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MapReduce is a programming process that processes and generates large data sets that include a distributed algorithm on a cluster. A program that uses MapReduce will have a filtering and sorting integration as well as a reduce method that will summarise the information.
The system of MapReduce arranges the marshalling of distributed servers and runs various tasks that are concurrent whilst data transfers and communication continue to take place.
MapReduce has been written into a range of programming languages and the process is particularly popular in open source implementations.
Steps that take place in the MapReduce process are outlined below:
Map step - each worker node applies the map function to data and writes the output to temporary storage. Shuffle step - data belonging to one key is sent to the same worker node. Reduce step - each group of data is processed in a parallel format.
The data is usually running in sequence in the MapReduce process and data can be shared among different servers throughout the process.
The main benefit of the MapReduce process is to exploit the optimized shuffle operation of the platform and reduce the data written to the disk. MapReduce is a fairly technical programming process and is managed by experienced developers with a deep understanding of technology.
Businesses or individuals may use the MapReduce process for a range of reasons including:
Distributed pattern-based searching Distributed sorting Web link-graph reversal Web access log stats Inverted index construction Document clustering Machine learning Desktop grids Volunteer computing environments Mobile environments
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