《物理数据库设计》(Physical Database Design)影印版[PDF]

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    出版社Morgan Kaufmann
  • 时间: 2012/03/14 19:55:48 发布 | 2012/03/14 20:30:49 更新
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原名Physical Database Design
出版社Morgan Kaufmann
书号ISBN: 9780123693891

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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!


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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Front Cover
Physical Database Design
Copyright Page
Usage Examples
Literature Summaries and Bibliography
Feedback and Errata
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
About the Authors






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