文件名称：Introduction to Apache Flink
文件大小：3.39MB
文件格式：ZIP
更新时间：2021-12-22 08:20:21
大数据 flink
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
【文件预览】：
Introduction_to_Apache_Flink.pdf