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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

Task 2 – Calculating the maximal length of a word in a stream

This is a similar example. In the previous task, we wanted to calculate the K most frequent words in a stream for a fixed time window. How would our solution change if our task was to calculate this from the beginning of the stream? Let's define the problem.

Defining the problem

Given an input data stream of lines of text, calculate the longest word ever seen in this stream. Start with an empty word value; once a longer word is seen, immediately output the new longest word.

Discussing the problem decomposition

Although the logic seems to be similar to the previous task, it can be simplified as follows:

Figure 2.3 – The problem decomposition

Note, there are two main differences from the previous task:

  • We must compute the word with the longest length; although this could be viewed as a Top transform, with K equal to one, Beam has a specific transform for that...
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