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Stream Processing with Apache Flink by Vasiliki Kalavri, Fabian Hueske

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Chapter 2. Stream Processing Fundamentals

So far, you have seen how stream processing addresses some of the limitations of traditional batch processing and how it enables new applications and architectures. You also know a little bit about the evolution of the open source stream processing space and what a Flink streaming application looks like. In this chapter, you will enter the streaming world for good.

The goal of this chapter is to introduce the fundamental concepts of stream processing and the requirements of its frameworks. We hope that after reading this chapter, you will be able to evaluate the features of modern stream processing systems.

Introduction to Dataflow Programming

Before we delve into the fundamentals of stream processing, let’s look at the background on dataflow programming and the terminology we will use throughout this book.

Dataflow Graphs

As the name suggests, a dataflow program describes how data flows between operations. Dataflow programs are commonly represented as directed graphs, where nodes are called operators and represent computations and edges represent data dependencies. Operators are the basic functional units of a dataflow application. They consume data from inputs, perform a computation on them, and produce data to outputs for further processing. Operators without input ports are called data sources and operators without output ports are called data sinks. A dataflow graph must have at least one data source and one data sink. Figure 2-1 ...

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