hierarchical relationship with respect to each other; and

a mechanism for performing an operation on the data in the storage, wherein the operation is one of a drill down operation that involves transforming the data at the coarser level of granularity to the data at the finer level of granularity, or a rollup operation that involves transforming the data at the finer level of granularity to the data at the coarser level of granularity;

wherein the mechanism for performing the operation on the data in the storage further comprises a mechanism for performing one of a drill down operation that involves transforming the data at the coarser level of component granularity to the data at the finer level of component granularity, or a rollup operation that involves transforming the data at the finer level of component granularity to the data at the coarser level of component granularity.

20. A system, comprising:

an application server system that includes a set of application server components that host at least one application;

data collection components operatively coupled to the set of application server components, wherein the data collection components sample operational characteristics of an application server component to obtain data reflecting performance of the component, and wherein the performance data includes at least one measure that reflects performance of the application server component;

data storage components for storing the data according to an organization including at least a time dimension and a component dimension;

wherein the time dimension is a list of time intervals that comprises first time intervals that correspond to a coarser level of granularity and second time intervals that correspond to a finer level of granularity, wherein the first time intervals correspond to longer time periods than the second time intervals;

wherein each time interval of the first time intervals corresponds to a plurality of the second time intervals;

wherein all of the second time intervals have a same fixed length;

wherein the time dimension is a hierarchical dimension in which the first time intervals and the second time intervals have a hierarchical relationship with respect to each other;

wherein the component dimension is a list of values that comprises first values that correspond to a coarser level of component granularity and second values that correspond to a finer level of component granularity, wherein the first values correspond to first component elements that include second component elements corresponding to the second values;

wherein each value of the first values corresponds to a plurality of the second values;

wherein the component dimension is a hierarchical dimension in which the first values and the second values have a hierarchical relationship with respect to each other; and

data access components operatively coupled to the data storage components for retrieving the data and performing an operation on the data in the storage, wherein the operation is one of a drill down operation that involves transforming the data at the coarser level of granularity to the data at the finer level of granularity, or a rollup operation that involves transforming the data at the finer level of granularity to the data at the coarser level of granularity;

wherein performing the operation on the data in the storage further comprises performing one of a drill down operation that involves transforming the data at the coarser level of component granularity to the data at the finer level of component granularity, or a rollup operation that involves transforming the data at the finer level of component granularity to the data at the coarser level of component granularity.