Introduction to Apache Flink下载

Chapter 1 Why Apache Flink? 　　Consequences of Not Doing Streaming Well 　　Goals for Processing Continuous Event Data 　　Evolution of Stream Processing Technologies 　　First Look at Apache Flink 　　Flink in Production 　　Where Flink Fits Chapter 2 Stream-First Architecture 　　Traditional Architecture versus Streaming Architecture 　　Message Transport and Message Processing 　　The Transport Layer: Ideal Capabilities 　　Streaming Data for a Microservices Architecture 　　Beyond Real-Time Applications 　　Geo-Distributed Replication of Streams Chapter 3 What Flink Does 　　Different Types of Correctness 　　Hierarchical Use Cases: Adopting Flink in Stages Chapter 4 Handling Time 　　Counting with Batch and Lambda Architectures 　　Counting with Streaming Architecture 　　Notions of Time 　　Windows 　　Time Travel 　　Watermarks 　　A Real-World Example: Kappa Architecture at Ericsson Chapter 5 Stateful Computation 　　Notions of Consistency 　　Flink Checkpoints: Guaranteeing Exactly Once 　　Savepoints: Versioning State 　　End-to-End Consistency and the Stream Processor as a Database 　　Flink Performance: the Yahoo! Streaming Benchmark 　　Conclusion Chapter 6 Batch Is a Special Case of Streaming 　　Batch Processing Technology 　　Case Study: Flink as a Batch Processor Appendix Additional Resources 　　Going Further with Apache Flink 　　Selected O’Reilly Publications by Ted Dunning and Ellen Friedman