Introduction to Apache Flink 翻译

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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