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Hands-On Big Data Analytics with PySpark

You're reading from  Hands-On Big Data Analytics with PySpark

Product type Book
Published in Mar 2019
Publisher Packt
ISBN-13 9781838644130
Pages 182 pages
Edition 1st Edition
Languages
Concepts
Authors (2):
Rudy Lai Rudy Lai
Profile icon Rudy Lai
Bartłomiej Potaczek Bartłomiej Potaczek
Profile icon Bartłomiej Potaczek
View More author details

Table of Contents (15) Chapters

Preface 1. Installing Pyspark and Setting up Your Development Environment 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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