Books & Videos

Table of Contents

  1. Chapter 1 Why Apache Flink?

    1. Consequences of Not Doing Streaming Well

    2. Goals for Processing Continuous Event Data

    3. Evolution of Stream Processing Technologies

    4. First Look at Apache Flink

    5. Flink in Production

    6. Where Flink Fits

  2. Chapter 2 Stream-First Architecture

    1. Traditional Architecture versus Streaming Architecture

    2. Message Transport and Message Processing

    3. The Transport Layer: Ideal Capabilities

    4. Streaming Data for a Microservices Architecture

    5. Beyond Real-Time Applications

    6. Geo-Distributed Replication of Streams

  3. Chapter 3 What Flink Does

    1. Different Types of Correctness

    2. Hierarchical Use Cases: Adopting Flink in Stages

  4. Chapter 4 Handling Time

    1. Counting with Batch and Lambda Architectures

    2. Counting with Streaming Architecture

    3. Notions of Time

    4. Windows

    5. Time Travel

    6. Watermarks

    7. A Real-World Example: Kappa Architecture at Ericsson

  5. Chapter 5 Stateful Computation

    1. Notions of Consistency

    2. Flink Checkpoints: Guaranteeing Exactly Once

    3. Savepoints: Versioning State

    4. End-to-End Consistency and the Stream Processor as a Database

    5. Flink Performance: the Yahoo! Streaming Benchmark

    6. Conclusion

  6. Chapter 6 Batch Is a Special Case of Streaming

    1. Batch Processing Technology

    2. Case Study: Flink as a Batch Processor

  7. Appendix Additional Resources

    1. Going Further with Apache Flink

    2. Selected O’Reilly Publications by Ted Dunning and Ellen Friedman