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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 12 – enhancing SportTracker by runner motivation using CoGroupByKey

In Task 11, we solved the problem of sending motivating push notifications to users currently on track using side inputs. We already know that this approach might suffer from the problem of forcing the side input to fit into memory for all users, which might become restrictive once we have many users. The chances are pretty high that we will not ever hit the memory limit in such a use case, but let's assume that we want to avoid using side inputs and use CoGroupByKey instead.

Now, let's redefine Task 11 so that we can reimplement it by using CoGroupByKey.

Problem definition

Implement Task 11 but instead of using side inputs, use CoGroupByKey to avoid any possible memory pressure due to forcing side input to fit into memory for all keys (user-tracks).

Problem decomposition discussion

We will reuse as much code from Task 11 as we can. Our discussion of the problem remains, so our...

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