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Spark Cookbook

You're reading from   Spark Cookbook With over 60 recipes on Spark, covering Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX libraries this is the perfect Spark book to always have by your side

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Product type Paperback
Published in Jul 2015
Publisher
ISBN-13 9781783987061
Length 226 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 (14) Chapters Close

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

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.

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.

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
    
  2. Get into the words directory:

    $ cd words
    
  3. Create the sh.txt text file and enter "to be or not to be" in it:

    $ echo "to be or not to be" > sh.txt
    
  4. Start the Spark shell:

    $ spark-shell
    
  5. Load the words directory as RDD:

    scala> val words...
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