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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 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

Task 13 – Writing a reusable PTransform – StreamingInnerJoin

Our goal in this task is to create a reusable PTransform that will do the fully stream-oriented join. For simplicity, we will constrain ourselves to the inner join as the implementation of the full outer join would become a little lengthy. We will mention the differences at the end of this section, though.

Problem definition

Implement a PTransform that will be applied to a PCollectionTuple with exactly two TupleTags (leftHandTag and rightHandTag) and will produce a streaming join operation on top of them. The transform needs to take functions to extract join keys and both sides' primary keys. The output will contain the (inner) joined values and any retractions.

Problem decomposition discussion

We described the theoretical properties of a streaming join in the previous section, so we will focus on describing the semantics in practical examples. We will then use these examples to test our solution...

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