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Foreword
Preface
Part Ⅰ． Fundamentals of Stream Processing with Apache Spark
1． Introducing Stream Processing
What Is Stream Processing?
Batch Versus Stream Processing
The Notion of Time in Stream Processing
The Factor of Uncertainty
Some Examples of Stream Processing
Scaling Up Data Processing
MapReduce
The Lesson Learned： Scalability and Fault Tolerance
Distributed Stream Processing
Stateful Stream Processing in a Distributed System
Introducing Apache Spark
The First Wave： Functional APIs
The Second Wave： SQL
A Unified Engine
Spark Components
Spark Streaming
Structured Streaming
Where Next?
2． Stream-Processing Model
Sources and Sinks
Immutable Streams Defined from One Another
Transformations and Aggregations
Window Aggregations
Tumbling Windows
Sliding Windows
Stateless and Stateful Processing
Stateful Streams
An Example： Local Stateful Computation in Scala
A Stateless Definition of the Fibonacci Sequence as a Stream
Transformation
Stateless or Stateful Streaming
The Effect of Time
Computing on Timestamped Events
Timestamps as the Provider of the Notion of Time
Event Time Versus Processing Time
Computing with a Watermark
Summary
3． Streaming Architectures
Components of a Data Platform
Architectural Models
The Use of a Batch-Processing Component in a Streaming Application
Referential Streaming Architectures
The Lambda Architecture
The Kappa Architecture
Streaming Versus Batch Algorithms
Streaming Algorithms Are Sometimes Completely Different in Nature
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