Physical database design: the database professional's guide to exploiting indexes, views, storage, and more
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Amsterdam [u.a.]
Elsevier [u.a.]
2007
|
| Series: | The Morgan Kaufmann series in data management systems
|
| Subjects: | |
| Online Access: | Table of contents only Publisher description Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references (p. 391-409) and indexes |
| Physical Description: | XX, 425 S. Ill. |
| ISBN: | 0123693896 9780123693891 |
Staff View
MARC
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| 245 | 1 | 0 | |a Physical database design |b the database professional's guide to exploiting indexes, views, storage, and more |c Sam Lightstone ; Toby Teorey ; Tom Nadeau |
| 264 | 1 | |a Amsterdam [u.a.] |b Elsevier [u.a.] |c 2007 | |
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| 490 | 0 | |a The Morgan Kaufmann series in data management systems | |
| 500 | |a Includes bibliographical references (p. 391-409) and indexes | ||
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Record in the Search Index
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| adam_text | Contents
Preface xv
Organization xvi
Usage Examples xvii
Literature Summaries and Bibliography xviii
Feedback and Errata xviii
Acknowledgments xix
1 Introduction to Physical Database Design I
I. I Motivation—The Growth of Data and Increasing
Relevance of Physical Database Design 2
1.2 Database Life Cycle 5
1.3 Elements of Physical Design: Indexing,
Partitioning, and Clustering 7
1.3.1 Indexes 8
1.3.2 Materialized Views 9
1.3.3 Partitioning and Multidimensional Clustering 10
1.3.4 Other Methods for Physical Database Design 10
1.4 Why Physical Design Is Hard 11
1.5 Literature Summary 12
2 Basic Indexing Methods 15
2.1 B+tree Index 16
2.2 Composite Index Search 20
v
vi Contents
2.2.1 Composite Index Approach 24
2.2.2 Table Scan 24
2.3 Bitmap Indexing 25
2.4 Record Identifiers 27
2.5 Summary 28
2.6 Literature Summary 28
3 Query Optimization and Plan Selection 31
3.1 Query Processing and Optimization 32
3.2 Useful Optimization Features in Database Systems 32
3.2.1 Query Transformation or Rewrite 32
3.2.2 Query Execution Plan Viewing 33
3.2.3 Histograms 33
3.2.4 Query Execution Plan Hints 33
3.2.5 Optimization Depth 34
3.3 Query Cost Evaluation—An Example 34
3.3.1 Example Query 3.1 34
3.4 Query Execution Plan Development 41
3.4.1 Transformation Rules for Query Execution Plans 42
3.4.2 Query Execution Plan Restructuring Algorithm 42
3.5 Selectivity Factors.Table Size, and Query Cost Estimation 43
3.5.1 Estimating Selectivity Factor for a Selection
Operation or Predicate 43
3.5.2 Histograms 45
3.5.3 Estimating the Selectivity Factor for a Join 46
3.5.4 Example Query 3.2 46
3.5.5 Example Estimations of Query Execution Plan
Table Sizes 49
3.6 Summary 50
3.7 Literature Summary 51
4 Selecting Indexes 53
4.1 Indexing Concepts and Terminology 53
4.1.1 Basic Types of Indexes 54
4.1.2 Access Methods for Indexes 55
4.2 Indexing Rules ofThumb 55
4.3 Index Selection Decisions 58
4.4 Join Index Selection 62
4.4.1 Nested loop Join 62
4.4.2 Block Nested loop Join 65
4.4.3 Indexed Nested loop Join 65
Contents vii
4.4.4 Sort merge Join 66
4.4.5 Hash Join 67
4.5 Summary 69
4.6 Literature Summary 70
5 Selecting Materialized Views 71
5.1 Simple View Materialization 72
5.2 Exploiting Commonality 77
5.3 Exploiting Grouping and Generalization 84
5.4 Resource Considerations 86
5.5 Examples:The Good, the Bad, and the Ugly 89
5.6 Usage Syntax and Examples 92
5.7 Summary 95
