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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

Stream-to-stream joins explained

Let's look at Figure 4.6 but modify it a little. Let's say that we want to get the results from the join as quickly as possible. Currently, the latency is defined by the length of the window – because the join is delegated on CoGroupBeyKey, which, in turn, relies on GroupByKey, we can only get results when a trigger that's associated with our window function fires. This typically happens at the end of the window (though it can happen sooner, which would then result in duplicates). If we want to avoid deduplication downstream and increase efficiency, because the duplicates can become a performance issue, we have no other option than to decrease the size of the window. At the limit, we end up with a situation like this:

Figure 4.6 – Degenerated windowed join

The smaller we make our window, the less data we can join. If the window's duration is zero, will not be able to join any data at all. This...

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