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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

Understanding default triggers, on time, and closing behavior

As we have seen, when specifying a PTransform window, which is necessary for all grouping operations, we may optionally specify a triggering. We explored this concept in the theoretical part of Chapter 1, Introduction to Data Processing with Apache Beam. Here, we will focus specifically on understanding how Beam interprets triggers and when the output is triggered.

The simplest trigger we can specify is the AfterWatermark.pastEndOfWindow() trigger, which simply means trigger the output once the window has completed. That is, once the watermark passes the end timestamp of each particular window. We have already seen that each window has such an end timestamp, including the global window, which has a timestamp set in the very distant future.

A question we might ask is, which trigger will be used if we create a PTransform window without specifying a trigger? The answer is DefaultTrigger. How should this trigger be defined...

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