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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 Section 1 Apache Beam: Essentials
Chapter 1: Introduction to Data Processing with Apache Beam Chapter 2: Implementing, Testing, and Deploying Basic Pipelines Chapter 3: Implementing Pipelines Using Stateful Processing Section 2 Apache Beam: Toward Improving Usability
Chapter 4: Structuring Code for Reusability Chapter 5: Using SQL for Pipeline Implementation Chapter 6: Using Your Preferred Language with Portability Section 3 Apache Beam: Advanced Concepts
Chapter 7: Extending Apache Beam's I/O Connectors Chapter 8: Understanding How Runners Execute Pipelines 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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