5.8 Literature Review 96
6 Shared nothing Partitioning 97
6.1 Understanding Shared nothing Partitioning 98
6.1.1 Shared nothing Architecture 98
6.1.2 Why Shared Nothing Scales So Well 100
6.2 More Key Concepts and Terms 101
6.3 Hash Partitioning 101
6.4 Pros and Cons of Shared Nothing 103
6.5 Use in OLTP Systems 106
6.6 Design Challenges: Skew and Join Collocation 108
6.6.1 Data Skew 108
6.6.2 Collocation 109
6.7 Database Design Tips for Reducing Cross node
Data Shipping 110
6.7.1 Careful Partitioning 110
6.7.2 Materialized View Replication and Other
Duplication Techniques 111
6.7.3 The Internode Interconnect 115
6.8 Topology Design 117
6.8.1 Using Subsets of Nodes 117
6.8.2 Logical Nodes versus Physical Nodes 119
6.9 Where the Money Goes 120
6.10 Grid Computing 120
6.11 Summary 121
6.12 Literature Summary 122
viii Contents
7 Range Partitioning 125
7.1 Range Partitioning Basics 126
7.2 List Partitioning 128
7.2.1 Essentials of List Partitioning 128
7.2.2 Composite Range and List Partitioning 128
7.3 Syntax Examples 129
7.4 Administration and Fast Roll in and Roll out 131
7.4.1 Utility Isolation 131
7.4.2 Roll in and Roll out 133
7.5 Increased Addressability 134
7.6 Partition Elimination 135
7.7 Indexing Range Partitioned Data 138
7.8 Range Partitioning and Clustering Indexes 139
7.9 The Full Gestalt: Composite Range and Hash
Partitioning with Multidimensional Clustering 139
7.10 Summary 142
7.11 Literature Summary 142
8 Multidimensional Clustering 143
8.1 Understanding MDC 144
8.1.1 Why Clustering Helps So Much 144
8.1.2 MDC 145
8.1.3 Syntax for Creating MDC Tables 151
8.2 Performance Benefits of MDC 151
8.3 Not Just Query Performance: Designing
for Roll in and Roll out 152
8.4 Examples of Queries Benefiting from MDC 153
8.5 Storage Considerations 157
8.6 Designing MDC Tables 159
8.6.1 Constraining the Storage Expansion
Using Coarsification 159
8.6.2 Monotonicity for MDC Exploitation 162
8.6.3 Picking the Right Dimensions 163
8.7 Summary 165
8.8 Literature Summary 166
9 The Interdependence Problem 167
9.1 Strong and Weak Dependency Analysis 168
9.2 Pain first Waterfall Strategy 170
9.3 Impact first Waterfall Strategy 171
9.4 Greedy Algorithm for Change Management 172
Contents ix
9.5 The Popular Strategy (the Chicken Soup Algorithm) 173
9.6 Summary 175
9.7 Literature Summary 175
10 Counting and Data Sampling in Physical
Design Exploration 177
10.1 Application to Physical Database Design 178
10.1.1 Counting for Index Design 180
10.1.2 Counting for Materialized View Design 180
10.1.3 Counting for Multidimensional
Clustering Design 182
10.1.4 Counting for Shared nothing
Partitioning Design 183
10.2 The Power of Sampling 184
10.2.1 The Benefits of Sampling with SQL 184
10.2.2 Sampling for Database Design 185
10.2.3 Types of Sampling 189
10.2.4 Repeatability with Sampling 192
10.3 An Obvious Limitation 192
10.4 Summary 194
10.5 Literature Summary 195
11 Query Execution Plans and Physical Design 197
I I. I Getting from Query Text to Result Set 198
I 1.2 What Do Query Execution Plans Look Like? 201
11.3 Nongraphical Explain 201
I 1.4 Exploring Query Execution Plans to Improve
Database Design 205
I 1.5 Query Execution Plan Indicators for Improved
Physical Database Designs 211
11.6 Exploring without Changing the Database 214
I 1.7 Forcing the Issue When the Query Optimizer
Chooses Wrong 215
I 1.7.1 Three Essential Strategies 215
11.7.2 Introduction to Query Hints 216
11.7.3 Query Hints When the SQL Is Not Available
to Modify 219
I 1.8 Summary 220
I 1.9 Literature Summary 220
x Contents
12 Automated Physical Database Design 223
