By Jason Venner

You've heard the hype approximately Hadoop: it runs petabyte–scale facts mining projects insanely quickly, it runs huge projects on clouds for absurdly reasonable, it's been seriously dedicated to through 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 on top of things on Hadoop. You study the fine details of MapReduce; the way to constitution a cluster, layout, and enforce the Hadoop dossier procedure; and the way to construct your first cloud–computing projects utilizing Hadoop. methods to permit 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 expert who's been within the Hadoop scene due to the fact day one. Written from the viewpoint of a critical engineer with down–in–the–trenches wisdom of what to do flawed with Hadoop, you find out how to stay away from the typical, pricey first mistakes that everybody makes with growing their very own Hadoop approach or inheriting somebody else's.

Skip the beginner degree and the pricy, hard–to–fix mistakes...go instantly to pro professional at the preferred cloud–computing framework with professional Hadoop. Your productiveness will blow your managers away.

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Na`q_a,! na`q_a,! heajp6Na`q_aejlqpcnkqlo9. heajp6I]lejlqpna_kn`o9. oa_kj`o Aopei]pa`r]hqakbLEeo/*4 NNote The Hadoop projects use the Apache Foundation’s log4j package for logging. By default, all output by the framework will have a leading date stamp, a log level, and the name of the class that emitted the message. In addition, the default is only to emit log messages of level EJBK or higher. For brevity, I’ve removed the data stamp and log level from the output reproduced in this book.

Heajp*nqjFk^$_kjb%7 hkccan*ejbk$Pdafk^d]o_kilhapa`*%7 eb$fk^*eoOq__aoobqh$%%w hkccan*annkn$Pdafk^b]eha`*%7 Ouopai*atep$-%7 y hkccan*ejbk$Pdafk^_kilhapa`oq__aoobqhhu*%7 Ouopai*atep$,%7 y_]p_d$bej]hEKAt_alpekja%w hkccan*annkn$Pdafk^d]ob]eha``qapk]jEKAt_alpekj(a%7 a*lnejpOp]_gPn]_a$%7 y y y Input Splitting For the framework to be able to distribute pieces of the job to multiple machines, it needs to fragment the input into individual pieces, which can in turn be provided as input to the individual distributed tasks.

The Parts of a Hadoop MapReduce Job The user configures and submits a MapReduce job (or just job for short) to the framework, which will decompose the job into a set of map tasks, shuffles, a sort, and a set of reduce tasks. The framework will then manage the distribution and execution of the tasks, collect the output, and report the status to the user. The job consists of the parts shown in Figure 2-1 and listed in Table 2-1. Table 2-1. Parts of a MapReduce Job Part Handled By Configuration of the job User Input splitting and distribution Hadoop framework Start of the individual map tasks with their input split Hadoop framework Map function, called once for each input key/value pair User Shuffle, which partitions and sorts the per-map output Hadoop framework Sort, which merge sorts the shuffle output for each partition of all map outputs Hadoop framework Start of the individual reduce tasks, with their input partition Hadoop framework Reduce function, which is called once for each unique input key, with all of the input values that share that key User Collection of the output and storage in the configured job output directory, in N parts, where N is the number of reduce tasks Hadoop framework 27 28 CH APT ER 2 N THE BA S IC S OF A MA PREDUC E J OB Provided by User Job Configuration Provided by Hadoop Framework Input Splitting & Distribution Input Format Input Locations Start of Individual Map Tasks Map Function Number of Reduce Tasks Shuffle, Partition/Sort per Map Output Reduce Function Output Key Type Output Value Type Merge Sort for Map Outputs for Each Reduce Task Start of Individual Reduce Tasks Output Format Output Location Collection of Final Output Figure 2-1.

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