Chapter 8: Optimization and performance tuning
by Christopher Yao, Carlos Ruiz, Steven Rogers, Chris McPherson, Skyla Loomis, Beth
IBM Cognos Dynamic Cubes
- Notices
- Preface
- Chapter 1: Overview of Cognos Dynamic Cubes
- Chapter 2: IBM Cognos Dynamic Cubes architecture
- Chapter 3: Installation and configuration of Cognos Cube Designer and Dynamic Query Analyzer
- Chapter 4: Modeling dynamic cubes
- Introduction to IBM Cognos Cube Designer
- Working with Cognos Cube Designer
- Modeling dimensions
- Calculated members
- Modeling time dimensions for relative time
- Slowly changing dimensions
- Role-playing dimensions
- Multi-grain fact scenarios
- Aggregate cubes
- Validation
- Deploying dynamic cubes for reporting and analysis
- Troubleshooting
- Creating a sample cube model by using GOSLDW
- Creating a time dimension
- Modeling for multi-grain scenarios
- Chapter 5: Administering dynamic cubes
- Chapter 6: Virtual cubes
- Chapter 7: Dimensional security
- Overview of dimensional security
- Security model
- Grant versus deny approaches to security
- Visible ancestors
- Secured padding members
- Default members
- Calculated members
- Merging of security views or security filters
- Defining security in Cognos Cube Designer
- Publish and start the cube
- Applying security views
- Verifying the security model
- Chapter 8: Optimization and performance tuning
- Performance implications of OLAP-style reports
- Aggregate awareness in Cognos Dynamic Cubes
- Overview of the Aggregate Advisor
- Model-based analysis
- Workload-based analysis
- Running the Aggregate Advisor without considering workload information (1/2)
- Running the Aggregate Advisor without considering workload information (2/2)
- Running the Aggregate Advisor with considering workload information
- Opening Aggregate Advisor results and viewing details (1/2)
- Opening Aggregate Advisor results and viewing details (2/2)
- Rerunning the Aggregate Advisor
- In-memory aggregates
- Database aggregates
- Cache Priming Techniques
- Cache size best practices
- Scenarios for performance tuning
- Chapter 9: Dynamic cube lifecycle
- Overview of the dynamic cube lifecycle
- Lifecycle across different environments
- Best practices for deploying between environments
- Keep a copy of the dynamic cube project file under source control
- Use consistent data source, schema, and table names in all environments
- Skip the optimize step in the development environment
- Use a separate database for each environment
- Include complete dimensional data in development environment
- Make the test system similar to the production system
- Chapter 10: Troubleshooting
- Related publications
- Back cover
© Copyright IBM Corp. 2012. All rights reserved. 201
Chapter 8. Optimization and performance
tuning
This chapter describes the use of the Aggregate Advisor to generate aggregate
recommendations to optimize query performance, and the techniques and scenarios for
performance tuning.
This chapter contains the following sections:
? 8.1, “Performance implications of OLAP-style reports ” on page 202
? 8.2, “Aggregate awareness in Cognos Dynamic Cubes” on page 202
? 8.3, “Overview of the Aggregate Advisor” on page 203
? 8.4, “In-memory aggregates” on page 221
? 8.5, “Database aggregates” on page 231
? 8.6, “Cache Priming Techniques” on page 240
? 8.7, “Cache size best practices” on page 242
? 8.8, “Scenarios for performance tuning” on page 242
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