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How does MapReduce scale algorithms for big data analytics?
Parallel and distributed computing is a natural first remedy to scale these algorithms to “Big algorithms” for large-scale data. Advances in many Big Data analytics algorithms are contributed by MapReduce, a programming paradigm that enables parallel and distributed execution of massive data processing on large clusters of machines.
How is MapReduce used in the real world?
MapReduce is a programming model and an associated implementation for processing and generating large datasets that is responsive to a broad variety of real-world tasks [9]. The MapReduce paradigm acquires the feature of parallel programming that provides simplicity.
How does a reduce function work in MapReduce?
Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model, as shown in the paper.
How is MapReduce used to process large data sets?
MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key.
Last Update: Oct 2021
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