物理数据库设计 Physical Database Design 影印版[PDF]

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资源信息： 中文名: 物理数据库设计 原名: Physical Database Design 作者: Sam S. Lightstone Toby J. Teorey Tom Nadeau 图书分类: 软件 资源格式: PDF 版本: 影印版 出版社: Morgan Kaufmann 书号: ISBN: 9780123693891 发行时间: 2007年04月04日 地区: 美国 语言: 英文 概述:

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. 内容截图

目录: Front Cover Physical Database Design Copyright Page Contents Preface Organization Usage Examples Literature Summaries and Bibliography Feedback and Errata Acknowledgments Chapter 1. Introduction to Physical Database Design 1.1 Motivation--The Growth of Data and Increasing Relevance of Physical Database Design 1.2 Database Life Cycle 1.3 Elements of Physical Design: Indexing, Partitioning, and Clustering 1.4 Why Physical Design Is Hard 1.5 Literature Summary Chapter 2. Basic Indexing Methods 2.1 B+tree Index 2.2 Composite Index Search 2.3 Bitmap Indexing 2.4 Record Identifiers 2.5 Summary 2.6 Literature Summary Chapter 3. Query Optimization and Plan Selection 3.1 Query Processing and Optimization 3.2 Useful Optimization Features in Database Systems 3.3 Query Cost Evaluation--An Example 3.4 Query Execution Plan Development 3.5 Selectivity Factors, Table Size, and Query Cost Estimation 3.6 Summary 3.7 Literature Summary Chapter 4. Selecting Indexes 4.1 Indexing Concepts and Terminology 4.2 Indexing Rules of Thumb 4.3 Index Selection Decisions 4.4 Join Index Selection 4.5 Summary 4.6 Literature Summary Chapter 5. Selecting Materialized Views 5.1 Simple View Materialization 5.2 Exploiting Commonality 5.3 Exploiting Grouping and Generalization 5.4 Resource Considerations 5.5 Examples: The Good, the Bad, and the Ugly 5.6 Usage Syntax and Examples 5.7 Summary 5.8 Literature Review Chapter 6. Shared-nothing Partitioning 6.1 Understanding Shared-nothing Partitioning 6.2 More Key Concepts and Terms 6.3 Hash Partitioning 6.4 Pros and Cons of Shared Nothing 6.5 Use in OLTP Systems 6.6 Design Challenges: Skew and Join Collocation 6.7 Database Design Tips for Reducing Cross-node Data Shipping 6.8 Topology Design 6.9 Where the Money Goes 6.10 Grid Computing 6.11 Summary 6.12 Literature Summary Chapter 7. Range Partitioning 7.1 Range Partitioning Basics 7.2 List Partitioning 7.3 Syntax Examples 7.4 Administration and Fast Roll-in and Roll-out 7.5 Increased Addressability 7.6 Partition Elimination 7.7 Indexing Range Partitioned Data 7.8 Range Partitioning and Clustering Indexes 7.9 The Full Gestalt: Composite Range and Hash Partitioning with Multidimensional Clustering 7.10 Summary 7.11 Literature Summary Chapter 8. Multidimensional Clustering 8.1 Understanding MDC 8.2 Performance Benefits of MDC 8.3 Not Just Query Performance: Designing for Roll-in and Roll-out 8.4 Examples of Queries Benefiting from MDC 8.5 Storage Considerations 8.6 Designing MDC Tables 8.7 Summary 8.8 Literature Summary Chapter 9. The Interdependence Problem 9.1 Strong and Weak Dependency Analysis 9.2 Pain-first Waterfall Strategy 9.3 Impact-.rst Waterfall Strategy 9.4 Greedy Algorithm for Change Management 9.5 The Popular Strategy (the Chicken Soup Algorithm) 9.6 Summary 9.7 Literature Summary Chapter 10. Counting and Data Sampling in Physical Design Exploration 10.1 Application to Physical Database Design 10.2 The Power of Sampling 10.3 An Obvious Limitation 10.4 Summary 10.5 Literature Summary Chapter 11. Query Execution Plans and Physical Design 11.1 Getting from Query Text to Result Set 11.2 What Do Query Execution Plans Look Like? 11.3 Nongraphical Explain 11.4 Exploring Query Execution Plans to Improve Database Design 11.5 Query Execution Plan Indicators for Improved Physical Database Designs 11.6 Exploring without Changing the Database 11.7 Forcing the Issue When the Query Optimizer Chooses Wrong 11.8 Summary 11.9 Literature Summary Chapter 12. Automated Physical Database Design 12.1 What-if Analysis, Indexes, and Beyond 12.2 Automated Design Features from Oracle, DB2, and SQL Server 12.3 Data Sampling for Improved Statistics during Analysis 12.4 Scalability and Workload Compression 12.5 Design Exploration between Test and Production Systems 12.6 Experimental Results from Published Literature 12.7 Index Selection 12.8 Materialized View Selection 12.9 Multidimensional Clustering Selection 12.10 Shared-nothing Partitioning 12.11 Range Partitioning Design 12.12 Summary 12.13 Literature Summary Chapter 13. Down to the Metal: Server Resources and Topology 13.1 What You Need to Know about CPU Architecture and Trends 13.2 Client Server Architectures 13.3 Symmetric Multiprocessors and NUMA 13.4 Server Clusters 13.5 A Little about Operating Systems 13.6 Storage Systems 13.7 Making Storage Both Reliable and Fast Using RAID 13.8 Balancing Resources in a Database Server 13.9 Strategies for Availability and Recovery 13.10 Main Memory and Database Tuning 13.11 Summary 13.12 Literature Summary Chapter 14. Physical Design for Decision Support, Warehousing, and OLAP 14.1 What Is OLAP? 14.2 Dimension Hierarchies 14.3 Star and Snowflake Schemas 14.4 Warehouses and Marts 14.5 Scaling Up the System 14.6 DSS, Warehousing, and OLAP Design Considerations 14.7 Usage Syntax and Examples for Major Database Servers 14.8 Summary 14.9 Literature Summary Chapter 15. Denormalization 15.1 Basics of Normalization 15.2 Common Types of Denormalization 15.3 Table Denormalization Strategy 15.4 Example of Denormalization 15.5 Summary 15.6 Literature Summary Chapter 16. Distributed Data Allocation 16.1 Introduction 16.2 Distributed Database Allocation 16.3 Replicated Data Allocation--"All-beneficial Sites" Method 16.4 Progressive Table Allocation Method 16.5 Summary 16.6 Literature Summary Appendix A. A Simple Performance Model for Databases A.1 I/O Time Cost--Individual Block Access A.2 I/O Time Cost--Table Scans and Sorts A.3 Network Time Delays A.4 CPU Time Delays Appendix B. Technical Comparison of DB2 HADR with Oracle Data Guard for Database Disaster Recovery B.1 Standby Remains "Hot" during Failover B.2 Subminute Failover B.3 Geographically Separated B.4 Support for Multiple Standby Servers B.5 Support for Read on the Standby Server B.6 Primary Can Be Easily Reintegrated after Failover Glossary Bibliography Index About the Authors