| Preface |
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xxi | |
| Introduction |
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xxv | |
| Part I: Introduction |
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1 | (78) |
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Chapter 1 Different Databases for Different Requirements |
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3 | (36) |
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Relational Database Design |
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4 | (2) |
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5 | (1) |
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Early Database Management Systems |
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6 | (13) |
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Flat File Data Management Systems |
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7 | (5) |
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Organization of Flat File Data Management Systems |
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7 | (2) |
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9 | (1) |
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Limitations of Flat File Data Management Systems |
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9 | (3) |
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Hierarchical Data Model Systems |
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12 | (2) |
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Organization of Hierarchical Data Management Systems |
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12 | (2) |
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Limitations of Hierarchical Data Management Systems |
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14 | (1) |
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Network Data Management Systems |
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14 | (3) |
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Organization of Network Data Management Systems |
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15 | (2) |
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Limitations of Network Data Management Systems |
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17 | (1) |
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Summary of Early Database Management Systems |
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17 | (2) |
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The Relational Database Revolution |
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19 | (10) |
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Relational Database Management Systems |
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19 | (10) |
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Organization of Relational Database Management Systems |
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20 | (6) |
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Organization of Applications Using Relational Database Management Systems |
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26 | (1) |
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Limitations of Relational Databases |
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27 | (2) |
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Motivations for Not Just/No SQL (NoSQL) Databases |
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29 | (5) |
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29 | (2) |
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31 | (1) |
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31 | (1) |
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32 | (2) |
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34 | (1) |
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35 | (1) |
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36 | (1) |
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37 | (1) |
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37 | (2) |
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Chapter 2 Variety of NoSQL Databases |
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39 | (40) |
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Data Management with Distributed Databases |
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41 | (13) |
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41 | (1) |
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Maintain Data Consistency |
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42 | (2) |
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44 | (5) |
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Consistency of Database Transactions |
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47 | (1) |
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Availability and Consistency in Distributed Databases |
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48 | (1) |
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Balancing Response Times, Consistency, and Durability |
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49 | (2) |
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Consistency, Availability, and Partitioning: The CAP Theorem |
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51 | (3) |
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54 | (5) |
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ACID: Atomicity, Consistency, Isolation, and Durability |
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54 | (2) |
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BASE: Basically Available, Soft State, Eventually Consistent |
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56 | (1) |
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Types of Eventual Consistency |
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57 | (2) |
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57 | (1) |
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Read-Your-Writes Consistency |
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57 | (1) |
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58 | (1) |
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Monotonic Read Consistency |
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58 | (1) |
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Monotonic Write Consistency |
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58 | (1) |
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Four Types of NoSQL Databases |
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59 | (16) |
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60 | (6) |
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60 | (4) |
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64 | (1) |
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Differences Between Key-Value and Relational Databases |
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65 | (1) |
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66 | (3) |
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66 | (1) |
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67 | (1) |
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Differences Between Document and Relational Databases |
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68 | (1) |
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69 | (2) |
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Columns and Column Families |
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69 | (1) |
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Differences Between Column Family and Relational Databases |
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70 | (1) |
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71 | (11) |
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72 | (1) |
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Differences Between Graph and Relational Databases |
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73 | (2) |
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75 | (1) |
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76 | (1) |
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77 | (1) |
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77 | (2) |
| Part II: Key-Value Databases |
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79 | (100) |
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Chapter 3 Introduction to Key-Value Databases |
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81 | (36) |
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From Arrays to Key-Value Databases |
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82 | (9) |
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Arrays: Key Value Stores with Training Wheels |
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82 | (2) |
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Associative Arrays: Taking Off the Training Wheels |
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84 | (1) |
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Caches: Adding Gears to the Bike |
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85 | (4) |
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In-Memory and On-Disk Key-Value Database: From Bikes to Motorized Vehicles |
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89 | (2) |
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Essential Features of Key-Value Databases |
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91 | (12) |
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Simplicity: Who Needs Complicated Data Models Anyway? |
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91 | (2) |
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Speed: There Is No Such Thing as Too Fast |
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93 | (2) |
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Scalability: Keeping Up with the Rush |
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95 | (8) |
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Scaling with Master-Slave Replication |
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95 | (3) |
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Scaling with Masterless Replication |
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98 | (5) |
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Keys: More Than Meaningless Identifiers |
