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In computing, reactive programming is a declarative programming paradigm concerned with data streams and the propagation of change. With this paradigm, it is possible to express static (e.g., arrays) or dynamic (e.g., event emitters) data streams with ease, and also communicate that an inferred dependency within the associated execution model exists, which facilitates the automatic propagation ...
ReactiveX (Rx, also known as Reactive Extensions) is a software library originally created by Microsoft that allows imperative programming languages to operate on sequences of data regardless of whether the data is synchronous or asynchronous. It provides a set of sequence operators that operate on each item in the sequence.
F# added asynchronous workflows with await points in version 2.0 in 2007. [5] This influenced the async/await mechanism added to C#. [6] Microsoft first released a version of C# with async/await in the Async CTP (2011). It was later officially released in C# 5 (2012). [7] [1]: 10 Haskell lead developer Simon Marlow created the async package in ...
Python — uses thread-based parallelism and process-based parallelism [17] Raku includes classes for threads, promises and channels by default [18] Reia—uses asynchronous message passing between shared-nothing objects; Red/System—for system programming, based on Rebol
Kotlin, however kotlin.native.concurrent.Future is only usually used when writing Kotlin that is intended to run natively [35] Nim; Oxygene; Oz version 3 [36] Python concurrent.futures, since 3.2, [37] as proposed by the PEP 3148, and Python 3.5 added async and await [38] R (promises for lazy evaluation, still single threaded) Racket [39] Raku [40]
A reactor object provides an asynchronous interface to resources that must be handled synchronously. Yes — Read-write lock: Allows concurrent read access to an object, but requires exclusive access for write operations. An underlying semaphore might be used for writing, and a Copy-on-write mechanism may or may not be used. No — Scheduler
Flow-based programming defines applications using the metaphor of a "data factory". It views an application not as a single, sequential process, which starts at a point in time, and then does one thing at a time until it is finished, but as a network of asynchronous processes communicating by means of streams of structured data chunks, called "information packets" (IPs).
Metaprogramming – writing programs that write or manipulate other programs (or themselves) as their data, or that do part of the work at compile time that would otherwise be done at runtime Template metaprogramming – metaprogramming methods in which a compiler uses templates to generate temporary source code, which is merged by the compiler ...