Streaming Systems

Preface Or: What Are You Getting Yourself Into Here?
Navigating This Book
Takeaways
Conventions Used in This Book
Online Resources
Figures
Code Snippets
O’Reilly Safari
How to Contact Us
Acknowledgments

I. The Beam Model
1. Streaming 101
Terminology: What Is Streaming?
On the Greatly Exaggerated Limitations of Streaming
Event Time Versus Processing Time
Data Processing Patterns
Bounded Data
Unbounded Data: Batch
Unbounded Data: Streaming
Summary

2. The What, Where, When, and How of Data Processing
Roadmap
Batch Foundations: What and Where
What: Transformations
Where: Windowing
Going Streaming: When and How
When: The Wonderful Thing About Triggers Is Triggers Are Wonderful Things!
When: Watermarks
When: Early/On-Time/Late Triggers FTW!
When: Allowed Lateness (i.e., Garbage Collection)
How: Accumulation
Summary

3. Watermarks
Definition
Source Watermark Creation
Perfect Watermark Creation
Heuristic Watermark Creation
Watermark Propagation
Understanding Watermark Propagation
Watermark Propagation and Output Timestamps
The Tricky Case of Overlapping Windows
Percentile Watermarks
Processing-Time Watermarks
Case Studies
Case Study: Watermarks in Google Cloud Dataflow
Case Study: Watermarks in Apache Flink
Case Study: Source Watermarks for Google Cloud Pub/Sub
Summary

4. Advanced Windowing
When/Where: Processing-Time Windows
Event-Time Windowing
Processing-Time Windowing via Triggers
Processing-Time Windowing via Ingress Time
Where: Session Windows
Where: Custom Windowing
Variations on Fixed Windows
Variations on Session Windows
One Size Does Not Fit All
Summary

5. Exactly-Once and Side Effects
Why Exactly Once Matters
Accuracy Versus Completeness
Side Effects
Problem Definition
Ensuring Exactly Once in Shuffle
Addressing Determinism
Performance
Graph Optimization
Bloom Filters
Garbage Collection
Exactly Once in Sources
Exactly Once in Sinks
Use Cases
Example Source: Cloud Pub/Sub
Example Sink: Files
Example Sink: Google BigQuery
Other Systems
Apache Spark Streaming
Apache Flink
Summary

II. Streams and Tables
6. Streams and Tables
Stream-and-Table Basics Or: a Special Theory of Stream and Table Relativity
Toward a General Theory of Stream and Table Relativity
Batch Processing Versus Streams and Tables
A Streams and Tables Analysis of MapReduce
Reconciling with Batch Processing
What, Where, When, and How in a Streams and Tables World
What: Transformations
Where: Windowing
When: Triggers
How: Accumulation
A Holistic View of Streams and Tables in the Beam Model
A General Theory of Stream and Table Relativity
Summary

7. The Practicalities of Persistent State
Motivation
The Inevitability of Failure
Correctness and Efficiency
Implicit State
Raw Grouping
Incremental Combining
Generalized State
Case Study: Conversion Attribution
Conversion Attribution with Apache Beam
Summary

8. Streaming SQL
What Is Streaming SQL?
Relational Algebra
Time-Varying Relations
Streams and Tables
Looking Backward: Stream and Table Biases
The Beam Model: A Stream-Biased Approach
The SQL Model: A Table-Biased Approach
Looking Forward: Toward Robust Streaming SQL
Stream and Table Selection
Temporal Operators
Summary

9. Streaming Joins
All Your Joins Are Belong to Streaming
Unwindowed Joins
FULL OUTER
LEFT OUTER
RIGHT OUTER
INNER
ANTI
SEMI
Windowed Joins
Fixed Windows
Temporal Validity
Summary

10. The Evolution of Large-Scale Data Processing
MapReduce
Hadoop
Flume
Storm
Spark
MillWheel
Kafka
Cloud Dataflow
Flink
Beam
Summary

Index