12.1 What if Analysis, Indexes, and Beyond 225
12.2 Automated Design Features from Oracle,
DB2, and SQL Server 229
12.2.1 IBM DB2 Design Advisor 231
12.2.2 Microsoft SQL Server Database
Tuning Advisor 234
12.2.3 Oracle SQL Access Advisor 238
12.3 Data Sampling for Improved Statistics during Analysis 240
12.4 Scalability and Workload Compression 242
12.5 Design Exploration between Test
and Production Systems 247
12.6 Experimental Results from Published Literature 248
12.7 Index Selection 254
12.8 Materialized View Selection 254
12.9 Multidimensional Clustering Selection 256
12.10 Shared nothing Partitioning 258
12.1 I Range Partitioning Design 260
12.12 Summary 262
12.13 Literature Summary 262
13 Down to the Metal: Server Resources and Topology 265
13.1 What You Need to Know about CPU Architecture
and Trends 266
13.1.1 CPU Performance 266
13.1.2 Amdahl s Law for System Speedup with
Parallel Processing 269
13.1.3 Multicore CPUs 271
13.2 Client Server Architectures 271
13.3 Symmetric Multiprocessors and NUMA 273
13.3.1 Symmetric Multiprocessors and NUMA 273
13.3.2 Cache Coherence and False Sharing 274
13.4 Server Clusters 275
13.5 A Little about Operating Systems 275
13.6 Storage Systems 276
13.6.1 Disks, Spindles, and Striping 277
13.6.2 Storage Area Networks and Network
Attached Storage 278
13.7 Making Storage Both Reliable and Fast Using RAID 279
13.7.1 History of RAID 279
13.7.2 RAIDO 281
Contents xi
13.7.3 RAID I 281
13.7.4 RAID 2 and RAID 3 282
13.7.5 RAID 4 284
13.7.6 RAID 5 and RAID 6 284
13.7.7 RAID 1+0 285
13.7.8 RAID 0+1 285
13.7.9 RAID 10+0 and RAID 5+0 286
13.7.10 Which RAID Is Right for Your
Database Requirements? 288
13.8 Balancing Resources in a Database Server 288
13.9 Strategies for Availability and Recovery 290
13.10 Main Memory and Database Tuning 295
13.10.1 Memory Tuning by Mere Mortals 295
13.10.2 Automated Memory Tuning 298
13.10.3 Cutting Edge:The Latest Strategy
in Self tuning Memory Management 301
13.1 I Summary 314
13.12 Literature Summary 314
14 Physical Design for Decision Support, Warehousing,
and OLAP 317
14.1 What Is OLAP? 318
14.2 Dimension Hierarchies 320
14.3 Star and Snowflake Schemas 321
14.4 Warehouses and Marts 323
14.5 Scaling Up the System 327
14.6 DSS,Warehousing, and OLAP Design Considerations 328
14.7 Usage Syntax and Examples for Major Database Servers 329
14.7.1 Oracle 330
14.7.2 Microsoft s Analysis Services 331
14.8 Summary 333
14.9 Literature Summary 334
15 Denormalization 337
15.1 Basics of Normalization 338
15.2 Common Types of Denormalization 342
15.2.1 Two Entities in a One to One Relationship 342
15.2.2 Two Entities in a One to many Relationship 343
15.3 Table Denormalization Strategy 346
15.4 Example of Denormalization 347
15.4.1 Requirements Specification 347
xii Contents
15.4.2 Logical Design 349
15.4.3 Schema Refinement Using Denormalizaton 350
15.5 Summary 354
15.6 Literature Summary 354
16 Distributed Data Allocation 357
16.1 Introduction 358
16.2 Distributed Database Allocation 360
16.3 Replicated Data Allocation— All beneficial Sites
Method 362
16.3.1 Example 362
16.4 Progressive Table Allocation Method 367
16.5 Summary 368
16.6 Literature Summary 369
Appendix A A Simple Performance Model for Databases 371
A.I I/O Time Cost—Individual Block Access 371
A.2 I/O Time Cost—Table Scans and Sorts 372
A.3 Network Time Delays 372
A.4 CPU Time Delays 374
Appendix B Technical Comparison of DB2 HADR
with Oracle Data Guard for Database
Disaster Recovery 375
B. I Standby Remains Hot during Failover 376
B.2 Subminute Failover 377
B.3 Geographically Separated 377
