分类：数据库
语言：简体中文
大小：未知
发布时间：12-03-14
目录:
Front CoverPhysical Database DesignCopyright PageContentsPrefaceOrganizationUsage ExamplesLiterature Summaries and BibliographyFeedback and ErrataAcknowledgmentsChapter 1. Introduction to Physical Database Design1.1 Motivation--The Growth of Data and Increasing Relevance of Physical Database Design1.2 Database Life Cycle1.3 Elements of Physical Design: Indexing, Partitioning, and Clustering1.4 Why Physical Design Is Hard1.5 Literature SummaryChapter 2. Basic Indexing Methods2.1 B+tree Index2.2 Composite Index Search2.3 Bitmap Indexing2.4 Record Identifiers2.5 Summary2.6 Literature SummaryChapter 3. Query Optimization and Plan Selection3.1 Query Processing and Optimization3.2 Useful Optimization Features in Database Systems3.3 Query Cost Evaluation--An Example3.4 Query Execution Plan Development3.5 Selectivity Factors, Table Size, and Query Cost Estimation3.6 Summary3.7 Literature SummaryChapter 4. Selecting Indexes4.1 Indexing Concepts and Terminology4.2 Indexing Rules of Thumb4.3 Index Selection Decisions4.4 Join Index Selection4.5 Summary4.6 Literature SummaryChapter 5. Selecting Materialized Views5.1 Simple View Materialization5.2 Exploiting Commonality5.3 Exploiting Grouping and Generalization5.4 Resource Considerations5.5 Examples: The Good, the Bad, and the Ugly5.6 Usage Syntax and Examples5.7 Summary5.8 Literature ReviewChapter 6. Shared-nothing Partitioning6.1 Understanding Shared-nothing Partitioning6.2 More Key Concepts and Terms6.3 Hash Partitioning6.4 Pros and Cons of Shared Nothing6.5 Use in OLTP Systems6.6 Design Challenges: Skew and Join Collocation6.7 Database Design Tips for Reducing Cross-node Data Shipping6.8 Topology Design6.9 Where the Money Goes6.10 Grid Computing6.11 Summary6.12 Literature SummaryChapter 7. Range Partitioning7.1 Range Partitioning Basics7.2 List Partitioning7.3 Syntax Examples7.4 Administration and Fast Roll-in and Roll-out7.5 Increased Addressability7.6 Partition Elimination7.7 Indexing Range Partitioned Data7.8 Range Partitioning and Clustering Indexes7.9 The Full Gestalt: Composite Range and Hash Partitioning with Multidimensional Clustering7.10 Summary7.11 Literature SummaryChapter 8. Multidimensional Clustering8.1 Understanding MDC8.2 Performance Benefits of MDC8.3 Not Just Query Performance: Designing for Roll-in and Roll-out8.4 Examples of Queries Benefiting from MDC8.5 Storage Considerations8.6 Designing MDC Tables8.7 Summary8.8 Literature SummaryChapter 9. The Interdependence Problem9.1 Strong and Weak Dependency Analysis9.2 Pain-first Waterfall Strategy9.3 Impact-.rst Waterfall Strategy9.4 Greedy Algorithm for Change Management9.5 The Popular Strategy (the Chicken Soup Algorithm)9.6 Summary9.7 Literature SummaryChapter 10. Counting and Data Sampling in Physical Design Exploration10.1 Application to Physical Database Design10.2 The Power of Sampling10.3 An Obvious Limitation10.4 Summary10.5 Literature SummaryChapter 11. Query Execution Plans and Physical Design11.1 Getting from Query Text to Result Set11.2 What Do Query Execution Plans Look Like?11.3 Nongraphical Explain11.4 Exploring Query Execution Plans to Improve Database Design11.5 Query Execution Plan Indicators for Improved Physical Database Designs11.6 Exploring without Changing the Database11.7 Forcing the Issue When the Query Optimizer Chooses Wrong11.8 Summary11.9 Literature SummaryChapter 12. Automated Physical Database Design12.1 What-if Analysis, Indexes, and Beyond12.2 Automated Design Features from Oracle, DB2, and SQL Server12.3 Data Sampling for Improved Statistics during Analysis12.4 Scalability and Workload Compression12.5 Design Exploration between Test and Production Systems12.6 Experimental Results from Published Literature12.7 Index Selection12.8 Materialized View Selection12.9 Multidimensional Clustering Selection12.10 Shared-nothing Partitioning12.11 