Physical Database Design

1 Introduction to Physical Database Design 2 Basic Indexing Methods 3 Query Optimization and Plan Selection 4 Selecting Indexes 5 Selecting Materialized Views 6 Shared-nothing Partitioning 7 Range Partitioning 8 Multidimensional Clustering 9 The Interdependence Problem 10 Counting and Data Sampling in Physical Design Exploration 11 Query Execution Plans and Physical Design 12 Automated Physical Database Design 13 Down to the Metal: Server Resources and Topology 14 Physical Design for Decision Support, Warehousing, and OLAP 15 Denormalization 16 Distributed Data Allocation Appendix A A Simple Performance Model for Databases Appendix B Technical Comparison of DB2 HADR with Oracle Data Guard for Database Disaster Recovery

The rapidly increasing volume of information contained in relational databases places a strain on databases, performance, and maintainability: DBAs are under greater pressure than ever to optimize database structure for system performance and administration.

Physical Database Design discusses the concept of how physical structures of databases affect performance, including specific examples, guidelines, and best and worst practices for a variety of DBMSs and configurations. Something as simple as improving the table index design has a profound impact on performance. Every form of relational database, such as Online Transaction Processing (OLTP), Enterprise Resource Management (ERP), Data Mining (DM), or Management Resource Planning (MRP), can be improved using the methods provided in the book.

• The first complete treatment on physical database design, written by the authors of the seminal, Database Modeling and Design: Logical Design, Fourth Edition
• Includes an introduction to the major concepts of physical database design as well as detailed examples, using methodologies and tools most popular for relational databases today: Oracle, DB2 (IBM), and SQL Server (Microsoft)
• Focuses on physical database design for exploiting B+tree indexing, clustered indexes, multidimensional clustering (MDC), range partitioning, shared nothing partitioning, shared disk data placement, materialized views, bitmap indexes, automated design tools, and more!

Database designers and database administrators working with large databases and data warehouses, who want to improve processing time for transactions and information access, resolve storage issues, and make life better for all of us.