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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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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

Understanding GraphFrames


As everything in the Spark world has moved to DataFrames, it is natural to wonder how GraphX is still RDD based. This is where GraphFrames comes into the picture. GraphFrames is still not directly included in the Spark library and is being developed separately as a Spark package. It is just a matter of time before it is considered stable enough to be included in the main API.

In this recipe, we will understand GraphFrames. The GraphFrames has two primary DataFrames:

  • The vertices DataFrame, which needs to have a mandatory column called id
    • The edges DataFrame, which needs to have two mandatory columns, src and dst

Besides these requirements, both the vertices and edges DataFrames can have any arbitrary number and type of columns to represent attributes.

How to do it...

To get started with the recipe, we first need to perform the following steps: 

  1. Start spark-shell with the graphframes package:
        $ spark-shell --packages graphframes:graphframes:0.2.0-
          spark2...
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