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Apache Spark 2.x Cookbook

You're reading from   Apache Spark 2.x Cookbook Over 70 cloud-ready recipes for distributed Big Data processing and analytics

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Product type Paperback
Published in May 2017
Publisher
ISBN-13 9781787127265
Length 294 pages
Edition 1st Edition
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Author (1):
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Rishi Yadav Rishi Yadav
Author Profile Icon Rishi Yadav
Rishi Yadav
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Apache Spark FREE CHAPTER 2. Developing Applications with Spark 3. Spark SQL 4. Working with External Data Sources 5. Spark Streaming 6. Getting Started with Machine Learning 7. Supervised Learning with MLlib — Regression 8. Supervised Learning with MLlib — Classification 9. Unsupervised Learning 10. Recommendations Using Collaborative Filtering 11. Graph Processing Using GraphX and GraphFrames 12. Optimizations and Performance Tuning

Loading data from the local filesystem


Though the local filesystem is not a good fit to store big data due to disk size limitations and lack of distributed nature, technically you can load data in distributed systems using the local filesystem. But then the file/directory you are accessing has to be available on each node.

Note

Please note that if you are planning to use this feature to load side data, it is not a good idea. To load side data, Spark has a broadcast variable feature, which will be discussed in upcoming chapters. Enriching data with side data is a very common use-case and we will cover how to do it in the subsequent chapters. 

In this recipe, we will look at how to load data in Spark from the local filesystem.

How to do it...

Let's start with the example of Shakespeare's "to be or not to be":

  1. Create the words directory by using the following command:
$ mkdir words
  1. Get into the words directory:
$ cd words
  1. Create the sh.txt text file and enter "to be or not to be" in it:
$ echo "to be...
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