Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Jan 2022
Publisher Packt
ISBN-13 9781800564930
Length 342 pages
Edition 1st Edition
Languages
Concepts
Arrow right icon
Author (1):
Arrow left icon
Jan Lukavský Jan Lukavský
Author Profile Icon Jan Lukavský
Jan Lukavský
Arrow right icon
View More author details
Toc

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

Summary

In this chapter, we walked through how runners execute pipelines in both classic mode and using the portability layer. We have seen that classic runners are suitable only for cases where a particular underlying technology – for instance, Apache Flink – has an API in the same language as the pipeline SDK. The most practical cases for this include using the Java SDK for both the runner and the pipeline.

In cases where the language of the runner and the pipeline SDK differ, we have to use portability (Fn API), which brings some overhead. We have seen how pipeline fusion is used to reduce this overhead as much as possible. We have also discussed situations where we want to prevent fusion and how to do this by inserting a shuffle boundary.

Next, we discussed the responsibilities of a runner with regard to state management. We saw how the runner ensures fault tolerance and correctness upon failures. We outlined two basic types of fault-tolerant states: local state...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime