Hadoop Mapreduce

Hadoop Mapreduce Introduction:

Hadoop MapReduce is a framework using which we can write applications to process huge amounts of data, in parallel, on large clusters of commodity hardware in a reliable manner.MapReduce is a processing technique and a program model for distributed computing based on java. The MapReduce algorithm contains two important tasks, namely Map and Reduce.

Overview Of Hadoop Mapreduce Job Suppport:

The MapReduce is an programming model for writing  the applications that can process Big Data in an parallel on multiple nodes. The MapReduce provides analytical capabilities for analyzing the huge volumes of the complex data.Hadoop MapReduce is an software framework for easily writing applications which process vast amounts of data , in-parallel on large clusters  of commodity hardware in an reliable, fault-tolerant manner.

The Hadoop MapReduce includes many computers but little communication stragglers & the failures. In functional programming concepts the  MapReduce programs are designed to evaluate the bulk volume of data in an parallel fashion. In this concept needs to divides the work across an large number of machines. This type of model would not scale large clusters like hundreds or thousands of nodes if the components needs to keep the data or the  information on the nodes synchronized at all times would prevent the system from performing the  reliability or efficiently at large scale. In the MapReduce all the data elements cannot be  update.

Which means  in MapReduce all the data elements are immutable. In an mapping task we can change the input (key,value) pair & it does not reflected in the input file &the communication happens only by generating the new output (key,value) pairs which are forwarded by the Hadoop system into the next phase of the  execution.

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