The database is not affected by the HPU process and table spaces do not need to be placed in an offline state. HPU can therefore offer advantages over other recovery methods in data availability and processing speed. Incorporating HPU into your recovery strategy for scenarios involving data loss or corruption can help achieve service level objectives for data availability and data recovery.
Incorporate InfoSphere® Optim™ HPU into your recovery strategy to provide:
- Reduced recovery times through targeted data recovery
Use the WHERE clause when selecting data from backup images to target only the data that was lost, compromised, or corrupted, reducing recovery time. - Increased data availability and reduced resource usage during recovery scenarios
Unload data from a backup image and optionally process this data on a non-production system, maintaining availability of the query workload and reducing the effect on the production system. - Integration with DB2® and Tivoli® Storage Manager (TSM) software
Query TSM to unload data from full, incremental, and delta backup images archived to TSM by the DB2 BACKUP command, reducing errors and facilitating outside recovery.
This article discusses how to incorporate HPU into a recovery strategy and when to use HPU to meet your recovery objectives. In addition the authors contrast the use of output files and named pipes, discuss introducing parallelism, review the HPU command and control file, and recommend best practices for creating control files. Finally, the article details how to install and configure HPU in an IBM Smart Analytics System.
| Description | Name | Size | Download method |
|---|---|---|---|
| Article in PDF format | DB2BP_Warehouse_HPU_Recovery_1111.pdf | 484KB | HTTP |
Information about download methods Get Adobe® Reader®
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Garrett Fitzsimons is a Best Practices Specialist based in the IBM Dublin lab. He has worked with traditional database and data warehouse applications since 1990 on a variety of platforms in several of countries. Garrett has worked on large scale implementations that included Oracle Financials, Oracle Datawarehousing, and Microsoft DW/BI technologies. He is currently working on providing best practice guidelines for customers implementing the IBM Smart Analytics System which features IBM InfoSphere Data Warehouse.
Konrad Emanowicz is a DB2 Data Warehouse QA Specialist. His current work is focused on scalability and performance testing on very large data warehouse environments. Konrad has worked in the Information Management group for 5 years and has experience in database development, database administration, and database reports development.
Richard Lubell is an Information Developer based in the IBM Dublin lab. He has worked in software development since 2000, creating instructional and educational material for financial transaction and content control applications, leadership development training, and business process mapping. He currently works on documentation for IBM Smart Analytics System and IBM database platforms DBaaS on the cloud.