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

MapReduce in Hadoop

In order to use Hadoop to run our MapReduce applications, there are key terminologies used in the technology we should understand.

These are briefly described as follows:

  • NameNode: The NameNode is responsible for keeping the directory tree of all the files stored in the system and tracking where the file data is stored in the cluster.

  • JobTracker: The JobTracker passes out MapReduce tasks to each Raspberry Pi in our cluster.

  • DataNode: The DataNodes in our cluster use the HDFS to store replicated data.

  • TaskTracker: A node in our Raspberry Pi cluster that accepts tasks.

  • Default configuration: A default configuration file provides the base that the website-specific files overwrite/augment. An example is the core-default.xml file.

  • Site-specific configuration: You will be familiar with this from the previous chapter. This configuration contains specifics about our own development environment, such as our Raspberry Pi's IP address.

A comprehensive guide to Hadoop's terms and...