Summary of changes
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
Summary of changes
This section describes the technical changes made in this edition of the book and in previous editions. This edition might also include minor corrections and editorial changes that are not identified.
Summary of Changes
for SG24-8212-01
for Architecting and Deploying DB2 with BLU Acceleration
as created or updated on October 13, 2014.
for SG24-8212-01
for Architecting and Deploying DB2 with BLU Acceleration
as created or updated on October 13, 2014.
October 2014, Second Edition
This revision reflects the addition, deletion, or modification of new and changed information described below.
New information
New features and functions that the BLU Acceleration supported in DB2 for Linux, UNIX, and Windows Cancun Release 10.5.04:
•Shadow tables
•High Availability and Disaster Recovery feature
•SAP support
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