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You're reading from Apache Spark 2.x Machine Learning Cookbook Over 100 recipes to simplify machine learning model implementations with Spark

Product type Paperback

Published in Sep 2017

Publisher Packt

ISBN-13 9781783551606

Length 666 pages

Edition 1st Edition

Languages

Scala

Tools

Apache Spark

Concepts

Machine Learning

Authors (5):

Broderick Hall

Meenakshi Rajendran

Shuen Mei

Mohammed Guller

Siamak Amirghodsi

+1 more

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Table of Contents (14) Chapters

Preface

1. Practical Machine Learning with Spark Using Scala FREE CHAPTER

2. Just Enough Linear Algebra for Machine Learning with Spark

3. Spark's Three Data Musketeers for Machine Learning - Perfect Together

4. Common Recipes for Implementing a Robust Machine Learning System

5. Practical Machine Learning with Regression and Classification in Spark 2.0 - Part I

6. Practical Machine Learning with Regression and Classification in Spark 2.0 - Part II

7. Recommendation Engine that Scales with Spark

8. Unsupervised Clustering with Apache Spark 2.0

9. Optimization - Going Down the Hill with Gradient Descent

10. Building Machine Learning Systems with Decision Tree and Ensemble Models

11. Curse of High-Dimensionality in Big Data

12. Implementing Text Analytics with Spark 2.0 ML Library

13. Spark Streaming and Machine Learning Library

Creating RDDs with Spark 2.0 using internal data sources

There are four ways to create RDDs in Spark. They range from the parallelize() method for simple testing and debugging within the client driver code to streaming for near-realtime responses. In this recipe, we provide you with several examples to demonstrate RDD creation using sources. The streaming case will be covered in the streaming Spark example in Chapter 13, Streaming Machine Learning System, so we can address it in a meaningful way.

How to do it...

Start a new project in IntelliJ or in an IDE of your choice. Make sure the necessary JAR files are included.

Set up the package location where the program will reside:

package spark.ml.cookbook.chapter3

Import the necessary packages:

import breeze.numerics.pow 
import org.apache.spark.sql.SparkSession 
import Array._

Import the packages for setting up logging level for log4j. This step is optional, but we highly recommend it (change the level appropriately as you move through the development...