NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence / Edition 1

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The need to handle increasingly larger data volumes is one factor driving the adoption of a new class of nonrelational “NoSQL” databases. Advocates of NoSQL databases claim they can be used to build systems that are more performant, scale better, and are easier to program.

NoSQL Distilled is a concise but thorough introduction to this rapidly emerging technology. Pramod J. Sadalage and Martin Fowler explain how NoSQL databases work and the ways that they may be a superior alternative to a traditional RDBMS. The authors provide a fast-paced guide to the concepts you need to know in order to evaluate whether NoSQL databases are right for your needs and, if so, which technologies you should explore further.

The first part of the book concentrates on core concepts, including schemaless data models, aggregates, new distribution models, the CAP theorem, and map-reduce. In the second part, the authors explore architectural and design issues associated with implementing NoSQL. They also present realistic use cases that demonstrate NoSQL databases at work and feature representative examples using Riak, MongoDB, Cassandra, and Neo4j.

In addition, by drawing on Pramod Sadalage’s pioneering work, NoSQL Distilled shows how to implement evolutionary design with schema migration: an essential technique for applying NoSQL databases. The book concludes by describing how NoSQL is ushering in a new age of Polyglot Persistence, where multiple data-storage worlds coexist, and architects can choose the technology best optimized for each type of data access.

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Product Details

  • ISBN-13: 9780321826626
  • Publisher: Addison-Wesley
  • Publication date: 8/22/2012
  • Edition number: 1
  • Pages: 192
  • Sales rank: 360,480
  • Product dimensions: 7.08 (w) x 9.00 (h) x 0.40 (d)

Meet the Author

Pramod J. Sadalage, Principal Consultant at ThoughtWorks, enjoys the rare role of bridging the divide between database professionals and application developers. He regularly consults with clients who have particularly challenging data needs requiring new technologies and techniques. He developed pioneering techniques that allowed relational databases to be designed in an evolutionary manner based on version-controlled schema migrations. With Scott Ambler, he coauthored Refactoring Databases(Addison-Wesley, 2006).

Martin Fowler, Chief Scientist at ThoughtWorks, focuses on better ways to design software systems and improve developer productivity. His books include Patterns of Enterprise Application Architecture; UML Distilled, Third Edition; Domain-Specific Languages (with Rebecca Parsons); and Refactoring: Improving the Design of Existing Code (with Kent Beck, John Brant, and William Opdyke). All are published by Addison-Wesley.

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Table of Contents

Preface xiii

Part I: Understand 1

Chapter 1: Why NoSQL? 3

1.1 The Value of Relational Databases 3

1.2 Impedance Mismatch 5

1.3 Application and Integration Databases 6

1.4 Attack of the Clusters 8

1.5 The Emergence of NoSQL 9

1.6 Key Points 12

Chapter 2: Aggregate Data Models 13

2.1 Aggregates 14

2.2 Key-Value and Document Data Models 20

2.3 Column-Family Stores 21

2.4 Summarizing Aggregate-Oriented Databases 23

2.5 Further Reading 24

2.6 Key Points 24

Chapter 3: More Details on Data Models 25

3.1 Relationships 25

3.2 Graph Databases 26

3.3 Schemaless Databases 28

3.4 Materialized Views 30

3.5 Modeling for Data Access 31

3.6 Key Points 36

Chapter 4: Distribution Models 37

4.1 Single Server 37

4.2 Sharding 38

4.3 Master-Slave Replication 40

4.4 Peer-to-Peer Replication 42

4.5 Combining Sharding and Replication 43

4.6 Key Points 44

Chapter 5: Consistency 47

5.1 Update Consistency 47

5.2 Read Consistency 49

5.3 Relaxing Consistency 52

5.4 Relaxing Durability 56

5.5 Quorums 57

5.6 Further Reading 59

5.7 Key Points 59

Chapter 6: Version Stamps 61

6.1 Business and System Transactions 61

6.2 Version Stamps on Multiple Nodes 63

6.3 Key Points 65

Chapter 7: Map-Reduce 67

7.1 Basic Map-Reduce 68

7.2 Partitioning and Combining 69

7.3 Composing Map-Reduce Calculations 72

7.4 Further Reading 77

7.5 Key Points 77

Part II: Implement 79

Chapter 8: Key-Value Databases 81

8.1 What Is a Key-Value Store 81

8.2 Key-Value Store Features 83

8.3 Suitable Use Cases 87

8.4 When Not to Use 87

Chapter 9: Document Databases 89

9.1 What Is a Document Database? 90

9.2 Features 91

9.3 Suitable Use Cases 97

9.4 When Not to Use 98

Chapter 10: Column-Family Stores 99

10.1 What Is a Column-Family Data Store? 99

10.2 Features 100

10.3 Suitable Use Cases 107

10.4 When Not to Use 109

Chapter 11: Graph Databases 111

11.1 What Is a Graph Database? 111

11.2 Features 113

11.3 Suitable Use Cases 120

11.4 When Not to Use 121

Chapter 12: Schema Migrations 123

12.1 Schema Changes 123

12.2 Schema Changes in RDBMS 123

12.3 Schema Changes in a NoSQL Data Store 128

12.4 Further Reading 132

12.5 Key Points 132

Chapter 13: Polyglot Persistence 133

13.1 Disparate Data Storage Needs 133

13.2 Polyglot Data Store Usage 134

13.3 Service Usage over Direct Data Store Usage 136

13.4 Expanding for Better Functionality 136

13.5 Choosing the Right Technology 138

13.6 Enterprise Concerns with Polyglot Persistence 138

13.7 Deployment Complexity 139

13.8 Key Points 140

Chapter 14: Beyond NoSQL 141

14.1 File Systems 141

14.2 Event Sourcing 142

14.3 Memory Image 144

14.4 Version Control 145

14.5 XML Databases 145

14.6 Object Databases 146

14.7 Key Points 146

Chapter 15: Choosing Your Database 147

15.1 Programmer Productivity 147

15.2 Data-Access Performance 149

15.3 Sticking with the Default 150

15.4 Hedging Your Bets 150

15.5 Key Points 151

15.6 Final Thoughts 152

Bibliography 153

Index 157

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