B.4 Support for Multiple Standby Servers 377
B.5 Support for Read on the Standby Server 377
B.6 Primary Can Be Easily Reintegrated after Failover 378
Glossary 379
Bibliography 391
Index 411
About the Authors 427
|
| adam_txt |
Contents
Preface xv
Organization xvi
Usage Examples xvii
Literature Summaries and Bibliography xviii
Feedback and Errata xviii
Acknowledgments xix
1 Introduction to Physical Database Design I
I. I Motivation—The Growth of Data and Increasing
Relevance of Physical Database Design 2
1.2 Database Life Cycle 5
1.3 Elements of Physical Design: Indexing,
Partitioning, and Clustering 7
1.3.1 Indexes 8
1.3.2 Materialized Views 9
1.3.3 Partitioning and Multidimensional Clustering 10
1.3.4 Other Methods for Physical Database Design 10
1.4 Why Physical Design Is Hard 11
1.5 Literature Summary 12
2 Basic Indexing Methods 15
2.1 B+tree Index 16
2.2 Composite Index Search 20
v
vi Contents
2.2.1 Composite Index Approach 24
2.2.2 Table Scan 24
2.3 Bitmap Indexing 25
2.4 Record Identifiers 27
2.5 Summary 28
2.6 Literature Summary 28
3 Query Optimization and Plan Selection 31
3.1 Query Processing and Optimization 32
3.2 Useful Optimization Features in Database Systems 32
3.2.1 Query Transformation or Rewrite 32
3.2.2 Query Execution Plan Viewing 33
3.2.3 Histograms 33
3.2.4 Query Execution Plan Hints 33
3.2.5 Optimization Depth 34
3.3 Query Cost Evaluation—An Example 34
3.3.1 Example Query 3.1 34
3.4 Query Execution Plan Development 41
3.4.1 Transformation Rules for Query Execution Plans 42
3.4.2 Query Execution Plan Restructuring Algorithm 42
3.5 Selectivity Factors.Table Size, and Query Cost Estimation 43
3.5.1 Estimating Selectivity Factor for a Selection
Operation or Predicate 43
3.5.2 Histograms 45
3.5.3 Estimating the Selectivity Factor for a Join 46
3.5.4 Example Query 3.2 46
3.5.5 Example Estimations of Query Execution Plan
Table Sizes 49
3.6 Summary 50
3.7 Literature Summary 51
4 Selecting Indexes 53
4.1 Indexing Concepts and Terminology 53
4.1.1 Basic Types of Indexes 54
4.1.2 Access Methods for Indexes 55
4.2 Indexing Rules ofThumb 55
4.3 Index Selection Decisions 58
4.4 Join Index Selection 62
4.4.1 Nested loop Join 62
4.4.2 Block Nested loop Join 65
4.4.3 Indexed Nested loop Join 65
Contents vii
4.4.4 Sort merge Join 66
4.4.5 Hash Join 67
4.5 Summary 69
4.6 Literature Summary 70
5 Selecting Materialized Views 71
5.1 Simple View Materialization 72
5.2 Exploiting Commonality 77
5.3 Exploiting Grouping and Generalization 84
5.4 Resource Considerations 86
5.5 Examples:The Good, the Bad, and the Ugly 89
5.6 Usage Syntax and Examples 92
5.7 Summary 95
5.8 Literature Review 96
6 Shared nothing Partitioning 97
6.1 Understanding Shared nothing Partitioning 98
6.1.1 Shared nothing Architecture 98
6.1.2 Why Shared Nothing Scales So Well 100
6.2 More Key Concepts and Terms 101
6.3 Hash Partitioning 101
6.4 Pros and Cons of Shared Nothing 103
6.5 Use in OLTP Systems 106
6.6 Design Challenges: Skew and Join Collocation 108
6.6.1 Data Skew 108
6.6.2 Collocation 109
6.7 Database Design Tips for Reducing Cross node
Data Shipping 110
6.7.1 Careful Partitioning 110
6.7.2 Materialized View Replication and Other
Duplication Techniques 111
6.7.3 The Internode Interconnect 115
6.8 Topology Design 117
6.8.1 Using Subsets of Nodes 117
6.8.2 Logical Nodes versus Physical Nodes 119
6.9 Where the Money Goes 120
6.10 Grid Computing 120
6.11 Summary 121
6.12 Literature Summary 122
viii Contents
7 Range Partitioning 125
7.1 Range Partitioning Basics 126
7.2 List Partitioning 128
7.2.1 Essentials of List Partitioning 128
