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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 ...
The map function originated in functional programming languages. The language Lisp introduced a map function called maplist [3] in 1959, with slightly different versions already appearing in 1958. [4] This is the original definition for maplist, mapping a function over successive rest lists:
It has been pointed out that the ordinary FRP model, from inputs to outputs, is poorly suited to interactive programs. [9] Lacking the ability to "run" programs within a mapping from inputs to outputs may mean one of the following solutions must be used: Create a data structure of actions which appear as the outputs.
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 ...
the time needed for N plus the time needed for the reduction; the maximum of the space needed for N and the space needed for the reduction; We say that a class C of languages (or a subset of the power set of the natural numbers) is closed under many-one reducibility if there exists no reduction from a language outside C to a language in C.
A traditional imperative program might use a loop to traverse and modify a list. A functional program, on the other hand, would probably use a higher-order "map" function that takes a function and a list, generating and returning a new list by applying the function to each list item.
MongoDB provides three ways to perform aggregation: the aggregation pipeline, the map-reduce function and single-purpose aggregation methods. [40] Map-reduce can be used for batch processing of data and aggregation operations. However, according to MongoDB's documentation, the aggregation pipeline provides better performance for most ...
All-reduce can be interpreted as a reduce operation with a subsequent broadcast (§ Broadcast). For long messages a corresponding implementation is suitable, whereas for short messages, the latency can be reduced by using a hypercube ( Hypercube (communication pattern) § All-Gather/ All-Reduce ) topology, if p {\displaystyle p} is a power of two.