In computing, dataflow is a broad concept, which has various meanings depending on the application and context. In the context of software architecture, data flow relates to stream processing or reactive programming.
Dataflow computing is a software paradigm based on the idea of representing computations as a directed graph, where nodes are computations and data flow along the edges. Dataflow can also be called stream processing or reactive programming.
There have been multiple data-flow/stream processing languages of various forms (see Stream processing). Data-flow hardware (see Dataflow architecture) is an alternative to the classic von Neumann architecture. The most obvious example of data-flow programming is the subset known as reactive programming with spreadsheets. As a user enters new values, they are instantly transmitted to the next logical "actor" or formula for calculation.
Distributed data flows have also been proposed as a programming abstraction that captures the dynamics of distributed multi-protocols. The data-centric perspective characteristic of data flow programming promotes high-level functional specifications and simplifies formal reasoning about system components.
Dataflow architecture
Hardware architectures for dataflow was a major topic in computer architecture research in the 1970s and early 1980s. Jack Dennis of the Massachusetts Institute of Technology (MIT) pioneered the field of static dataflow architectures. Designs that use conventional memory addresses as data dependency tags are called static dataflow machines. These machines did not allow multiple instances of the same routines to be executed simultaneously because the simple tags could not differentiate between them. Designs that use content-addressable memory are called dynamic dataflow machines by Arvind. They use tags in memory to facilitate parallelism.
Data flows around the computer through the components of the computer. It gets entered from the input devices and can leave through output devices (printer etc.).
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In computer programming, dataflow programming is a programming paradigm that models a program as a directed graph of the data flowing between operations, thus implementing dataflow principles and architecture. Dataflow programming languages share some features of functional languages, and were generally developed in order to bring some functional concepts to a language more suitable for numeric processing. Some authors use the term datastream instead of dataflow to avoid confusion with dataflow computing or dataflow architecture, based on an indeterministic machine paradigm.
In computer science, stream processing (also known as event stream processing, data stream processing, or distributed stream processing) is a programming paradigm which views streams, or sequences of events in time, as the central input and output objects of computation. Stream processing encompasses dataflow programming, reactive programming, and distributed data processing. Stream processing systems aim to expose parallel processing for data streams and rely on streaming algorithms for efficient implementation.
Functional reactive programming (FRP) is a programming paradigm for reactive programming (asynchronous dataflow programming) using the building blocks of functional programming (e.g. map, reduce, filter). FRP has been used for programming graphical user interfaces (GUIs), robotics, games, and music, aiming to simplify these problems by explicitly modeling time. The original formulation of functional reactive programming can be found in the ICFP 97 paper Functional Reactive Animation by Conal Elliott and Paul Hudak.
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