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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

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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