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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 ...
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 .
[2] [3] [4] The reduction of sets of elements is an integral part of programming models such as Map Reduce, where a reduction operator is applied to all elements before they are reduced. Other parallel algorithms use reduction operators as primary operations to solve more complex problems. Many reduction operators can be used for broadcasting ...
Pig Latin abstracts the programming from the Java MapReduce idiom into a notation which makes MapReduce programming high level, similar to that of SQL for relational database management systems. Pig Latin can be extended using user-defined functions (UDFs) which the user can write in Java , Python , JavaScript , Ruby or Groovy [ 3 ] and then ...
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.
Program structure: PACT allows the composition of arbitrary acyclic data flow graphs. In contract, MapReduce programs have a static structure (Map -> Reduce). Data Model: PACT's data model are records of arbitrary many fields of arbitrary types. MapReduce's KeyValue-Pairs can be considered as records with two fields.
The colon comes from a general Scala syntax mechanism whereby the apparent infix operator is invoked as a method on the left operand with the right operand passed as an argument, or vice versa if the operator's last character is a colon, here applied symmetrically. Scala also features the tree-like folds using the method list.fold(z)(op). [11]
Collective operations are building blocks for interaction patterns, that are often used in SPMD algorithms in the parallel programming context. Hence, there is an interest in efficient realizations of these operations. A realization of the collective operations is provided by the Message Passing Interface [1] (MPI).