7.2.2 Composite Range and List Partitioning 128
7.3 Syntax Examples 129
7.4 Administration and Fast Roll in and Roll out 131
7.4.1 Utility Isolation 131
7.4.2 Roll in and Roll out 133
7.5 Increased Addressability 134
7.6 Partition Elimination 135
7.7 Indexing Range Partitioned Data 138
7.8 Range Partitioning and Clustering Indexes 139
7.9 The Full Gestalt: Composite Range and Hash
Partitioning with Multidimensional Clustering 139
7.10 Summary 142
7.11 Literature Summary 142
8 Multidimensional Clustering 143
8.1 Understanding MDC 144
8.1.1 Why Clustering Helps So Much 144
8.1.2 MDC 145
8.1.3 Syntax for Creating MDC Tables 151
8.2 Performance Benefits of MDC 151
8.3 Not Just Query Performance: Designing
for Roll in and Roll out 152
8.4 Examples of Queries Benefiting from MDC 153
8.5 Storage Considerations 157
8.6 Designing MDC Tables 159
8.6.1 Constraining the Storage Expansion
Using Coarsification 159
8.6.2 Monotonicity for MDC Exploitation 162
8.6.3 Picking the Right Dimensions 163
8.7 Summary 165
8.8 Literature Summary 166
9 The Interdependence Problem 167
9.1 Strong and Weak Dependency Analysis 168
9.2 Pain first Waterfall Strategy 170
9.3 Impact first Waterfall Strategy 171
9.4 Greedy Algorithm for Change Management 172
Contents ix
9.5 The Popular Strategy (the Chicken Soup Algorithm) 173
9.6 Summary 175
9.7 Literature Summary 175
10 Counting and Data Sampling in Physical
Design Exploration 177
10.1 Application to Physical Database Design 178
10.1.1 Counting for Index Design 180
10.1.2 Counting for Materialized View Design 180
10.1.3 Counting for Multidimensional
Clustering Design 182
10.1.4 Counting for Shared nothing
Partitioning Design 183
10.2 The Power of Sampling 184
10.2.1 The Benefits of Sampling with SQL 184
10.2.2 Sampling for Database Design 185
10.2.3 Types of Sampling 189
10.2.4 Repeatability with Sampling 192
10.3 An Obvious Limitation 192
10.4 Summary 194
10.5 Literature Summary 195
11 Query Execution Plans and Physical Design 197
I I. I Getting from Query Text to Result Set 198
I 1.2 What Do Query Execution Plans Look Like? 201
11.3 Nongraphical Explain 201
I 1.4 Exploring Query Execution Plans to Improve
Database Design 205
I 1.5 Query Execution Plan Indicators for Improved
Physical Database Designs 211
11.6 Exploring without Changing the Database 214
I 1.7 Forcing the Issue When the Query Optimizer
Chooses Wrong 215
I 1.7.1 Three Essential Strategies 215
11.7.2 Introduction to Query Hints 216
11.7.3 Query Hints When the SQL Is Not Available
to Modify 219
I 1.8 Summary 220
I 1.9 Literature Summary 220
x Contents
12 Automated Physical Database Design 223
12.1 What if Analysis, Indexes, and Beyond 225
12.2 Automated Design Features from Oracle,
DB2, and SQL Server 229
12.2.1 IBM DB2 Design Advisor 231
12.2.2 Microsoft SQL Server Database
Tuning Advisor 234
12.2.3 Oracle SQL Access Advisor 238
12.3 Data Sampling for Improved Statistics during Analysis 240
12.4 Scalability and Workload Compression 242
12.5 Design Exploration between Test
and Production Systems 247
12.6 Experimental Results from Published Literature 248
12.7 Index Selection 254
12.8 Materialized View Selection 254
12.9 Multidimensional Clustering Selection 256
12.10 Shared nothing Partitioning 258
12.1 I Range Partitioning Design 260
12.12 Summary 262
12.13 Literature Summary 262
13 Down to the Metal: Server Resources and Topology 265
13.1 What You Need to Know about CPU Architecture
and Trends 266
13.1.1 CPU Performance 266
13.1.2 Amdahl's Law for System Speedup with
Parallel Processing 269
13.1.3 Multicore CPUs 271
