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ABSTRACT:
MapReduce is a software framework that allows developers to write programs that process massive amounts of unstructured data in parallel across a distributed cluster of processors or stand-alone computers. It was developed at Google in 2004. In the programming model, a user specifies the computation by two functions, Map and Reduce. The MapReduce as well as its open-source Hadoop, is aimed for parallelizing computing in large clusters of commodity machines. Other implementations for different environments have been introduced as well, such as Mars, which implements MapReduce for graphics processors, and Phoenix, the MapReduce implementation for shared-memory systems.
This paper provides an overview of MapReduce programming model, its various applications and different implementations of MapReduce. GridGain is another open source java implementation of mapreduce. We also discuss comparisons of Hadoop and GridGain.
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