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What is Mapreduce ?
MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel, distributed algorithm on a cluster. A MapReduce program is composed of a map procedure, which performs filtering and sorting, and a reduce method, which performs a summary operation. The "MapReduce System" orchestrates the processing by marshaling the distributed servers, running the various tasks in parallel, managing all communications and data transfers between the various parts of the system, and providing for redundancy and Fault tolerance.
A MapReduce framework is usually composed of three operations :
Mapper
Combiner
Reducer
Mapper : The map functions to the local data, and writes the output to a temporary storage. A master node ensures that only one copy of the redundant input data is processed. Mapper maps the input key/value pairs to a set of intermediate key/value pairs.
Combiner : Combiner is the between the mapper and reduces the amount of data that is transferred between them. The output of the map task is usually huge, and the amount of data passed to the reduced task is also considerable.
Reducer : worker nodes now process each group of output data, per key, in parallel.
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