13.2 Client Server Architectures 271
13.3 Symmetric Multiprocessors and NUMA 273
13.3.1 Symmetric Multiprocessors and NUMA 273
13.3.2 Cache Coherence and False Sharing 274
13.4 Server Clusters 275
13.5 A Little about Operating Systems 275
13.6 Storage Systems 276
13.6.1 Disks, Spindles, and Striping 277
13.6.2 Storage Area Networks and Network
Attached Storage 278
13.7 Making Storage Both Reliable and Fast Using RAID 279
13.7.1 History of RAID 279
13.7.2 RAIDO 281
Contents xi
13.7.3 RAID I 281
13.7.4 RAID 2 and RAID 3 282
13.7.5 RAID 4 284
13.7.6 RAID 5 and RAID 6 284
13.7.7 RAID 1+0 285
13.7.8 RAID 0+1 285
13.7.9 RAID 10+0 and RAID 5+0 286
13.7.10 Which RAID Is Right for Your
Database Requirements? 288
13.8 Balancing Resources in a Database Server 288
13.9 Strategies for Availability and Recovery 290
13.10 Main Memory and Database Tuning 295
13.10.1 Memory Tuning by Mere Mortals 295
13.10.2 Automated Memory Tuning 298
13.10.3 Cutting Edge:The Latest Strategy
in Self tuning Memory Management 301
13.1 I Summary 314
13.12 Literature Summary 314
14 Physical Design for Decision Support, Warehousing,
and OLAP 317
14.1 What Is OLAP? 318
14.2 Dimension Hierarchies 320
14.3 Star and Snowflake Schemas 321
14.4 Warehouses and Marts 323
14.5 Scaling Up the System 327
14.6 DSS,Warehousing, and OLAP Design Considerations 328
14.7 Usage Syntax and Examples for Major Database Servers 329
14.7.1 Oracle 330
14.7.2 Microsoft's Analysis Services 331
14.8 Summary 333
14.9 Literature Summary 334
15 Denormalization 337
15.1 Basics of Normalization 338
15.2 Common Types of Denormalization 342
15.2.1 Two Entities in a One to One Relationship 342
15.2.2 Two Entities in a One to many Relationship 343
15.3 Table Denormalization Strategy 346
15.4 Example of Denormalization 347
15.4.1 Requirements Specification 347
xii Contents
15.4.2 Logical Design 349
15.4.3 Schema Refinement Using Denormalizaton 350
15.5 Summary 354
15.6 Literature Summary 354
16 Distributed Data Allocation 357
16.1 Introduction 358
16.2 Distributed Database Allocation 360
16.3 Replicated Data Allocation—"All beneficial Sites"
Method 362
16.3.1 Example 362
16.4 Progressive Table Allocation Method 367
16.5 Summary 368
16.6 Literature Summary 369
Appendix A A Simple Performance Model for Databases 371
A.I I/O Time Cost—Individual Block Access 371
A.2 I/O Time Cost—Table Scans and Sorts 372
A.3 Network Time Delays 372
A.4 CPU Time Delays 374
Appendix B Technical Comparison of DB2 HADR
with Oracle Data Guard for Database
Disaster Recovery 375
B. I Standby Remains "Hot" during Failover 376
B.2 Subminute Failover 377
B.3 Geographically Separated 377
B.4 Support for Multiple Standby Servers 377
B.5 Support for Read on the Standby Server 377
B.6 Primary Can Be Easily Reintegrated after Failover 378
Glossary 379
Bibliography 391
Index 411
About the Authors 427 |
| any_adam_object | 1 |
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| author | Lightstone, Sam Teorey, Toby Nadeau, Tom |
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| discipline_str_mv | Informatik |
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| id | DE-604.BV022550136 |
| illustrated | Illustrated |
| index_date | 2024-07-02T18:13:15Z |
| indexdate | 2024-07-09T21:00:03Z |
| institution | BVB |
| isbn | 0123693896 9780123693891 |
| language | English |
| lccn | 2006102899 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-015756464 |
| oclc_num | 77572979 |
| open_access_boolean | |
| owner | DE-29T DE-703 DE-523 DE-634 DE-188 DE-898 DE-BY-UBR DE-739 |
