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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

Exploring the Spark shell


Spark comes bundled with a read–eval–print loop (REPL) shell, which is a wrapper around the Scala shell. Though the Spark shell looks like a command line for simple things, in reality, a lot of complex queries can also be executed using it. A lot of times, the Spark shell is used in the initial development phase and once the code is stabilized, it is written as a class file and bundled as a jar to be run using spark-submit flag. This chapter explores different development environments in which Spark applications can be developed.

How to do it...

Hadoop MapReduce's word count, which takes at least three class files and one configuration file, namely project object model (POM), becomes very simple with the Spark shell. In this recipe, we are going to create a simple one-line text file, upload it to the Hadoop distributed file system (HDFS), and use Spark to count the occurrences of words. Let's see how:

  1. Create the words directory using the following command:
$ mkdir words...
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