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Hands-On Big Data Analytics with PySpark

You're reading from   Hands-On Big Data Analytics with PySpark Analyze large datasets and discover techniques for testing, immunizing, and parallelizing Spark jobs

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781838644130
Length 182 pages
Edition 1st Edition
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Authors (3):
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James Cross James Cross
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James Cross
Bartłomiej Potaczek Bartłomiej Potaczek
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Bartłomiej Potaczek
Rudy Lai Rudy Lai
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Rudy Lai
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Toc

Table of Contents (15) Chapters Close

Preface 1. Installing Pyspark and Setting up Your Development Environment FREE CHAPTER 2. Getting Your Big Data into the Spark Environment Using RDDs 3. Big Data Cleaning and Wrangling with Spark Notebooks 4. Aggregating and Summarizing Data into Useful Reports 5. Powerful Exploratory Data Analysis with MLlib 6. Putting Structure on Your Big Data with SparkSQL 7. Transformations and Actions 8. Immutable Design 9. Avoiding Shuffle and Reducing Operational Expenses 10. Saving Data in the Correct Format 11. Working with the Spark Key/Value API 12. Testing Apache Spark Jobs 13. Leveraging the Spark GraphX API 14. Other Books You May Enjoy

Calculating PageRank

In this section, we will load data about users and reload data about their followers. We will use the graph API and the structure of our data, and we will calculate PageRank to calculate the rank of users.

First, we need to load edgeListFile, as follows:

package com.tomekl007.chapter_7

import org.apache.spark.graphx.GraphLoader
import org.apache.spark.sql.SparkSession
import org.scalatest.FunSuite
import org.scalatest.Matchers._

class PageRankTest extends FunSuite {
private val sc = SparkSession.builder().master("local[2]").getOrCreate().sparkContext

test("should calculate page rank using GraphX API") {
//given
val graph = GraphLoader.edgeListFile(sc, getClass.getResource("/pagerank/followers.txt").getPath)

We have a followers.txt file; the following screenshot shows the format of the file, which is similar to the file we...

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