Reactive Imperative Programming with Dataflow Constraints

Abstract

Dataflow languages provide natural support for specifying constraints between objects in dynamic applications, where programs need to react efficiently to changes of their environment. Researchers have long investigated how to take advantage of dataflow constraints by embedding them into procedural languages. Previous mixed imperative/dataflow systems, however, require syntactic extensions or libraries of ad hoc data types for binding the imperative program to the dataflow solver. In this paper we propose a novel approach that smoothly combines the two paradigms without placing undue burden on the programmer.In our framework, programmers can define ordinary statements of the imperative host language that enforce constraints between objects stored in special memory locations designated as "reactive". Differently from previous approaches, reactive objects can be of any legal type in the host language, including primitive data types, pointers, arrays, and structures. Statements defining constraints are automatically re-executed every time their input memory locations change, letting a program behave like a spreadsheet where the values of some variables depend upon the values of other variables. The constraint solving mechanism is handled transparently by altering the semantics of elementary operations of the host language for reading and modifying objects. We provide a formal semantics and describe a concrete embodiment of our technique into C/C++, showing how to implement it efficiently in conventional platforms using off-the-shelf compilers. We discuss common coding idioms and relevant applications to reactive scenarios, including incremental computation, observer design pattern, and data structure repair. The performance of our implementation is compared to ad hoc problem-specific change propagation algorithms, as well as to language-centric apPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. OOPSLA'11, October 22-27, 2011, Portland, Oregon, USA. Copyright c 2011 proaches such as self-adjusting computation and subject/observer communication mechanisms, showing that the proposed approach is efficient in practice.

Distributed REScala

“…REScala [24] is an embedding of reactive programming in Scala and focuses on the integration of reactive abstractions into object-oriented applications [25]. Demetrescu et al [11] describe a runtime environment which natively supports reactive memory to implement dataflow constraints.…”

Functional programming for dynamic and large data with self-adjusting computation

“…One exception is the recent work of Demetrescu et al, which provides the programmer with techniques for writing incremental update functions in (imperative) reactive programs [24]. Another exception is Donham's Froc [25], which provides support for FRP based on a data-driven implementation using self-adjusting computation.…”

i3QL

“…The Garnet and Amulet [30] graphical toolkits support automatic constraint resolution to relieve the programmer from manual updates of graphical interfaces. Demetrescu et al [9] implemented dataflow constraints based on a runtime environment which natively supports reactive memory.…”