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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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Toc

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

Performing neighborhood aggregation

GraphX does most of the computation by isolating each vertex and its neighbors. It makes it easier to process the massive graph data on distributed systems. This makes the neighborhood operations very important. GraphX has a mechanism to do it at each neighborhood level in the form of the aggregateMessages method. It does it in two steps:

  1. In the first step (the first function of the method), messages are sent to the destination vertex or source vertex (similar to the Map function in MapReduce).
  2. In the second step (the second function of the method), aggregation is done on these messages (similar to the Reduce function in MapReduce).

Getting ready

Let's build a small dataset of the followers:

Follower Followee
John Barack...
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