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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

Introducing the primitive PTransform object – GroupByKey

As we have seen, a GroupByKey transform works in the way illustrated in the following figure:

Figure 2.14 – GroupByKey

As in the case of Combine PTransform objects, the input stream must be keyed. This is a way of saying that the PCollection must have elements of the KV type. This is generally true for any stateful operations. The reason for this is that having a state (which cannot be partitioned) divided into smaller, independent sub-states means that it cannot scale and would therefore lead to scalability issues. Therefore, Beam explicitly prohibits this and enforces the use of keyed PCollections for the input of each stateful operation.

The GroupByKey transform then takes this keyed stream (in Figure 2.14, the key is represented as the shape of the stream element) and creates something that can be viewed as a sub-stream for each key. We can then process elements with a different...

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