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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.
Membership of the organization is made up of users of the data model; these are mainly pipeline operators and government agencies. Over the last 25 years, the PODS data model has been implemented by over 200 pipeline operators in 36 countries, representing over 3 million miles pipeline and systems including facilities, storage and stations.
Such functions are called decomposable aggregation functions [4] or decomposable aggregate functions. The simplest may be referred to as self-decomposable aggregation functions , which are defined as those functions f such that there is a merge operator ⋄ {\displaystyle \diamond } such that
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 ...
In computer networking, link aggregation is the combining (aggregating) of multiple network connections in parallel by any of several methods. Link aggregation increases total throughput beyond what a single connection could sustain, and provides redundancy where all but one of the physical links may fail without losing connectivity.
In computing, a pipeline or data pipeline [1] is a set of data processing elements connected in series, where the output of one element is the input of the next one. The elements of a pipeline are often executed in parallel or in time-sliced fashion. Some amount of buffer storage is often inserted between elements. Computer-related pipelines ...
In software engineering, a pipeline consists of a chain of processing elements (processes, threads, coroutines, functions, etc.), arranged so that the output of each element is the input of the next. The concept is analogous to a physical pipeline .
Some APL interpreters support the compose operator ∘ and the commute operator ⍨. The former ∘ glues functions together so that foo∘bar , for example, could be a hypothetical function that applies defined function foo to the result of defined function bar ; foo and bar can represent any existing function.