Data flow programming focuses on designing systems around data processing rather than code manipulation. It uses small modules called nodes to process data and pass it to other nodes. This is different from imperative programming, which focuses on explicit function calls. Data flow programming allows for unique data flows and can be used on distributed systems and parallel processors.
Data flow programming is a model used during software conceptualization and implementation. The goal of data flow programming is to center the design of a system around the data being processed versus the code used to manipulate the information. The result is a system in which basic computational functions are isolated in small modules known as nodes; these accept some data when a certain state is reached, process the data, and push the output back into program control flow, potentially passing the information to another node. This is in contrast to the normal imperative programming paradigm, where a straightforward list of commands defines the control flow of a program, not the state of the data. There are many uses for data-flow programs, including parallel processing, real-time systems, and embedded systems.
In implicit programming, which is the most commonly used type of computer programming language, programs are often constructed from flowcharts that contain a sequence of function or method calls, with each call branching off to other functions. This type of programming inherently focuses on the procedures used to manipulate program data. When dataflow programming is used, the focus is removed from explicit function calls and instead is focused on creating abstract forms that accept data when the data or program has met or met certain conditions. Now, instead of calling a function, the program’s design causes the data to flow to modules, or nodes, where it potentially enters a stream for processing by multiple nodes.
An abstract example of how data flow programming works can be seen when considering how to fill a glass with water from a tap. An imperative approach would be to generate functions to turn on the water, move the glass to the appropriate spot under the faucet, and then fill the glass with water. Through a dataflow programming example, the faucet instead waits for the cup to have been placed underneath it to begin filling it, and whatever is moving the cup waits until the cup has reached a certain state, such as full, to remove it from underneath the tap . The actual programming mechanisms that change the state of the data are not the immediate concern of the project.
The benefit of dataflow programming is an application or system where different nodes can be manipulated to create completely unique dataflows without necessarily requiring the relationship to be encrypted. Also, a program using dataflow programming is actively prepared to process the data at all times, rather than explicitly entering a state or model that blocks access or execution by one or more nodes. The design and concept of the nodes mean that dataflow programming applications can easily be designed for use on distributed systems and parallel processors.
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