RELATIVE PERFORMANCE PREDICTION OF A REPLACEMENT DATABASE MANAGEMENT SYSTEM (DBMS)

• US 20140136516A1
• Filed: 10/20/2013
• Published: 05/15/2014
• Est. Priority Date: 11/09/2012
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Clifford, Austin, McCallig, Enda
• Granted Patent: US 9,436,734 B2
• Time in Patent Office: Days
• US Class Current: 707/719
• CPC Class Codes: G06F 11/3414 Workload generation, e.g. scripts, playback
• CPC Class Codes: G06F 11/3419 by assessing time
• CPC Class Codes: G06F 16/21 Design, administration or maintenance of databases
• CPC Class Codes: G06F 16/217 Database tuning G06F16/2282 takes precedence; database performance monitoring G06F11/3409
• CPC Class Codes: G06F 16/24545 Selectivity estimation or determination
• CPC Class Codes: G06F 2201/865 Monitoring of software

Abstract

Embodiments of the present invention provide a method, system and computer program product for pre-migration performance prediction of a database management system (DBMS). In an embodiment of the invention, a method for pre-migration performance prediction of a DBMS can include executing a calibration workload in a target DBMS to produce a conversion factor of cost of executing the calibration workload to temporal performance of executing the calibration workload. The method also can include subsequently submitting a sample workload from a database of a source DBMS for evaluation of cost of execution on an empty replica of the database in the target DBMS. Finally, the method can include predicting a temporal performance of the sample workload in the target DBMS as a product of the conversion factor and the cost of execution of the sample workload on the empty replica of the database in the target DBMS.

Claims

1. A method for pre-migration performance prediction of a database management system (DBMS), the method comprising:

executing a calibration workload in a target DBMS to produce a conversion factor of cost of executing the calibration workload to temporal performance of executing the calibration workload;

subsequently submitting a sample workload from a database of a source DBMS for evaluation of cost of execution on an empty replica of the database in the target DBMS; and

,

predicting a temporal performance of the sample workload in the target DBMS as a product of the conversion factor and the cost of execution of the sample workload on the empty replica of the database in the target DBMS.

2. The method of claim 1, wherein the calibration workload is a Transaction Processing Performance Council (TPC)-H benchmark derived workload.

3. The method of claim 1, wherein the sample workload is a selection of database queries captured over a duration of time in the source DBMS.

4. The method of claim 1, wherein the cost of executing the calibration workload is determined from a query execution plan produced by submitting the calibration workload to a query optimizer of the target DBMS, and the cost of executing the sample workload is determined from a query execution plan produced by establishing optimization statistics from a query optimizer of the source DBMS in the query optimizer of the target DBMS and submitting the sample workload to the query optimizer of the target DBMS.

5. The method of claim 1, wherein the empty replica of the database is produced from a set of dynamic data language (DDL) statements extracted from the database of the source DBMS.

6-15. -15. (canceled)