Introduction to Apache Flink 翻译2018-12-02 本文已影响12人 耳边的火 无链接部分暂不翻译，链接无反应代表还未翻译完成，工作之余翻译，时间琐碎，更新较慢。 1.Why Apache Flink? Consequences of Not Doing Streaming Well 翻译略 Goals for Processing Continuous Event Data Evolution of Stream Processing Technologies First Look at Apache Flink Flink in Production 翻译略 Where Flink Fits 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 3.What Flink Does Different Types of Correctness Hierarchical Use Cases: Adopting Flink in Stages 4.Handing Time Counting with Batch and Lambda Architecture Counting with Streaming Architecture Notions of Time Windows Time Travel Watermarks A Real-World Example: Kappa Architecture at Ericsson 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 6.Batch Is a Special Case of Streaming Batch Processing Technology Case Study: Flink as a Batch Processor