| owner_facet | DE-29T DE-703 DE-523 DE-634 DE-188 DE-898 DE-BY-UBR DE-739 |
| physical | XX, 425 S. Ill. |
| publishDate | 2007 |
| publishDateSearch | 2007 |
| publishDateSort | 2007 |
| publisher | Elsevier [u.a.] |
| record_format | marc |
| series2 | The Morgan Kaufmann series in data management systems |
| spelling | Lightstone, Sam Verfasser aut Physical database design the database professional's guide to exploiting indexes, views, storage, and more Sam Lightstone ; Toby Teorey ; Tom Nadeau Amsterdam [u.a.] Elsevier [u.a.] 2007 XX, 425 S. Ill. txt rdacontent n rdamedia nc rdacarrier The Morgan Kaufmann series in data management systems Includes bibliographical references (p. 391-409) and indexes Database design Datenbankentwurf (DE-588)4127613-9 gnd rswk-swf Relationale Datenbank (DE-588)4049358-1 gnd rswk-swf Datenbankentwurf (DE-588)4127613-9 s DE-604 Relationale Datenbank (DE-588)4049358-1 s DE-188 Teorey, Toby Verfasser (DE-588)1175901482 aut Nadeau, Tom Verfasser aut http://www.loc.gov/catdir/toc/ecip077/2006102899.html Table of contents only http://www.loc.gov/catdir/enhancements/fy0704/2006102899-d.html Publisher description HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015756464&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Lightstone, Sam Teorey, Toby Nadeau, Tom Physical database design the database professional's guide to exploiting indexes, views, storage, and more Database design Datenbankentwurf (DE-588)4127613-9 gnd Relationale Datenbank (DE-588)4049358-1 gnd |
| subject_GND | (DE-588)4127613-9 (DE-588)4049358-1 |
| title | Physical database design the database professional's guide to exploiting indexes, views, storage, and more |
| title_auth | Physical database design the database professional's guide to exploiting indexes, views, storage, and more |
| title_exact_search | Physical database design the database professional's guide to exploiting indexes, views, storage, and more |
| title_exact_search_txtP | Physical database design the database professional's guide to exploiting indexes, views, storage, and more |
| title_full | Physical database design the database professional's guide to exploiting indexes, views, storage, and more Sam Lightstone ; Toby Teorey ; Tom Nadeau |
| title_fullStr | Physical database design the database professional's guide to exploiting indexes, views, storage, and more Sam Lightstone ; Toby Teorey ; Tom Nadeau |
| title_full_unstemmed | Physical database design the database professional's guide to exploiting indexes, views, storage, and more Sam Lightstone ; Toby Teorey ; Tom Nadeau |
| title_short | Physical database design |
| title_sort | physical database design the database professional s guide to exploiting indexes views storage and more |
| title_sub | the database professional's guide to exploiting indexes, views, storage, and more |
| topic | Database design Datenbankentwurf (DE-588)4127613-9 gnd Relationale Datenbank (DE-588)4049358-1 gnd |
| topic_facet | Database design Datenbankentwurf Relationale Datenbank |
| url | http://www.loc.gov/catdir/toc/ecip077/2006102899.html http://www.loc.gov/catdir/enhancements/fy0704/2006102899-d.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015756464&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT lightstonesam physicaldatabasedesignthedatabaseprofessionalsguidetoexploitingindexesviewsstorageandmore AT teoreytoby physicaldatabasedesignthedatabaseprofessionalsguidetoexploitingindexesviewsstorageandmore AT nadeautom physicaldatabasedesignthedatabaseprofessionalsguidetoexploitingindexesviewsstorageandmore |