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Building Big Data Pipelines with Apache Beam

You're reading from   Building Big Data Pipelines with Apache Beam Use a single programming model for both batch and stream data processing

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Product type Paperback
Published in Jan 2022
Publisher Packt
ISBN-13 9781800564930
Length 342 pages
Edition 1st Edition
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Author (1):
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Jan Lukavský Jan Lukavský
Author Profile Icon Jan Lukavský
Jan Lukavský
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Table of Contents (13) Chapters Close

Preface 1. Section 1 Apache Beam: Essentials
2. Chapter 1: Introduction to Data Processing with Apache Beam FREE CHAPTER 3. Chapter 2: Implementing, Testing, and Deploying Basic Pipelines 4. Chapter 3: Implementing Pipelines Using Stateful Processing 5. Section 2 Apache Beam: Toward Improving Usability
6. Chapter 4: Structuring Code for Reusability 7. Chapter 5: Using SQL for Pipeline Implementation 8. Chapter 6: Using Your Preferred Language with Portability 9. Section 3 Apache Beam: Advanced Concepts
10. Chapter 7: Extending Apache Beam's I/O Connectors 11. Chapter 8: Understanding How Runners Execute Pipelines 12. Other Books You May Enjoy

Writing a custom data sink

As opposed to a data source, a data sink has much less work to do. Actually – in trivial cases – a data sink can be implemented using a plain ParDo object. In fact, we have already implemented one of these, which was PrintElements, located in the util module. The PrintElements transform can be considered a sink to stderr, as we can see from this implementation:

@Override
public PDone expand(PCollection<T> input) {
  input.apply(ParDo.of(new LogResultsFn<>()));
  return PDone.in(input.getPipeline());
}
private static class LogResultsFn<T> extends DoFn<T, Void> {
  @ProcessElement
  public void process(@Element T elem) {
    System.err.println(elem);
  }
}

This sink is very simplistic – a real-life solution would need some of the tools we already know. For example, batching RPCs using bundle life cycles via @StartBundle and @FinishBundle...

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