我读过维基百科上关于响应式编程的文章。我还读过一篇关于函数式响应式编程的小文章。这些描述相当抽象。
函数式响应式编程(FRP)在实践中意味着什么? 反应式编程(相对于非反应式编程?)由什么组成?
我的背景是命令式/OO语言,所以与此范例相关的解释将受到赞赏。
我读过维基百科上关于响应式编程的文章。我还读过一篇关于函数式响应式编程的小文章。这些描述相当抽象。
函数式响应式编程(FRP)在实践中意味着什么? 反应式编程(相对于非反应式编程?)由什么组成?
我的背景是命令式/OO语言,所以与此范例相关的解释将受到赞赏。
当前回答
有一种简单的方法可以直观地了解它是什么样子的,那就是把你的程序想象成一个电子表格,所有的变量都是单元格。如果电子表格中的任何单元格发生变化,则引用该单元格的任何单元格也会发生变化。玻璃钢也是一样。现在想象一下,一些单元格会自己改变(或者更确切地说,是从外部世界中获取的):在GUI情况下,鼠标的位置就是一个很好的例子。
这必然会错过很多东西。当你实际使用FRP系统时,这个比喻很快就被打破了。首先,通常也会尝试建模离散事件(例如鼠标被点击)。我把这个放在这里只是为了让你们了解它是什么样的。
其他回答
如果你想感受一下FRP,你可以从1998年的Fran教程开始,它有动画插图。对于论文,从函数反应动画开始,然后在我的主页上的出版物链接和Haskell wiki上的FRP链接上跟踪链接。
就我个人而言,我喜欢在讨论如何实施FRP之前思考它意味着什么。 (没有规范的代码是没有问题的答案,因此“甚至没有错”。) 因此,我没有像Thomas K在另一个答案(图、节点、边、触发、执行等)中那样用表示/实现术语描述FRP。 有许多可能的实现风格,但没有一种实现说明FRP是什么。
I do resonate with Laurence G's simple description that FRP is about "datatypes that represent a value 'over time' ". Conventional imperative programming captures these dynamic values only indirectly, through state and mutations. The complete history (past, present, future) has no first class representation. Moreover, only discretely evolving values can be (indirectly) captured, since the imperative paradigm is temporally discrete. In contrast, FRP captures these evolving values directly and has no difficulty with continuously evolving values.
FRP is also unusual in that it is concurrent without running afoul of the theoretical & pragmatic rats' nest that plagues imperative concurrency. Semantically, FRP's concurrency is fine-grained, determinate, and continuous. (I'm talking about meaning, not implementation. An implementation may or may not involve concurrency or parallelism.) Semantic determinacy is very important for reasoning, both rigorous and informal. While concurrency adds enormous complexity to imperative programming (due to nondeterministic interleaving), it is effortless in FRP.
那么,什么是FRP? 你可以自己发明的。 从这些想法开始:
Dynamic/evolving values (i.e., values "over time") are first class values in themselves. You can define them and combine them, pass them into & out of functions. I called these things "behaviors". Behaviors are built up out of a few primitives, like constant (static) behaviors and time (like a clock), and then with sequential and parallel combination. n behaviors are combined by applying an n-ary function (on static values), "point-wise", i.e., continuously over time. To account for discrete phenomena, have another type (family) of "events", each of which has a stream (finite or infinite) of occurrences. Each occurrence has an associated time and value. To come up with the compositional vocabulary out of which all behaviors and events can be built, play with some examples. Keep deconstructing into pieces that are more general/simple. So that you know you're on solid ground, give the whole model a compositional foundation, using the technique of denotational semantics, which just means that (a) each type has a corresponding simple & precise mathematical type of "meanings", and (b) each primitive and operator has a simple & precise meaning as a function of the meanings of the constituents. Never, ever mix implementation considerations into your exploration process. If this description is gibberish to you, consult (a) Denotational design with type class morphisms, (b) Push-pull functional reactive programming (ignoring the implementation bits), and (c) the Denotational Semantics Haskell wikibooks page. Beware that denotational semantics has two parts, from its two founders Christopher Strachey and Dana Scott: the easier & more useful Strachey part and the harder and less useful (for software design) Scott part.
