Table of Contents
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Part I.
The Basics
1. Introduction to Scalable Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
What Is Scalability?
Examples of System Scale in the Early 2000s
How Did We Get Here? A Brief History of System Growth
Scalability Basic Design Principles
Scalability and Costs
Scalability and Architecture Trade-Offs
Performance
Availability
Security
Manageability
Summary and Further Reading
3
6
7
9
11
13
13
14
15
16
17
2. Distributed Systems Architectures: An Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Basic System Architecture
Scale Out
Scaling the Database with Caching
Distributing the Database
Multiple Processing Tiers
Increasing Responsiveness
Systems and Hardware Scalability
Summary and Further Reading
19
21
23
25
27
30
32
34
v
3. Distributed Systems Essentials. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Communications Basics
Communications Hardware
Communications Software
Remote Method Invocation
Partial Failures
Consensus in Distributed Systems
Time in Distributed Systems
Summary and Further Reading
35
36
39
43
49
53
56
58
4. An Overview of Concurrent Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
Why Concurrency?
Threads
Order of Thread Execution
Problems with Threads
Race Conditions
Deadlocks
Thread States
Thread Coordination
Thread Pools
Barrier Synchronization
Thread-Safe Collections
Summary and Further Reading
Part II.
62
64
67
68
69
73
78
79
82
84
86
88
Scalable Systems
5. Application Services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
Service Design
Application Programming Interface (API)
Designing Services
State Management
Applications Servers
Horizontal Scaling
Load Balancing
Load Distribution Policies
Health Monitoring
Elasticity
Session Affinity
Summary and Further Reading
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93
94
97
100
103
106
107
109
109
110
111
113
6. Distributed Caching. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
Application Caching
Web Caching
Cache-Control
Expires and Last-Modified
Etag
Summary and Further Reading
115
120
121
121
122
124
7. Asynchronous Messaging. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
Introduction to Messaging
Messaging Primitives
Message Persistence
Publish–Subscribe
Message Replication
Example: RabbitMQ
Messages, Exchanges, and Queues
Distribution and Concurrency
Data Safety and Performance Trade-offs
Availability and Performance Trade-Offs
Messaging Patterns
Competing Consumers
Exactly-Once Processing
Poison Messages
Summary and Further Reading
128
128
130
131
132
133
133
135
138
140
141
141
142
143
144
8. Serverless Processing Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
The Attractions of Serverless
Google App Engine
The Basics
GAE Standard Environment
Autoscaling
AWS Lambda
Lambda Function Life Cycle
Execution Considerations
Scalability
Case Study: Balancing Throughput and Costs
Choosing Parameter Values
GAE Autoscaling Parameter Study Design
Results
Summary and Further Reading
147
149
149
150
151
152
153
154
155
157
158
159
160
161
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9. Microservices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
The Movement to Microservices
Monolithic Applications
Breaking Up the Monolith
Deploying Microservices
Principles of Microservices
Resilience in Microservices
Cascading Failures
Bulkhead Pattern
Summary and Further Reading
164
164
166
168
170
172
173
178
180
Part III. Scalable Distributed Databases
10. Scalable Database Fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
Distributed Databases
Scaling Relational Databases
Scaling Up
Scaling Out: Read Replicas
Scale Out: Partitioning Data
Example: Oracle RAC
The Movement to NoSQL
NoSQL Data Models
Query Languages
Data Distribution
The CAP Theorem
Summary and Further Reading
185
186
186
188
189
191
192
196
197
198
202
203
11. Eventual Consistency. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205
What Is Eventual Consistency?
Inconsistency Window
Read Your Own Writes
Tunable Consistency
Quorum Reads and Writes
Replica Repair
Active Repair
Passive Repair
Handling Conflicts
Last Writer Wins
Version Vectors
Summary and Further Reading
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205
206
207
209
211
213
214
214
215
216
217
221
12. Strong Consistency. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223
Introduction to Strong Consistency
Consistency Models
Distributed Transactions
Two-Phase Commit
2PC Failure Modes
Distributed Consensus Algorithms
Raft
Leader Election
Strong Consistency in Practice
VoltDB
Google Cloud Spanner
Summary and Further Reading
224
226
227
228
230
232
234
236
238
238
241
244
13. Distributed Database Implementations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247
Redis
Data Model and API
Distribution and Replication
Strengths and Weaknesses
MongoDB
Data Model and API
Distribution and Replication
Strengths and Weaknesses
Amazon DynamoDB
Data Model and API
Distribution and Replication
Strengths and Weaknesses
Summary and Further Reading
Part IV.
248
248
250
251
253
254
256
259
260
261
264
266
267
Event and Stream Processing
14. Scalable Event-Driven Processing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271
Event-Driven Architectures
Apache Kafka
Topics
Producers and Consumers
Scalability
Availability
Summary and Further Reading
272
274
275
276
279
283
284
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15. Stream Processing Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287
Introduction to Stream Processing
Stream Processing Platforms
Case Study: Apache Flink
DataStream API
Scalability
Data Safety
Conclusions and Further Reading
288
291
293
293
295
298
300
16. Final Tips for Success. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303
Automation
Observability
Deployment Platforms
Data Lakes
Further Reading and Conclusions
304
305
306
307
307
Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309
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