Principles of Distributed Database Systems 4th Edition by M. Tamer Özsu · Patrick Valduriez | eBook-PDF | Contents
1 Introduction . 1
1.1 What Is a Distributed Database System? 1
1.2 History of Distributed DBMS . 3
...
Principles of Distributed Database Systems 4th Edition by M. Tamer Özsu · Patrick Valduriez | eBook-PDF | Contents
1 Introduction . 1
1.1 What Is a Distributed Database System? 1
1.2 History of Distributed DBMS . 3
1.3 Data Delivery Alternatives . 5
1.4 Promises of Distributed DBMSs 7
1.4.1 Transparent Management of Distributed and
Replicated Data
1.4.2 Reliability Through Distributed Transactions 10
1.4.3 Improved Performance 11
1.4.4 Scalability . 13
1.5 Design Issues . 13
1.5.1 Distributed Database Design . 13
1.5.2 Distributed Data Control 14
1.5.3 Distributed Query Processing. 14
1.5.4 Distributed Concurrency Control . 14
1.5.5 Reliability of Distributed DBMS . 15
1.5.6 Replication. 15
1.5.7 Parallel DBMSs . 16
1.5.8 Database Integration 16
1.5.9 Alternative Distribution Approaches . 16
1.5.10 Big Data Processing and NoSQL. 16
1.6 Distributed DBMS Architectures 17
1.6.1 Architectural Models for Distributed DBMSs . 17
1.6.2 Client/Server Systems. 20
1.6.3 Peer-to-Peer Systems 22
1.6.4 Multidatabase Systems 25
1.6.5 Cloud Computing . 27
1.7 Bibliographic Notes 31
xi
xii Contents
2 Distributed and Parallel Database Design . 33
2.1 Data Fragmentation 35
2.1.1 Horizontal Fragmentation. 37
2.1.2 Vertical Fragmentation 52
2.1.3 Hybrid Fragmentation. 65
2.2 Allocation. 66
2.2.1 Auxiliary Information . 68
2.2.2 Allocation Model 69
2.2.3 Solution Methods . 72
2.3 Combined Approaches . 72
2.3.1 Workload-Agnostic Partitioning Techniques 73
2.3.2 Workload-Aware Partitioning Techniques . 74
2.4 Adaptive Approaches 78
2.4.1 Detecting Workload Changes . 79
2.4.2 Detecting Affected Items . 79
2.4.3 Incremental Reconfiguration 80
2.5 Data Directory 82
2.6 Conclusion 83
2.7 Bibliographic Notes 84
3 Distributed Data Control 91
3.1 View Management . 92
3.1.1 Views in Centralized DBMSs. 92
3.1.2 Views in Distributed DBMSs . 95
3.1.3 Maintenance of Materialized Views 96
3.2 Access Control . 102
3.2.1 Discretionary Access Control. 103
3.2.2 Mandatory Access Control . 106
3.2.3 Distributed Access Control . 108
3.3 Semantic Integrity Control . 110
3.3.1 Centralized Semantic Integrity Control 111
3.3.2 Distributed Semantic Integrity Control. 116
3.4 Conclusion 123
3.5 Bibliographic Notes 123
4 Distributed Query Processing 129
4.1 Overview 130
4.1.1 Query Processing Problem 130
4.1.2 Query Optimization . 133
4.1.3 Layers Of Query Processing 136
4.2 Data Localization. 140
4.2.1 Reduction for Primary Horizontal Fragmentation . 141
4.2.2 Reduction with Join . 142
4.2.3 Reduction for Vertical Fragmentation 143
4.2.4 Reduction for Derived Fragmentation 145
4.2.5 Reduction for Hybrid Fragmentation. 148
Contents xiii
4.3 Join Ordering in Distributed Queries 149
4.3.1 Join Trees 149
4.3.2 Join Ordering 151
4.3.3 Semijoin-Based Algorithms 153
4.3.4 Join Versus Semijoin 156
4.4 Distributed Cost Model 157
4.4.1 Cost Functions. 157
4.4.2 Database Statistics 159
4.5 Distributed Query Optimization . 161
4.5.1 Dynamic Approach 161
4.5.2 Static Approach . 165
4.5.3 Hybrid Approach 169
4.6 Adaptive Query Processing 173
