The Easy, Common-Sense Guide to Solving Real Problems with NoSQL
The Mere Mortals® tutorials have earned worldwide praise as the clearest, simplest way to master essential database technologies. Now, there's one for today's exciting new NoSQL databases. NoSQL for Mere Mortals guides you through solving real problems with NoSQL and achieving unprecedented scalability, cost efficiency, flexibility, and availability.
Drawing on 20+ years of cutting-edge database experience, Dan Sullivan explains the advantages, use cases, and terminology associated with all four main categories of NoSQL databases: key-value, document, column family, and graph databases. For each, he introduces pragmatic best practices for building high-value applications. Through step-by-step examples, you'll discover how to choose the right database for each task, and use it the right way.
Coverage includes
--Getting started: What NoSQL databases are, how they differ from relational databases, when to use them, and when not to Data management principles and design criteria: Essential knowledge for creating any database solution, NoSQL or relational
--Key-value databases: Gaining more utility from data structures
--Document databases: Schemaless databases, normalization and denormalization, mutable documents, indexing, and design patterns
--Column family databases: Google's BigTable design, table design, indexing, partitioning, and Big Data
Graph databases: Graph/network modeling, design tips, query methods, and traps to avoid
Whether you're a database developer, data modeler, database user, or student, learning NoSQL can open up immense new opportunities. As thousands of database professionals already know, For Mere Mortals is the fastest, easiest route to mastery.
Autorentext
Dan Sullivan is a data architect and data scientist with more than 20 years of experience in business intelligence, machine learning, data mining, text mining, Big Data, data modeling, and application design. Dan's project work has ranged from analyzing complex genomics and proteomics data to designing and implementing numerous database applications. His most recent work has focused on NoSQL database modeling, data analysis, cloud computing, text mining, and data integration in life sciences. Dan has extensive experience in relational database design and works regularly with NoSQL databases. Dan has presented and written extensively on NoSQL, cloud computing, analytics, data warehousing, and business intelligence. He has worked in many industries, including life sciences, financial services, oil and gas, manufacturing, health care, insurance, retail, power systems, telecommunications, pharmaceuticals, and publishing.
Inhalt
Preface xxi
Introduction xxv
PART I: INTRODUCTION 1
Chapter 1 Different Databases for Different Requirements 3
Relational Database Design 4
E-commerce Application 5
Early Database Management Systems 6
Flat File Data Management Systems 7
Organization of Flat File Data Management Systems 7
Random Access of Data 9
Limitations of Flat File Data Management Systems 9
Hierarchical Data Model Systems 12
Organization of Hierarchical Data Management Systems 12
Limitations of Hierarchical Data Management Systems 14
Network Data Management Systems 14
Organization of Network Data Management Systems 15
Limitations of Network Data Management Systems 17
Summary of Early Database Management Systems 17
The Relational Database Revolution 19
Relational Database Management Systems 19
Organization of Relational Database Management Systems 20
Organization of Applications Using Relational Database Management Systems 26
Limitations of Relational Databases 27
Motivations for Not Just/No SQL (NoSQL) Databases 29
Scalability 29
Cost 31
Flexibility 31
Availability 32
Summary 34
Case Study 35
Review Questions 36
References 37
Bibliography 37
Chapter 2 Variety of NoSQL Databases 39
Data Management with Distributed Databases 41
Store Data Persistently 41
Maintain Data Consistency 42
Ensure Data Availability 44
Consistency of Database Transactions 47
Availability and Consistency in Distributed Databases 48
Balancing Response Times, Consistency, and Durability 49
Consistency, Availability, and Partitioning: The CAP Theorem 51
ACID and BASE 54
ACID: Atomicity, Consistency, Isolation, and Durability 54
BASE: Basically Available, Soft State, Eventually Consistent 56
Types of Eventual Consistency 57
Casual Consistency 57
Read-Your-Writes Consistency 57
Session Consistency 58
Monotonic Read Consistency 58
Monotonic Write Consistency 58
Four Types of NoSQL Databases 59
Key-Value Pair Databases 60
Keys 60
Values 64
Differences Between Key-Value and Relational Databases 65
Document Databases 66
Documents 66
Querying Documents 67
Differences Between Document and Relational Databases 68
Column Family Databases 69
Columns and Column Families 69
Differences Between Column Family and Relational Databases 70
Graph Databases 71
Nodes and Relationships 72
Differences Between Graph and Relational Databases 73
Summary 75
Review Questions 76
References 77
Bibliography 77
PART II: KEY-VALUE DATABASES 79
Chapter 3 Introduction to Key-Value Databases 81
From Arrays to Key-Value Databases 82
Arrays: Key Value Stores with Training Wheels 82
Associative Arrays: Taking Off the Training Wheels 84
Caches: Adding Gears to the Bike 85
In-Memory and On-Disk Key-Value Database: From Bikes to Motorized Vehicles 89
Essential Features of Key-Value Databases 91
Simplicity: Who Needs Complicated Data Models Anyway? 91
Speed: There Is No Such Thing as Too Fast 93
Scalability: Keeping Up with the Rush 95
Scaling with Master-Slave Replication 95
Scaling with Masterless Replication 98
Keys: More Than Meaningless Identifiers 103
How to Construct a Key 103
Using Keys to Locate Values 105
Hash Functions: From Keys to Locations 106
Keys Help Avoid Write Problems 107
Values: Storing Just About Any Data You Want 110
Values Do Not Require Strong Typing 110
Limitations on Searching for Values 112
Summary 114
Review Questions 115
References 116
Bibliography 116
Chapter 4 Key-Value Database Terminology 117
Key-Value Database Data Modeling Terms 118
Key 121
Value 123
Namespace 124
Partition 126
Partition Key 129
Schemaless 129
Key-Value Architecture Terms 131
Cluster 131
Ring 133
Replication 135
Key-Value Implementation Terms 137
Hash Function 137
Collision 138
Compression 139
Summary 141
Review Questions 141
References 142
Chapter 5 Designing for Key-Value Databases 143
Key Design and Partitioning 144
Keys Should Follow a Naming Convention 145
Well-Designed Keys Save Code 145
Dealing with Ranges of Values 147
Keys Must Take into Account Implementation Limitations 149
How Keys Are Used in Partitioning 150
Designing Structured Values 151
Structu…