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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
Toc

Table of Contents (13) Chapters close

Preface 1. Section 1 Apache Beam: Essentials
2. Chapter 1: Introduction to Data Processing with Apache Beam 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

Specifying the PCollection Coder object and the TypeDescriptor object

The PCollection name is an abbreviation of parallel collection, which suggests that it is a collection of elements that are somewhat distributed among multiple workers. And that is exactly what a parallel collection is. To be able to communicate with the individual elements of this collection, each element needs a serialized representation. That is, we need a piece of code that takes a raw (in-memory) object and produces a byte representation that can be sent over the wire. After receiving on the remote side, we need another piece of code that will take this byte representation and recreate the original in-memory object (or rather, a copied version of the original). And that is exactly what coders are for.

We have already used Coder in our test cases. Recall how we constructed our TestStream object:

TestStream.create(StringUtf8Coder.of())

The reason we need to specify a Coder object here is that every PCollection...

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