4.6.1 Adaptive Query Processing Process 174
4.6.2 Eddy Approach 176
4.7 Conclusion 177
4.8 Bibliographic Notes 178
5 Distributed Transaction Processing 183
5.1 Background and Terminology . 184
5.2 Distributed Concurrency Control 188
5.2.1 Locking-Based Algorithms . 189
5.2.2 Timestamp-Based Algorithms 197
5.2.3 Multiversion Concurrency Control . 203
5.2.4 Optimistic Algorithms 205
5.3 Distributed Concurrency Control Using Snapshot Isolation . 206
5.4 Distributed DBMS Reliability . 209
5.4.1 Two-Phase Commit Protocol . 211
5.4.2 Variations of 2PC 217
5.4.3 Dealing with Site Failures 220
5.4.4 Network Partitioning 227
5.4.5 Paxos Consensus Protocol 231
5.4.6 Architectural Considerations . 234
5.5 Modern Approaches to Scaling Out Transaction Management 236
5.5.1 Spanner 237
5.5.2 LeanXcale . 237
5.6 Conclusion 239
5.7 Bibliographic Notes 241
6 Data Replication . 247
6.1 Consistency of Replicated Databases . 249
6.1.1 Mutual Consistency . 249
6.1.2 Mutual Consistency Versus Transaction Consistency . 251
6.2 Update Management Strategies 252
6.2.1 Eager Update Propagation 253
6.2.2 Lazy Update Propagation . 254
xiv Contents
6.2.3 Centralized Techniques . 254
6.2.4 Distributed Techniques 255
6.3 Replication Protocols 255
6.3.1 Eager Centralized Protocols 256
6.3.2 Eager Distributed Protocols. 262
6.3.3 Lazy Centralized Protocols . 262
6.3.4 Lazy Distributed Protocols . 268
6.4 Group Communication . 269
6.5 Replication and Failures . 272
6.5.1 Failures and Lazy Replication 273
6.5.2 Failures and Eager Replication . 273
6.6 Conclusion 276
6.7 Bibliographic Notes 277
7 Database Integration—Multidatabase Systems . 281
7.1 Database Integration . 282
7.1.1 Bottom-Up Design Methodology 283
7.1.2 Schema Matching . 287
7.1.3 Schema Integration 296
7.1.4 Schema Mapping 298
7.1.5 Data Cleaning . 306
7.2 Multidatabase Query Processing 307
7.2.1 Issues in Multidatabase Query Processing . 308
7.2.2 Multidatabase Query Processing Architecture . 309
7.2.3 Query Rewriting Using Views 311
7.2.4 Query Optimization and Execution 317
7.2.5 Query Translation and Execution. 329
7.3 Conclusion 332
7.4 Bibliographic Notes 334
8 Parallel Database Systems . 349
8.1 Objectives. 350
8.2 Parallel Architectures 352
8.2.1 General Architecture 353
8.2.2 Shared-Memory . 355
8.2.3 Shared-Disk . 357
8.2.4 Shared-Nothing 358
8.3 Data Placement . 359
8.4 Parallel Query Processing 362
8.4.1 Parallel Algorithms for Data Processing . 362
8.4.2 Parallel Query Optimization 369
8.5 Load Balancing . 374
8.5.1 Parallel Execution Problems 374
8.5.2 Intraoperator Load Balancing. 376
8.5.3 Interoperator Load Balancing. 378
8.5.4 Intraquery Load Balancing . 378
Contents xv
8.6 Fault-Tolerance . 383
8.7 Database Clusters 384
8.7.1 Database Cluster Architecture 385
8.7.2 Replication. 386
8.7.3 Load Balancing 386
8.7.4 Query Processing 387
8.8 Conclusion 390
8.9 Bibliographic Notes 390
9 Peer-to-Peer Data Management. 395
9.1 Infrastructure . 398
9.1.1 Unstructured P2P Networks 399
9.1.2 Structured P2P Networks . 402
9.1.3 Superpeer P2P Networks . 406
9.1.4 Comparison of P2P Networks 408
9.2 Schema Mapping in P2P Systems . 408
9.2.1 Pairwise Schema Mapping 408
9.2.2 Mapping Based on Machine Learning Techniques 409
9.2.3 Common Agreement Mapping . 410
