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Google Cloud Dataflow was announced in June, 2014 [3] and released to the general public as an open beta in April, 2015. [4] In January, 2016 Google donated the underlying SDK, the implementation of a local runner, and a set of IOs (data connectors) to access Google Cloud Platform data services to the Apache Software Foundation. [5]
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. [1] Dataflow can also be called stream processing or reactive programming. [2] There have been multiple data-flow/stream processing languages of various forms (see Stream ...
In a 1966 Ph.D. thesis, The On-line Graphical Specification of Computer Procedures, [10] Bert Sutherland created one of the first graphical dataflow programming frameworks in order to make parallel programming easier. Subsequent dataflow languages were often developed at the large supercomputer labs.
Dataflow architecture is a dataflow-based computer architecture that directly contrasts the traditional von Neumann architecture or control flow architecture. Dataflow architectures have no program counter, in concept: the executability and execution of instructions is solely determined based on the availability of input arguments to the instructions, [1] so that the order of instruction ...
By way of illustration, the following code fragments demonstrate detection of patterns within event streams. The first is an example of processing a data stream using a continuous SQL query (a query that executes forever processing arriving data based on timestamps and window duration).
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Dataflow computers execute programs according to the "dataflow firing rule", which stipulates that an instruction may execute at any time after its operands are available. Due to the isolation of data, similar to EDGE, dataflow languages are inherently parallel, and interest in them followed the more general interest in massive parallelism as a ...
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