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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 ...
Many-one reductions are often subjected to resource restrictions, for example that the reduction function is computable in polynomial time, logarithmic space, by or circuits, or polylogarithmic projections where each subsequent reduction notion is weaker than the prior; see polynomial-time reduction and log-space reduction for details.
Folds can be regarded as consistently replacing the structural components of a data structure with functions and values. Lists, for example, are built up in many functional languages from two primitives: any list is either an empty list, commonly called nil ([]), or is constructed by prefixing an element in front of another list, creating what is called a cons node ( Cons(X1,Cons(X2,Cons ...
For example, map combined with category reduction gives the MapReduce pattern. [3]: 106–107 ...
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 ...
[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 ...
An example is Spark where Java is the base language, and Spark is the programming model. Execution may be based on what appear to be library calls. Other examples include the POSIX Threads library and Hadoop's MapReduce. [1] In both cases, the execution model of the programming model is different from that of the base language in which the code ...
For example, reverse :: List a -> List a, which reverses a list, is a natural transformation, as is flattenInorder :: Tree a -> List a, which flattens a tree from left to right, and even sortBy :: (a -> a -> Bool) -> List a -> List a, which sorts a list based on a provided comparison function.