9.2.4 Schema Mapping Using IR Techniques 411
9.3 Querying Over P2P Systems. 411
9.3.1 Top-k Queries . 412
9.3.2 Join Queries . 424
9.3.3 Range Queries . 425
9.4 Replica Consistency 428
9.4.1 Basic Support in DHTs . 429
9.4.2 Data Currency in DHTs . 431
9.4.3 Replica Reconciliation 432
9.5 Blockchain 436
9.5.1 Blockchain Definition . 437
9.5.2 Blockchain Infrastructure . 438
9.5.3 Blockchain 2.0. 442
9.5.4 Issues. 443
9.6 Conclusion 444
9.7 Bibliographic Notes 445
10 Big Data Processing 449
10.1 Distributed Storage Systems . 451
10.1.1 Google File System . 453
10.1.2 Combining Object Storage and File Storage . 454
10.2 Big Data Processing Frameworks . 455
10.2.1 MapReduce Data Processing . 456
10.2.2 Data Processing Using Spark . 466
10.3 Stream Data Management . 470
10.3.1 Stream Models, Languages, and Operators 472
10.3.2 Query Processing over Data Streams. 476
10.3.3 DSS Fault-Tolerance 483
xvi Contents
10.4 Graph Analytics Platforms. 486
10.4.1 Graph Partitioning. 489
10.4.2 MapReduce and Graph Analytics 494
10.4.3 Special-Purpose Graph Analytics Systems 495
10.4.4 Vertex-Centric Block Synchronous. 498
10.4.5 Vertex-Centric Asynchronous 501
10.4.6 Vertex-Centric Gather-Apply-Scatter 503
10.4.7 Partition-Centric Block Synchronous Processing 504
10.4.8 Partition-Centric Asynchronous 506
10.4.9 Partition-Centric Gather-Apply-Scatter 506
10.4.10 Edge-Centric Block Synchronous Processing . 507
10.4.11 Edge-Centric Asynchronous 507
10.4.12 Edge-Centric Gather-Apply-Scatter 507
10.5 Data Lakes 508
10.5.1 Data Lake Versus Data Warehouse . 508
10.5.2 Architecture . 510
10.5.3 Challenges . 511
10.6 Conclusion 512
10.7 Bibliographic Notes 512
11 NoSQL, NewSQL, and Polystores 519
11.1 Motivations for NoSQL 520
11.2 Key-Value Stores . 521
11.2.1 DynamoDB 522
11.2.2 Other Key-Value Stores . 524
11.3 Document Stores . 525
11.3.1 MongoDB . 525
11.3.2 Other Document Stores . 528
11.4 Wide Column Stores . 529
11.4.1 Bigtable 529
11.4.2 Other Wide Column Stores . 531
11.5 Graph DBMSs 531
11.5.1 Neo4j. 532
11.5.2 Other Graph Databases . 535
11.6 Hybrid Data Stores . 535
11.6.1 Multimodel NoSQL Stores . 536
11.6.2 NewSQL DBMSs . 537
11.7 Polystores . 540
11.7.1 Loosely Coupled Polystores 540
11.7.2 Tightly Coupled Polystores . 544
11.7.3 Hybrid Systems . 549
11.7.4 Concluding Remarks 553
11.8 Conclusion 554
11.9 Bibliographic Notes 555
Contents xvii
12 Web Data Management 559
12.1 Web Graph Management. 560
12.2 Web Search . 562
12.2.1 Web Crawling . 563
12.2.2 Indexing . 566
12.2.3 Ranking and Link Analysis . 567
12.2.4 Evaluation of Keyword Search . 568
12.3 Web Querying 569
12.3.1 Semistructured Data Approach . 570
12.3.2 Web Query Language Approach . 574
12.4 Question Answering Systems 580
12.5 Searching and Querying the Hidden Web . 584
12.5.1 Crawling the Hidden Web 585
12.5.2 Metasearching . 586
12.6 Web Data Integration. 588
12.6.1 Web Tables/Fusion Tables 589
12.6.2 Semantic Web and Linked Open Data . 590
12.6.3 Data Quality Issues in Web Data Integration 608
12.7 Bibliographic Notes 615
A Overview of Relational DBMS 619
B Centralized Query Processing. 621
C Transaction Processing Fundamentals . 623
D Review of Computer Networks 625
References. 627
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