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Building Big Data Pipelines with Apache Beam

You're reading from  Building Big Data Pipelines with Apache Beam

Product type Book
Published in Jan 2022
Publisher Packt
ISBN-13 9781800564930
Pages 342 pages
Edition 1st Edition
Languages
Author (1):
Jan Lukavský Jan Lukavský
Profile icon Jan Lukavský

Table of Contents (13) Chapters

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

Describing the anatomy of an Apache Beam runner

Let's first take a look at the typical life cycle of a pipeline, from the construction time to the pipeline teardown. The complete life cycle is illustrated in the following figure:

Figure 8.1 – The execution of a pipeline by a runner

The pipeline construction is already well known – we spent most of this book showing how to construct and test pipelines. The next step is submitting the pipeline to a runner. This is the point where the pipeline crosses the SDK-runner boundary, typically by a call to Pipeline.run().

After the pipeline is submitted to the runner, the runner proceeds as follows:

  1. Once a runner receives a pipeline, it first performs pipeline validation. This consists of various runner-independent validations – for instance, validating that an appropriate window function and/or trigger is being set and depending on the boundedness of the inputs of the pipeline. These...
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