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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

Streaming KMeans to classify data in near real-time

Spark streaming is a powerful facility lets you combine near real time and batch in the same paradigm. The streaming KMeans interface lives at the intersection of ML clustering and Spark streaming, and takes full advantage of the core facilities provided by Spark streaming itself (for example, fault tolerance, exactly once delivery semantics, and so on).

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.

Import the necessary packages for streaming KMeans:

package spark.ml.cookbook.chapter8.

Import the necessary packages for streaming KMeans:

import org.apache.log4j.{Level, Logger}
import org.apache.spark.mllib.clustering.StreamingKMeans
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.sql.SparkSession
import org.apache.spark.streaming.{Seconds, StreamingContext}