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103 | (7) |
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103 | (2) |
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Using Keys to Locate Values |
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105 | (5) |
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Hash Functions: From Keys to Locations |
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106 | (1) |
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Keys Help Avoid Write Problems |
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107 | (3) |
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Values: Storing Just About Any Data You Want |
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110 | (4) |
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Values Do Not Require Strong Typing |
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110 | (2) |
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Limitations on Searching for Values |
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112 | (2) |
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114 | (1) |
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115 | (1) |
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116 | (1) |
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116 | (1) |
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Chapter 4 Key-Value Database Terminology |
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117 | (26) |
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Key-Value Database Data Modeling Terms |
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118 | (13) |
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121 | (2) |
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123 | (1) |
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124 | (2) |
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126 | (3) |
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129 | (1) |
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129 | (2) |
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Key-Value Architecture Terms |
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131 | (6) |
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131 | (2) |
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133 | (2) |
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135 | (2) |
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Key-Value Implementation Terms |
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137 | (4) |
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137 | (1) |
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138 | (1) |
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139 | (2) |
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141 | (1) |
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141 | (1) |
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142 | (1) |
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Chapter 5 Designing for Key-Value Databases |
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143 | (36) |
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Key Design and Partitioning |
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144 | (7) |
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Keys Should Follow a Naming Convention |
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145 | (1) |
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Well-Designed Keys Save Code |
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145 | (2) |
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Dealing with Ranges of Values |
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147 | (2) |
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Keys Must Take into Account Implementation Limitations |
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149 | (1) |
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How Keys Are Used in Partitioning |
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150 | (1) |
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Designing Structured Values |
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151 | (8) |
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Structured Data Types Help Reduce Latency |
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152 | (3) |
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Large Values Can Lead to Inefficient Read and Write Operations |
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155 | (4) |
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Limitations of Key-Value Databases |
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159 | (3) |
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Look Up Values by Key Only |
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160 | (1) |
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Key-Value Databases Do Not Support Range Queries |
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161 | (1) |
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No Standard Query Language Comparable to SQL for Relational Databases |
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161 | (1) |
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Design Patterns for Key-Value Databases |
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162 | (11) |
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163 | (2) |
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165 | (1) |
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166 | (3) |
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169 | (1) |
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170 | (1) |
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171 | (2) |
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173 | (1) |
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Case Study: Key-Value Databases for Mobile Application Configuration |
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174 | (3) |
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177 | (1) |
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178 | (1) |
| Part IA: Document Databases |
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179 | (96) |
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Chapter 6 Introduction to Document Databases |
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181 | (32) |
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182 | (17) |
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Documents Are Not So Simple After All |
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182 | (5) |
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Documents and Key-Value Pairs |
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187 | (1) |
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Managing Multiple Documents in Collections |
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188 | (13) |
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Getting Started with Collections |
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188 | (3) |
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Tips on Designing Collections |
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191 | (8) |
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Avoid Explicit Schema Definitions |
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199 | (2) |
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Basic Operations on Document Databases |
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201 | (9) |
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Inserting Documents into a Collection |
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202 | (2) |
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Deleting Documents from a Collection |
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204 | (2) |
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Updating Documents in a Collection |
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206 | (2) |
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Retrieving Documents from a Collection |
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208 | (2) |
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210 | (1) |
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210 | (1) |
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211 | (2) |
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Chapter 7 Document Database Terminology |
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213 | (26) |
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Document and Collection Terms |
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214 | (10) |
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215 | (2) |
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Documents: Ordered Sets of Key-Value Pairs |
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215 | (1) |
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216 | (1) |
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217 | (1) |
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218 | (2) |
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220 | (3) |
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Schemaless Means More Flexibility |
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221 | (1) |
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Schemaless Means More Responsibility |
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222 | (1) |
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223 | (1) |
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224 | (8) |
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225 | (2) |
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Horizontal Partitioning or Sharding |
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227 | (5) |
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Separating Data with Shard Keys |
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229 | (1) |
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Distributing Data with a Partitioning Algorithm |
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230 | (2) |
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Data Modeling and Query Processing |
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232 | (5) |
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233 | (2) |
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235 | (1) |
