Architecting and Deploying DB2 with BLU Acceleration
by Alexander Zietlow, Ayesha Zaka, Martin Schlegel, Jean Cristie Pacanaro, Polly La
Architecting and Deploying DB2 with BLU Acceleration
- Front cover
- Notices
- Preface
- Summary of changes
- Chapter 1. Introducing DB2 BLU Acceleration
- 1.1 DB2 with BLU Acceleration
- 1.2 BLU Acceleration: Seven Big Ideas
- 1.2.1 Big Idea 1: Simplicity and ease of use
- 1.2.2 Big Idea 2: Column store
- 1.2.3 Big Idea 3: Adaptive compression
- 1.2.4 Big Idea 4: Parallel vector processing
- 1.2.5 Big Idea 5: Core-friendly parallelism
- 1.2.6 Big Idea 6: Scan-friendly memory caching
- 1.2.7 Big Idea 7: Data skipping
- 1.2.8 The seven big ideas in action
- 1.3 Next generation analytics: Cognos BI and DB2 with BLU Acceleration
- 1.4 IBM DB2 with BLU Acceleration offerings
- 1.5 Obtaining DB2 with BLU Acceleration
- Chapter 2. Planning and deployment of BLU Acceleration
- 2.1 BLU Acceleration deployment made easy
- 2.2 Data environments targeted for analytic workloads
- 2.3 Data environments with mixed workloads
- 2.4 Prerequisites
- 2.5 Deployment
- 2.6 Configuration preferred practices for BLU Acceleration deployment
- 2.7 Configuration preferred practices for HADR
- 2.7.1 Column-organized tables now support high availability and disaster recovery
- 2.7.2 Column-organized tables and HADR synchronization modes
- 2.7.3 Configuring primary and standby databases
- 2.7.4 Automatic Client Reroute and HADR
- 2.7.5 Switching the roles in your HADR environment
- 2.7.6 Checking HADR status
- 2.7.7 Considerations for fix pack upgrades in an HADR environment
- 2.7.8 HADR best practices
- Chapter 3. Planning and deployment of BLU Acceleration shadow tables for mixed workload environments
- 3.1 Overview
- 3.2 Prerequisites
- 3.3 Deployment
- 3.4 Configuration preferred practices
- 3.4.1 DB2 and InfoSphere CDC sizing guidelines
- 3.4.2 DB2 configuration parameters
- 3.4.3 InfoSphere CDC instance configuration parameters
- 3.4.4 Startup and shutdown procedures
- 3.4.5 Buffer pool and storage considerations
- 3.4.6 Enabling latency based routing
- 3.4.7 Optimizing compression dictionaries
- 3.4.8 Latency and throughput
- 3.5 Operational preferred practices
- 3.5.1 Backup and recovery
- 3.5.2 Restoring table space backups for shadow tables
- 3.5.3 Moving InfoSphere CDC metadata tables to a specific table space
- 3.5.4 Reorganizing shadow tables
- 3.5.5 Table statistics
- 3.5.6 Monitoring replication
- 3.5.7 Troubleshooting shadow tables
- 3.5.8 Restoring replication when restricted DDL is applied to a table
- 3.5.9 Preventing disruption to replication with restrictive DDL applied to the source table
- 3.6 Shadow tables and HADR
- 3.6.1 Shadow tables configuration
- 3.6.2 HADR considerations
- 3.6.3 InfoSphere CDC considerations
- 3.6.4 Standby server considerations
- 3.6.5 Installation and configuration
- 3.6.6 Shadow tables configuration and metadata
- 3.6.7 Considerations for an HADR role switch
- 3.6.8 Considerations for InfoSphere CDC Access Server after a failover
- Chapter 4. Optim Query Workload Tuner and BLU Acceleration
- 4.1 Planning and testing BLU Acceleration with IBM InfoSphere Optim Query Workload
- 4.2 How the Workload Table Organization Advisor works
- 4.3 Prerequisites
- 4.4 Preparing an empty DB2 10.5 database with current objects and statistics using db2look
- 4.5 Step 1: Capturing existing workloads for analysis
- 4.6 Step 2: Managing a list of captured workloads
- 4.7 Step 3: Running the Workload Table Organization Advisor
- 4.8 Step 4: Reviewing the table organization summary
- 4.9 Running the conversion recommendations from the advisor
- 4.10 Optional: Selecting your own candidate tables for conversion analysis
- Chapter 5. Performance test with a Cognos BI example
- Chapter 6. Post-deployment of DB2 with BLU Acceleration
- Chapter 7. Oracle compatibility for BLU Acceleration
- Chapter 8. DB2 with BLU Acceleration and SAP integration
- 8.1 Introduction to SAP Business Warehouse (BW)
- 8.2 Prerequisites and restrictions for using BLU Acceleration in SAP BW
- 8.3 BLU Acceleration support in the ABAP Dictionary
- 8.4 BLU Acceleration support in the DBA Cockpit
- 8.4.1 Checking whether individual tables in SAP database are column-organized
- 8.4.2 Checking if SAP database contains column-organized tables
- 8.4.3 Monitoring columnar data processing time in the SAP database
- 8.4.4 Monitoring columnar processing-related prefetcher and buffer pool activity in the SAP database
- 8.5 BLU Acceleration support in SAP BW
- 8.6 Conversion of SAP BW objects to column-organized tables
- 8.7 Deployment
- 8.8 Performance of SAP BW with BLU Acceleration
- 8.9 BLU Acceleration for SAP near-line storage solution on DB2 (NLS)
- Appendix A. BLU Acceleration monitor elements
- Related publications
- Back cover
- IBM System x Reference Architecture for Hadoop: IBM InfoSphere BigInsights Reference Architecture
- Introduction
- Business problem and business value
- Reference architecture use
- Requirements
- InfoSphere BigInsights predefined configuration
- InfoSphere BigInsights HBase predefined configuration
- Deployment considerations
- Customizing the predefined configurations
- Predefined configuration bill of materials
- References
- The team who wrote this paper
- Now you can become a published author, too!
- Stay connected to IBM Redbooks
- Notices
Architecting and Deploying DB2 with BLU Acceleration
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