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Parallel R by Stephen Weston, Q. Ethan McCallum

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Chapter 5. A Primer on MapReduce and Hadoop

Hadoop is an open-source framework for large-scale data storage and distributed computing, built on the MapReduce model. Doug Cutting initially created Hadoop as a component of the Nutch web crawler. It became its own project in 2006, and graduated to a top-level Apache project in 2008. During this time, Hadoop has experienced widespread adoption.

One of Hadoop’s strengths is that it is a general framework, applicable to a variety of domains and programming languages. One use case, and the common thread of the book’s remaining chapters, is to drive large R jobs.

This chapter explains some basics of MapReduce and Hadoop. It may feel a little out of place, as it’s not specific to R; but the content is too important to hide in an appendix.

Have no fear: I don’t dive into deep details here. There is a lot more to MapReduce and Hadoop than I could possibly cover in this book, let alone a chapter. I’ll provide just enough guidance to set you on your way. For a more thorough exploration I encourage you to read the Google MapReduce paper mentioned in , as well as Hadoop: The Definitive Guide by Tom White (O’Reilly).

If you already have a grasp on MapReduce and Hadoop, feel free to skip to the next chapter.

Hadoop at Cruising Altitude

When people think “Apache Hadoop,”[43] they often think about churning through terabytes of input across clusters made of tens or hundreds of machines, or nodes. Logfile processing is such an oft-cited use case, in fact, ...

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