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235 | (2) |
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237 | (1) |
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237 | (1) |
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238 | (1) |
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Chapter 8 Designing for Document Databases |
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239 | (36) |
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Normalization, Denormalization, and the Search for Proper Balance |
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241 | (14) |
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242 | (1) |
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243 | (1) |
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243 | (2) |
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Executing Joins: The Heavy Lifting of Relational Databases |
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245 | (3) |
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247 | (1) |
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What Would a Document Database Modeler Do? |
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248 | (7) |
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The Joy of Denormalization |
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249 | (2) |
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Avoid Overusing Denormalization |
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251 | (2) |
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Just Say No to Joins, Sometimes |
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253 | (2) |
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Planning for Mutable Documents |
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255 | (3) |
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Avoid Moving Oversized Documents |
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258 | (1) |
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The Goldilocks Zone of Indexes |
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258 | (3) |
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259 | (1) |
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260 | (1) |
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Modeling Common Relations |
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261 | (6) |
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One-to-Many Relations in Document Databases |
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262 | (1) |
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Many-to-Many Relations in Document Databases |
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263 | (2) |
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Modeling Hierarchies in Document Databases |
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265 | (4) |
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Parent or Child References |
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265 | (1) |
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266 | (1) |
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267 | (2) |
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Case Study: Customer Manifests |
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269 | (4) |
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271 | (1) |
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271 | (1) |
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Separate Collections by Type? |
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272 | (1) |
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273 | (1) |
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273 | (2) |
| Part IV: Column Family Databases |
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275 | (86) |
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Chapter 9 Introduction to Column Family Databases |
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277 | (30) |
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In the Beginning, There Was Google BigTable |
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279 | (7) |
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Utilizing Dynamic Control over Columns |
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280 | (1) |
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Indexing by Row, Column Name, and Time Stamp |
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281 | (1) |
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Controlling Location of Data |
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282 | (1) |
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Reading and Writing Atomic Rows |
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283 | (1) |
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Maintaining Rows in Sorted Order |
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284 | (2) |
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Differences and Similarities to Key-Value and Document Databases |
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286 | (7) |
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Column Family Database Features |
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286 | (1) |
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Column Family Database Similarities to and Differences from Document Databases |
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287 | (2) |
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Column Family Database Versus Relational Databases |
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289 | (4) |
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Avoiding Multirow Transactions |
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290 | (1) |
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291 | (2) |
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Architectures Used in Column Family Databases |
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293 | (10) |
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HBase Architecture: Variety of Nodes |
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293 | (2) |
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Cassandra Architecture: Peer-to-Peer |
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295 | (1) |
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Getting the Word Around: Gossip Protocol |
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296 | (3) |
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Thermodynamics and Distributed Database: Why We Need Anti-Entropy |
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299 | (1) |
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Hold This for Me: Hinted Handoff |
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300 | (3) |
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When to Use Column Family Databases |
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303 | (1) |
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304 | (1) |
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304 | (1) |
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305 | (2) |
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Chapter 10 Column Family Database Terminology |
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307 | (22) |
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Basic Components of Column Family Databases |
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308 | (5) |
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309 | (1) |
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309 | (1) |
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310 | (2) |
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312 | (1) |
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Structures and Processes: Implementing Column Family Databases |
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313 | (9) |
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Internal Structures and Configuration Parameters of Column Family Databases |
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313 | (1) |
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Old Friends: Clusters and Partitions |
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314 | (3) |
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314 | (2) |
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316 | (1) |
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Taking a Look Under the Hood: More Column Family Database Components |
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317 | (5) |
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317 | (2) |
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319 | (2) |
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321 | (1) |
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322 | (4) |
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322 | (1) |
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323 | (1) |
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324 | (1) |
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325 | (1) |
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326 | (1) |
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327 | (1) |
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327 | (2) |
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Chapter 11 Designing for Column Family Databases |
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329 | (32) |
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Guidelines for Designing Tables |
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332 | (8) |
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Denormalize Instead of Join |
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333 | (1) |
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Make Use of Valueless Columns |
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334 | (1) |
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Use Both Column Names and Column Values to Store Data |
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334 | (1) |
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Model an Entity with a Single Row |
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335 | (2) |
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Avoid Hotspotting in Row Keys |
