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How is mapreduce based on functional programming?


Asked by Mathias Gonzalez on Dec 07, 2021 FAQ



MapReduce is based on functional programming models largely from Lisp. Typically, the users will implement two functions: The Map function written by the user will receive an input pair of keys and values, and after the computation cycles, will produce a set of intermediate key-value pairs.
Consequently,
The Map and Reduce functions of MapReduce are both defined with respect to data structured in (key, value) pairs. Map takes one pair of data with a type in one data domain, and returns a list of pairs in a different domain: The Map function is applied in parallel to every pair (keyed by k1) in the input dataset.
Moreover, MapReduce is a software framework and programming model used for processing huge amounts of data. MapReduce program work in two phases, namely, Map and Reduce. Map tasks deal with splitting and mapping of data while Reduce tasks shuffle and reduce the data.
Thereof,
The MapReduce programming framework uses two tasks common in functional programming: Map and Reduce. MapReduce is a new parallel processing framework and Hadoop is its open-source implementation on a single computing node or on clusters.
Also Know,
Understanding MapReduce, from functional programming language to distributed system. MapReduce is a computing model for processing big data with a parallel, distributed algorithm on a cluster. It was invented by Google and largely used in the industry since 2004.