如果你坚持这些原则,我希望你能得到或多或少符合FRP精神的东西。
Where did I get these principles? In software design, I always ask the same question: "what does it mean?". Denotational semantics gave me a precise framework for this question, and one that fits my aesthetics (unlike operational or axiomatic semantics, both of which leave me unsatisfied). So I asked myself what is behavior? I soon realized that the temporally discrete nature of imperative computation is an accommodation to a particular style of machine, rather than a natural description of behavior itself. The simplest precise description of behavior I can think of is simply "function of (continuous) time", so that's my model. Delightfully, this model handles continuous, deterministic concurrency with ease and grace.
正确有效地实现这个模型是一个相当大的挑战,但那是另一个故事了。
对我来说,这是关于符号的2个不同的含义=:
在数学中,x = sint的意思是,x是sint的另一个名字。所以写x + y和sin(t) + y是一样的。函数式响应式编程在这方面就像数学:如果你写x + y,它是用t在使用时的任何值来计算的。 在类c编程语言(命令式语言)中,x = sin(t)是一个赋值:它意味着x存储在赋值时所取的sin(t)的值。
FRP是函数式编程(编程范式建立在一切都是函数的思想上)和响应式编程范式(建立在一切都是流的思想上(观察者和可观察的哲学))的结合。它应该是世界上最好的。
看看Andre Staltz关于响应式编程的文章。
免责声明:我的答案是在rx.js的上下文中给出的——一个用于Javascript的“响应式编程”库。
在函数式编程中,不是遍历集合的每个项,而是对集合本身应用高阶函数(hof)。因此,FRP背后的思想是,与其处理每个单独的事件,不如创建一个事件流(使用可观察对象*实现),并对其应用HoFs。通过这种方式,您可以将系统可视化为连接发布者和订阅者的数据管道。
The major advantages of using an observable are: i) it abstracts away state from your code, e.g., if you want the event handler to get fired only for every 'n'th event, or stop firing after the first 'n' events, or start firing only after the first 'n' events, you can just use the HoFs (filter, takeUntil, skip respectively) instead of setting, updating and checking counters. ii) it improves code locality - if you have 5 different event handlers changing the state of a component, you can merge their observables and define a single event handler on the merged observable instead, effectively combining 5 event handlers into 1. This makes it very easy to reason about what events in your entire system can affect a component, since it's all present in a single handler.
可观察对象是可迭代对象的对偶。
Iterable是一个惰性消费序列——迭代器在需要使用每个项时都会拉出它,因此枚举是由消费者驱动的。
可观察对象是一个惰性生成的序列——每一项在被添加到序列时都被推送给观察者,因此枚举是由生产者驱动的。
在纯函数式编程中,没有副作用。对于许多类型的软件(例如,任何与用户交互的软件),在某种程度上副作用都是必要的。
在保持函数式风格的同时获得类似副作用的行为的一种方法是使用函数式响应式编程。这是函数式编程和响应式编程的结合。(你链接到的维基百科文章是关于后者的。)
响应式编程背后的基本思想是,有特定的数据类型表示“随时间”的值。涉及这些随时间变化的值的计算本身也具有随时间变化的值。
例如,您可以将鼠标坐标表示为一对随时间变化的整数值。假设我们有这样的东西(这是伪代码):
x = <mouse-x>;
y = <mouse-y>;
在任何时刻,x和y都是鼠标的坐标。与非响应式编程不同,我们只需要进行一次赋值,x和y变量将自动保持“最新”。这就是响应式编程和函数式编程协同工作的原因:响应式编程消除了对变量突变的需要,同时仍然允许您完成许多可以通过变量突变完成的工作。
如果我们在此基础上进行一些计算,得到的值也将是随时间变化的值。例如:
minX = x - 16;
minY = y - 16;
maxX = x + 16;
maxY = y + 16;
在这个例子中,minX总是比鼠标指针的x坐标小16。使用响应式感知库,你可以这样说:
rectangle(minX, minY, maxX, maxY)
一个32x32的方框将围绕鼠标指针绘制,并跟踪它的移动位置。
这是一篇关于函数式响应式编程的很好的论文。