Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Jan 2022
Publisher Packt
ISBN-13 9781800564930
Length 342 pages
Edition 1st Edition
Languages
Concepts
Arrow right icon
Author (1):
Arrow left icon
Jan Lukavský Jan Lukavský
Author Profile Icon Jan Lukavský
Jan Lukavský
Arrow right icon
View More author details
Toc

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

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...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at R$50/month. Cancel anytime