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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 .
An internal iterator is a higher order function (often taking anonymous functions) that traverses a collection while applying a function to each element. For example, Python's map function applies a caller-defined function to each element:
Each node in the map space is associated with a "weight" vector, which is the position of the node in the input space. While nodes in the map space stay fixed, training consists in moving weight vectors toward the input data (reducing a distance metric such as Euclidean distance) without spoiling the topology induced from the map space. After ...
In the Common Lisp dialect, destructive functions are commonplace; the equivalent of set-car! is named rplaca for "replace car". This function is rarely seen, however, as Common Lisp includes a special facility, setf, to make it easier to define and use destructive functions. A frequent style in Common Lisp is to write code functionally ...
Locator/ID Separation Protocol (LISP) (RFC 6830) is a "map-and-encapsulate" protocol which is developed by the Internet Engineering Task Force LISP Working Group. [1] The basic idea behind the separation is that the Internet architecture combines two functions, routing locators (where a client is attached to the network) and identifiers (who ...
The following Cuneiform program creates three parallel applications of the function f by mapping f over a three-element list: let xs : [File] = ['a.txt', 'b.txt', 'c.txt' : File]; for x <- xs do f( x = x ) : File end; Similarly, the applications of f and g are independent in the construction of the record r and can, thus, be run in parallel:
String functions common to many languages are listed below, including the different names used. The below list of common functions aims to help programmers find the equivalent function in a language. Note, string concatenation and regular expressions are handled in separate pages. Statements in guillemets (« … ») are optional.