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MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel and distributed algorithm on a cluster. [1] [2] [3]A MapReduce program is composed of a map procedure, which performs filtering and sorting (such as sorting students by first name into queues, one queue for each name), and a reduce method, which performs a summary ...
Second-order Functions: PACT provides more second-order functions. Currently, five second-order functions called Input Contracts are supported. This set might be extended in the future. Program structure: PACT allows the composition of arbitrary acyclic data flow graphs. In contract, MapReduce programs have a static structure (Map -> Reduce).
Information flow of Reduce operation performed on three nodes. f is the associative operator and α is the result of the reduction. The reduce pattern [4] is used to collect data or partial results from different processing units and to combine them into a global result by a chosen operator.
The MapReduce architecture allows programmers to use a functional programming style to create a map function that processes a key–value pair associated with the input data to generate a set of intermediate key–value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Since the system ...
It is those results that transit on the network and are finally combined into one. In any application where the list is enormous and the result is a very simple type – say an integer – the benefits of parallelisation are considerable. This is the basis of the map-reduce approach.
Therefore, compilers will attempt to transform the first form into the second; this type of optimization is known as map fusion and is the functional analog of loop fusion. [2] Map functions can be and often are defined in terms of a fold such as foldr, which means one can do a map-fold fusion: foldr f z . map g is equivalent to foldr (f .
Some parallel programming systems, such as OpenMP and Cilk, have language support for the map pattern in the form of a parallel for loop; [2] languages such as OpenCL and CUDA support elemental functions (as "kernels") at the language level. The map pattern is typically combined with other parallel design patterns.
To define an augmented map, users need to specify the key type, the comparison function on the key type, the value type, the augmented value type, the base function, the combine function and the identity of the combine function. On top of the ordered map interface, PAM also supports functions for augmented maps, such as aug_range.