Book Image

Building Big Data Pipelines with Apache Beam

By : Jan Lukavský
Book Image

Building Big Data Pipelines with Apache Beam

By: Jan Lukavský

Overview of this book

Apache Beam is an open source unified programming model for implementing and executing data processing pipelines, including Extract, Transform, and Load (ETL), batch, and stream processing. This book will help you to confidently build data processing pipelines with Apache Beam. You’ll start with an overview of Apache Beam and understand how to use it to implement basic pipelines. You’ll also learn how to test and run the pipelines efficiently. As you progress, you’ll explore how to structure your code for reusability and also use various Domain Specific Languages (DSLs). Later chapters will show you how to use schemas and query your data using (streaming) SQL. Finally, you’ll understand advanced Apache Beam concepts, such as implementing your own I/O connectors. By the end of this book, you’ll have gained a deep understanding of the Apache Beam model and be able to apply it to solve problems.
Table of Contents (13 chapters)
1
Section 1 Apache Beam: Essentials
5
Section 2 Apache Beam: Toward Improving Usability
9
Section 3 Apache Beam: Advanced Concepts

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...