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Fast Data Processing with Spark 2

You're reading from   Fast Data Processing with Spark 2 Accelerate your data for rapid insight

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Product type Paperback
Published in Oct 2016
Publisher Packt
ISBN-13 9781785889271
Length 274 pages
Edition 3rd Edition
Languages
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Authors (2):
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Krishna Sankar Krishna Sankar
Author Profile Icon Krishna Sankar
Krishna Sankar
Holden Karau Holden Karau
Author Profile Icon Holden Karau
Holden Karau
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Toc

Table of Contents (13) Chapters Close

Preface 1. Installing Spark and Setting Up Your Cluster 2. Using the Spark Shell FREE CHAPTER 3. Building and Running a Spark Application 4. Creating a SparkSession Object 5. Loading and Saving Data in Spark 6. Manipulating Your RDD 7. Spark 2.0 Concepts 8. Spark SQL 9. Foundations of Datasets/DataFrames – The Proverbial Workhorse for DataScientists 10. Spark with Big Data 11. Machine Learning with Spark ML Pipelines 12. GraphX

Structural APIs


Armed with the knowledge of how to create a graph and the knowledge of how to navigate the APIs, let's focus on the structural APIs. The code is available in graph-03.scala, graph-04.scala, and graph-05.scala files:

You can get the number of edges and vertices by numEdges and numVertices.

graph.numEdges 
graph.numVertices 
// 
val vertices = graph.vertices 
vertices.collect.foreach(println) 
// 
val edges = graph.edges 
edges.collect.foreach(println) 
// 
val triplets = graph.triplets 
triplets.take(3) 
triplets.map(t=>t.toString).collect().foreach(println) 

The triplets are interesting. They actually encompass an edge and two vertices that the edge connects to and all the user-defined objects in one neat object. They are very useful when we want to write algorithms:

Let's look at the code. It is not that complex:

val inDeg = graph.inDegrees // Followers 
inDeg.collect() 
val outDeg = graph.outDegrees...
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