Range Partitioning Design12.12 Summary12.13 Literature SummaryChapter 13. Down to the Metal: Server Resources and Topology13.1 What You Need to Know about CPU Architecture and Trends13.2 Client Server Architectures13.3 Symmetric Multiprocessors and NUMA13.4 Server Clusters13.5 A Little about Operating Systems13.6 Storage Systems13.7 Making Storage Both Reliable and Fast Using RAID13.8 Balancing Resources in a Database Server13.9 Strategies for Availability and Recovery13.10 Main Memory and Database Tuning13.11 Summary13.12 Literature SummaryChapter 14. Physical Design for Decision Support, Warehousing, and OLAP14.1 What Is OLAP?14.2 Dimension Hierarchies14.3 Star and Snowflake Schemas14.4 Warehouses and Marts14.5 Scaling Up the System14.6 DSS, Warehousing, and OLAP Design Considerations14.7 Usage Syntax and Examples for Major Database Servers14.8 Summary14.9 Literature SummaryChapter 15. Denormalization15.1 Basics of Normalization15.2 Common Types of Denormalization15.3 Table Denormalization Strategy15.4 Example of Denormalization15.5 Summary15.6 Literature SummaryChapter 16. Distributed Data Allocation16.1 Introduction16.2 Distributed Database Allocation16.3 Replicated Data Allocation--"All-beneficial Sites" Method16.4 Progressive Table Allocation Method16.5 Summary16.6 Literature SummaryAppendix A. A Simple Performance Model for DatabasesA.1 I/O Time Cost--Individual Block AccessA.2 I/O Time Cost--Table Scans and SortsA.3 Network Time DelaysA.4 CPU Time DelaysAppendix B. Technical Comparison of DB2 HADR with Oracle Data Guard for Database Disaster RecoveryB.1 Standby Remains "Hot" during FailoverB.2 Subminute FailoverB.3 Geographically SeparatedB.4 Support for Multiple Standby ServersB.5 Support for Read on the Standby ServerB.6 Primary Can Be Easily Reintegrated after FailoverGlossaryBibliographyIndexAbout the Authors 内容简介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, 4th 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! 作者简介Sam Lightstone is a Senior Technical Staff Member and Development Manager with IBM's DB2 product development team. His work includes numerous topics in autonomic computing and relational database management systems. He is cofounder and leader of DB2's autonomic computing R&D effort. He is Chair of the IEEE Data Engineering Workgroup on Self Managing Database Systems and a member of the IEEE Computer Society Task Force on Autonomous and Autonomic Computing. In 2003 he was elected to the Canadian Technical Excellence Council, the Canadian affiliate of the IBM Academy of Technology. He is an IBM Master Inventor with over 25 patents and patents pending; he has published widely on autonomic computing for relational database systems. He has been with IBM since 1991.Toby J. Teorey is a professor in the Electrical Engineering and Computer Science Department at the University of Michigan, Ann Arbor. He received his B.S. and M.S. degrees in electrical engineering from the University of Arizona, Tucson, and a Ph.D. in computer sciences from the University of Wisconsin, Madison. He was general chair of the 1981 ACM SIGMOD Conference and program chair for the 1991 Entity-Relationship Conference. Professor Teorey's current research focuses on database design and data warehousing, OLAP, advanced database systems, and performance of computer networks. He is a member of the ACM and the IEEE Computer Society.Tom Nadeau is the founder of Aladdin Software (aladdinsoftware.com) and works in the area of data and text mining. He received his B.S. degree in computer science and M.S. and Ph.D. degrees in electrical engineering and computer science from the University of Michigan, Ann Arbor. His technical interests include data warehousing, OLAP, data mining and machine learning. He won the best paper award at the 2001 IBM CASCON Conference.
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