Book Image

Building Big Data Pipelines with Apache Beam

By : Jan Lukavský
Book Image

Building Big Data Pipelines with Apache Beam

By: Jan Lukavský

Overview of this book

Apache Beam is an open source unified programming model for implementing and executing data processing pipelines, including Extract, Transform, and Load (ETL), batch, and stream processing. This book will help you to confidently build data processing pipelines with Apache Beam. You’ll start with an overview of Apache Beam and understand how to use it to implement basic pipelines. You’ll also learn how to test and run the pipelines efficiently. As you progress, you’ll explore how to structure your code for reusability and also use various Domain Specific Languages (DSLs). Later chapters will show you how to use schemas and query your data using (streaming) SQL. Finally, you’ll understand advanced Apache Beam concepts, such as implementing your own I/O connectors. By the end of this book, you’ll have gained a deep understanding of the Apache Beam model and be able to apply it to solve problems.
Table of Contents (13 chapters)
1
Section 1 Apache Beam: Essentials
5
Section 2 Apache Beam: Toward Improving Usability
9
Section 3 Apache Beam: Advanced Concepts

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...