Table of Contents
Preface Or: What Are You Getting Yourself Into Here? vii
Part I The Beam Model
1 Streaming 101 3
Terminology: What Is Streaming? 4
On the Greatly Exaggerated Limitations of Streaming 6
Event Time Versus Processing Time 9
Data Processing Patterns 12
Bounded Data 12
Unbounded Data: Batch 13
Unbounded Data: Streaming 14
Summary 22
2 The What, Where, When, and How of Data Processing 25
Roadmap 26
Batch Foundations: What and Where 28
When: Transformations 28
Where: Windowing 32
Going Streaming: When and How 34
When: The Wonderful Thing About Triggers Is Triggers Are Wonderful Things! 34
When: Watermarks 39
When: Early/On-Time/Late Triggers FTW! 44
When: Allowed Lateness (i.e., Garbage Collection) 47
How: Accumulation 51
Summary 55
3 Watermarks 59
Definition 59
Source Watermark Creation 62
Perfect Watermark Creation 64
Heuristic Watermark Creation 65
Watermark Propagation 67
Understanding Watermark Propagation 69
Watermark Propagation and Output Timestamps 75
The Tricky Case of Overlapping Windows 80
Percentile Watermarks 81
Processing-Time Watermarks 84
Case Studies 86
Case Study: Watermarks in Google Cloud Dataflow 87
Case Study: Watermarks in Apache Flink 88
Case Study: Source Watermarks for Google Cloud Pub/Sub 90
Summary 93
4 Advanced Windowing 95
When/Where: Processing-Time Windows 95
Event-Time Windowing 97
Processing-Time Windowing via Triggers 98
Processing-Time Windowing via Ingress Time 100
Where: Session Windows 103
Where: Custom Windowing 107
Variations on Fixed Windows 108
Variations on Session Windows 115
One Size Does Not Fit All 119
Summary 119
5 Exactly-Once and Side Effects 121
Why Exactly Once Matters 121
Accuracy Versus Completeness 122
Side Effects 123
Problem Definition 123
Ensuring Exactly Once in Shuffle 125
Addressing Determinism 126
Performance 127
Graph Optimization 127
Bloom Filters 128
Garbage Collection 129
Exactly Once in Sources 130
Exactly Once in Sinks 131
Use Cases 133
Example Source: Cloud Pub/Sub 133
Example Sink: Files 134
Example Sink: Google BigQuery 135
Other Systems 136
Apache Spark Streaming 136
Apache Flink 136
Summary 138
Part II Streams and Tables
6 Streams and Tables 141
Stream-and-Table Basics Or: a Special Theory of Stream and Table Relativity 142
Toward a General Theory of Stream and Table Relativity 143
Batch Processing Versus Streams and Tables 144
A Streams and Tables Analysis of MapReduce 144
Reconciling with Batch Processing 150
What, Where, When, and How in a Streams and Tables World 150
What: Transformations 150
Where: Windowing 154
When: Triggers 157
How: Accumulation 165
A Holistic View of Streams and Tables in the Beam Model 166
A General Theory of Stream and Table Relativity 171
Summary 172
7 The Practicalities of Persistent State 175
Motivation 175
The Inevitability of Failure 176
Correctness and Efficiency 177
Implicit State 178
Raw Grouping 179
Incremental Combining 181
Generalized State 184
Case Study: Conversion Attribution 186
Conversion Attribution with Apache Beam 189
Summary 199
8 Streaming SQL 201
What Is Streaming SQL? 201
Relational Algebra 202
Time-Varying Relations 203
Streams and Tables 207
Looking Backward: Stream and Table Biases 214
The Beam Model: A Stream-Biased Approach 214
The SQL Model: A Table-Biased Approach 218
Looking Forward: Toward Robust Streaming SQL 226
Stream and Table Selection 227
Temporal Operators 228
Summary 249
9 Streaming Joins 253
All Your Joins Are Belong to Streaming 253
Unwindowed Joins 254
Full Outer 255
Left Outer 258
Right Outer 259
Inner 259
Anti 261
Semi 262
Windowed Joins 266
Fixed Windows 267
Temporal Validity 269
Summary 282
10 The Evolution of Large-Scale Data Processing 283
MapReduce 284
Hadoop 288
Flume 289
Storm 294
Spark 297
MillWheel 300
Kafka 304
Cloud Dataflow 307
Flink 309
Beam 313
Summary 316
Index 319
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