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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 18 – Implementing SportTracker in the Python SDK

This task will be a reimplementation of Task 5 from Chapter 2, Implementing, Testing, and Deploying Basic Pipelines. Again, for clarity, let's restate the problem definition.

Problem definition

Given an input data stream of quadruples (workoutId, gpsLatitude, gpsLongitude, and timestamp), calculate the current speed and total tracked distance. The data comes from a GPS tracker that sends data only when the user starts a sports activity. We can assume that workoutId is unique and contains userId in it.

The caveats of the implementation are the same as what we discussed in the original Task 5, so we'll skip to its Python SDK implementation right away.

Solution implementation

The complete implementation can be found in the source code for of this chapter, in chapter6/src/main/python/sport_tracker.py. The logic is concentrated in two functions – SportTrackerCalc and computeMetrics:

  1. The...
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