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Spark Cookbook

You're reading from  Spark Cookbook

Product type Book
Published in Jul 2015
Publisher
ISBN-13 9781783987061
Pages 226 pages
Edition 1st Edition
Languages
Author (1):
Rishi Yadav Rishi Yadav
Profile icon Rishi Yadav

Table of Contents (19) Chapters

Spark Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started with Apache Spark 2. Developing Applications with Spark 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

Fundamental operations on graphs


In this recipe, we will learn how to create graphs and do basic operations on them.

Getting ready

As a starting example, we will have three vertices, each representing the city center of three cities in California—Santa Clara, Fremont, and San Francisco. The following is the distance between these cities:

Source

Destination

Distance (miles)

Santa Clara, CA

Fremont, CA

20

Fremont, CA

San Francisco, CA

44

San Francisco, CA

Santa Clara, CA

53

How to do it…

  1. Import the GraphX-related classes:

    scala> import org.apache.spark.graphx._
    scala> import org.apache.spark.rdd.RDD
    
  2. Load the vertex data in an array:

    scala> val vertices = Array((1L, ("Santa Clara","CA")),(2L, ("Fremont","CA")),(3L, ("San Francisco","CA")))
    
  3. Load the array of vertices into the RDD of vertices:

    scala> val vrdd = sc.parallelize(vertices)
    
  4. Load the edge data in an array:

    scala> val edges = Array(Edge(1L,2L,20),Edge(2L,3L,44),Edge(3L,1L,53))
    
  5. Load the data into the RDD of edges...

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