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asked SRVMTrainings December 13, 2012 06:27 AM  

what is MAP REDUCE?


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Map Reduce is a set of programs used to access and manipulate large data sets over a Hadoop cluster. 
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MapReduce is linearly scalable programming model. The programmer writes two functions a map function and a reduce function each of which defines a mapping from one set of key-value pairs to another.

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MapReduce is a programming model designed for processing large volumes of data in parallel by dividing the work into a set of independent tasks. MapReduce programs are written in a particular style influenced by functional programming constructs, specifically idioms for processing lists of data. This module explains the nature of this programming model and how it can be used to write programs which run in the Hadoop environment.
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answered By Jesh   0  

MapReduce is a programming model for processing large data sets, and the name of an implementation of the model by Google. MapReduce is typically used to do distributed computing on clusters of computers.
The model is inspired by the map and reduce functions commonly used in functional programming, although their purpose in the MapReduce framework is not the same as their original forms.
MapReduce libraries have been written in many programming languages. A popular free implementation is Apache Hadoop.

MapReduce is a framework for processing embarrassingly parallel problems across huge datasets using a large number of computers (nodes), collectively referred to as a cluster (if all nodes are on the same local network and use similar hardware) or a grid (if the nodes are shared across geographically and administratively distributed systems, and use more heterogenous hardware). Computational processing can occur on data stored either in a filesystem (unstructured) or in a database (structured). MapReduce can take advantage of locality of data, processing data on or near the storage assets to decrease transmission of data.

"Map" step: The master node takes the input, divides it into smaller sub-problems, and distributes them to worker nodes. A worker node may do this again in turn, leading to a multi-level tree structure. The worker node processes the smaller problem, and passes the answer back to its master node.

"Reduce" step: The master node then collects the answers to all the sub-problems and combines them in some way to form the output – the answer to the problem it was originally trying to solve.


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answered By hadooptrainings   0  

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