By Jason Venner
You've heard the hype approximately Hadoop: it runs petabyte–scale info mining initiatives insanely speedy, it runs monstrous projects on clouds for absurdly reasonable, it's been seriously dedicated to by way of tech giants like IBM, Yahoo!, and the Apache undertaking, and it's thoroughly open-source (thus free). yet what precisely is it, and extra importantly, how do you even get a Hadoop cluster up and running?
From Apress, the identify you've come to belief for hands–on technical wisdom, professional Hadoop brings you up to the mark on Hadoop. You examine the bits and bobs of MapReduce; easy methods to constitution a cluster, layout, and enforce the Hadoop dossier process; and the way to construct your first cloud–computing initiatives utilizing Hadoop. the best way to allow Hadoop look after allotting and parallelizing your software—you simply specialise in the code, Hadoop looks after the rest.
Best of all, you'll research from a tech specialist who's been within the Hadoop scene given that day one. Written from the point of view of a significant engineer with down–in–the–trenches wisdom of what to do mistaken with Hadoop, you the best way to steer clear of the typical, pricey first error that everybody makes with growing their very own Hadoop approach or inheriting a person else's.
Skip the beginner level and the pricy, hard–to–fix mistakes...go directly to professional professional at the most well-liked cloud–computing framework with professional Hadoop. Your productiveness will blow your managers away.
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O]ilhaolanI]l9-, SnkpaejlqpbknI]l, SnkpaejlqpbknI]lThe framework has taken over at this point and sets up input splits (each fragment of input is called an input split) for the map tasks. The following line provides the job ID, which you could use to refer to this job with the job control tools: Nqjjejcfk^6fk^[hk_]h[,,,- The following lines let you know that there are two input files and two input splits: fri*FriIapne_o6Ejepe]hevejcFRIIapne_osepdlnk_aooJ]ia9Fk^Pn]_gan(oaooekjE`9 i]lna`*BehaEjlqpBkni]p6Pkp]hejlqpl]pdopklnk_aoo6.
All the examples in this chapter are based on the file I]lNa`q_aEjpnk*f]r], shown in Listing 2-1. The job created by the code in I]lNa`q_aEjpnk*f]r] will read all of its textual input line by line, and sort the lines based on that portion of the line before the first tab character. If there are no tab characters in the line, the sort will be based on the entire line. The I]lNa`q_aEjpnk*f]r] file is structured to provide a simple example of configuring and running a MapReduce job. C H A P T E R 2 N T H E B A S I C S O F A M A P R E D U C E J O B Listing 2-1.