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337 | (1) |
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Keep an Appropriate Number of Column Value Versions |
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338 | (1) |
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Avoid Complex Data Structures in Column Values |
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339 | (1) |
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340 | (8) |
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When to Use Secondary Indexes Managed by the Column Family Database System |
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341 | (4) |
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When to Create and Manage Secondary Indexes Using Tables |
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345 | (3) |
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Tools for Working with Big Data |
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348 | (8) |
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Extracting, Transforming, and Loading Big Data |
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350 | (1) |
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351 | (4) |
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Describing and Predicting with Statistics |
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351 | (2) |
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Finding Patterns with Machine Learning |
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353 | (1) |
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Tools for Analyzing Big Data |
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354 | (1) |
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Tools for Monitoring Big Data |
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355 | (1) |
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356 | (1) |
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Case Study: Customer Data Analysis |
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357 | (2) |
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357 | (2) |
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359 | (1) |
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360 | (1) |
| Part V: Graph Databases |
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361 | (64) |
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Chapter 12 Introduction to Graph Databases |
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363 | (16) |
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363 | (2) |
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Graphs and Network Modeling |
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365 | (7) |
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Modeling Geographic Locations |
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365 | (1) |
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Modeling Infectious Diseases |
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366 | (3) |
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Modeling Abstract and Concrete Entities |
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369 | (1) |
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370 | (2) |
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Advantages of Graph Databases |
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372 | (4) |
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Query Faster by Avoiding Joins |
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372 | (3) |
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375 | (1) |
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Multiple Relations Between Entities |
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375 | (1) |
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376 | (1) |
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376 | (1) |
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377 | (2) |
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Chapter 13 Graph Database Terminology |
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379 | (20) |
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380 | (5) |
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380 | (1) |
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381 | (2) |
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383 | (1) |
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384 | (1) |
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385 | (3) |
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385 | (1) |
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386 | (1) |
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387 | (1) |
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Properties of Graphs and Nodes |
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388 | (4) |
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388 | (1) |
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389 | (1) |
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390 | (1) |
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390 | (1) |
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391 | (1) |
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392 | (4) |
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Undirected and Directed Graphs |
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392 | (1) |
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393 | (1) |
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394 | (1) |
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395 | (1) |
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395 | (1) |
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396 | (1) |
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397 | (1) |
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397 | (2) |
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Chapter 14 Designing for Graph Databases |
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399 | (26) |
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Getting Started with Graph Design |
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400 | (8) |
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Designing a Social Network Graph Database |
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401 | (4) |
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Queries Drive Design (Again) |
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405 | (3) |
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408 | (7) |
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Cypher: Declarative Querying |
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408 | (2) |
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Gremlin: Query by Graph Traversal |
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410 | (5) |
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410 | (2) |
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Traversing a Graph with Depth-First and Breadth-First Searches |
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412 | (3) |
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Tips and Traps of Graph Database Design |
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415 | (5) |
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Use Indexes to Improve Retrieval Time |
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415 | (1) |
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Use Appropriate Types of Edges |
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416 | (1) |
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Watch for Cycles When Traversing Graphs |
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417 | (1) |
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Consider the Scalability of Your Graph Database |
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418 | (2) |
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420 | (1) |
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Case Study: Optimizing Transportation Routes |
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420 | (3) |
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420 | (1) |
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Designing a Graph Analysis Solution |
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421 | (2) |
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423 | (1) |
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423 | (2) |
| Part VI: Choosing A Database For Your Application |
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425 | (16) |
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Chapter 15 Guidelines for Selecting a Database |
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427 | (14) |
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Choosing a NoSQL Database |
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428 | (6) |
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Criteria for Selecting Key-Value Databases |
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429 | (1) |
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Use Cases and Criteria for Selecting Document Databases |
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430 | (1) |
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Use Cases and Criteria for Selecting Column Family Databases |
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431 | (2) |
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Use Cases and Criteria for Selecting Graph Databases |
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433 | (1) |
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Using NoSQL and Relational Databases Together |
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434 | (2) |
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436 | (1) |
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436 | (1) |
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437 | (4) |
| Part VII: Appendices |
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441 | (40) |
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Appendix A Answers to
Chapter Review Questions |
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443 | (34) |
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Appendix B List of NoSQL Databases |
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477 | (4) |
| Glossary |
|
481 | (10) |
| Index |
|
491 | |
