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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 3 – Calculating the average length of words in a stream

In this task, we will investigate how we can use CombineFn and accumulators to compute a directly non-combinable reduction and average. Let's see how this works.

Defining the problem

Given an input data stream of lines of text, calculate the average length of words currently seen in this stream. Output the current average as frequently as possible, ideally after every word.

Discussing the problem decomposition

Calculating an average is not a directly combinable function. An average of averages is not a proper average of the original data. However, we can calculate an average using an accumulator. An accumulator would be a pair of (sum, count) and the output will be extracted using a function that divides the sum by the count. We can illustrate this with Figure 2.9:

Figure 2.9 – Calculating an average using CombineFn

We will need to create an accumulator object for...

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