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Categories: google Hadoop MapR

I’ve been involved with cluster computing ever since #DEC introduced #VAXcluster in 1984. In those days, a three node VAXcluster cost about $1 million. Today you can build a much more powerful cluster for under $1,000, including much more storage than anyone could afford back then. @Hadoop is the open-source version of @Google ‘s #MapReduce and #GoogleFileSystem ( #GFS ), widely used for large data-crunching applications. It is a shared-nothing cluster, which means that as you add cluster nodes, performance scales up smoothly.

In the paper, Performance of a Low Cost Hadoop Cluster for Image Analysis, researchers Basit Qureshia, Yasir Javeda, Anis Kouba, Mohamed-Foued Sritic, and Maram Alajlan, built a 20 node RPi Model 2 cluster, brought up Hadoop on it, and used it for surveillance drone image analysis. They also benchmarked the RPi cluster against a 4-node PC cluster based on 3GHz Intel i7 CPUs, each with 4GB of RAM.

CONFIGURATION

The 20 node cluster was divided into four, 5-node subnets, each attached to 16 port switches that are, in turn, networked to a managed 24 port core switch. The extra switch ports enable easy cluster expansion.

Each 700MHz RPi B runs Raspbian, an ARM-optimized version of Debian Linux. Each RPi has a Class 10, 16 GB SD card capable of up to 80MB/s read/write speeds. An image of the OS with Hadoop 2.6.2 was copied onto the SD cards. The Hadoop Master node, which implements the name-node only, was installed on a PC running Ubuntu 14.4 and Hadoop.

In the paper, Performance of a Low Cost Hadoop Cluster for Image Analysis, researchers Basit Qureshia, Yasir Javeda, Anis Kouba, Mohamed-Foued Sritic, and Maram Alajlan, built a 20 node RPi Model 2 cluster, brought up Hadoop on it, and used it for surveillance drone image analysis. They also benchmarked the RPi cluster against a 4-node PC cluster based on 3GHz Intel i7 CPUs, each with 4GB of RAM.

http://www.zdnet.com/article/running-hadoop-on-a-raspberry-pi-2-cluster/

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