MV
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The continuous shift of various industries towards internet-based services have caused an exponential growth in the amount of data produced over the past few years. On top of this, the increasing need for real-time analytics and the increase in data velocity have made asynchronous, event-driven applications the norm. In this context, the reactive programming paradigm has gained much traction as it focuses on the propagation of change and composing/transforming streams of data. The industry standard reactive programming library for the JVM, .NET and Javascript ecosystems is the Reactive Extensions (Rx) library.
However, despite being well equipped to deal with asynchronous data, it does not offer any way of scaling the computation on multiple machines. In this thesis, we attempt to lay the groundwork for a scalable Rx library by implementing infrastructure and operators for remote execution of Rx streams. ...
However, despite being well equipped to deal with asynchronous data, it does not offer any way of scaling the computation on multiple machines. In this thesis, we attempt to lay the groundwork for a scalable Rx library by implementing infrastructure and operators for remote execution of Rx streams. ...
The continuous shift of various industries towards internet-based services have caused an exponential growth in the amount of data produced over the past few years. On top of this, the increasing need for real-time analytics and the increase in data velocity have made asynchronous, event-driven applications the norm. In this context, the reactive programming paradigm has gained much traction as it focuses on the propagation of change and composing/transforming streams of data. The industry standard reactive programming library for the JVM, .NET and Javascript ecosystems is the Reactive Extensions (Rx) library.
However, despite being well equipped to deal with asynchronous data, it does not offer any way of scaling the computation on multiple machines. In this thesis, we attempt to lay the groundwork for a scalable Rx library by implementing infrastructure and operators for remote execution of Rx streams.
However, despite being well equipped to deal with asynchronous data, it does not offer any way of scaling the computation on multiple machines. In this thesis, we attempt to lay the groundwork for a scalable Rx library by implementing infrastructure and operators for remote execution of Rx streams.