Book Image

Raspberry Pi Super Cluster

By : Andrew K. Dennis
Book Image

Raspberry Pi Super Cluster

By: Andrew K. Dennis

Overview of this book

A cluster is a type of parallel/distributed processing system which consists of a collection of interconnected stand-alone computers cooperatively working together. Using Raspberry Pi computers, you can build a two-node parallel computing cluster which enhances performance and availability. This practical, example-oriented guide will teach you how to set up the hardware and operating systems of multiple Raspberry Pi computers to create your own cluster. It will then navigate you through how to install the necessary software to write your own programs such as Hadoop and MPICH before moving on to cover topics such as MapReduce. Throughout this book, you will explore the technology with the help of practical examples and tutorials to help you learn quickly and efficiently. Starting from a pile of hardware, with this book, you will be guided through exciting tutorials that will help you turn your hardware into your own super-computing cluster. You'll start out by learning how to set up your Raspberry Pi cluster's hardware. Following this, you will be taken through how to install the operating system, and you will also be given a taste of what parallel computing is about. With your Raspberry Pi cluster successfully set up, you will then install software such as MPI and Hadoop. Having reviewed some examples and written some programs that explore these two technologies, you will then wrap up with some fun ancillary projects. Finally, you will be provided with useful links to help take your projects to the next step.
Table of Contents (15 chapters)
Raspberry Pi Super Cluster
About the Author
About the Reviewers

The WordCount MapReduce program

The following code is a typical example of a MapReduce program for Hadoop. Its purpose is to take a number of input files and return a count of each word located in them.

We will be running this application in order to illustrate how the files we added to the FS can be processed.

Add the preceding code into a new file called created in vim /home/pi/hadoop/apps/

package org.myorg;
import java.util.*;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.conf.*;
import org.apache.hadoop.mapred.*;
import org.apache.hadoop.util.*;

The preceding code includes the various libraries we will need to use in our application. After this let's add the WordCount class:

public class WordCount {

  public static class Map extends MapReduceBase implements
  Mapper<LongWritable, Text, Text, IntWritable> {
    private final static IntWritable one = new IntWritable(1);