什么是数据流并发的良好激励示例？ [英] What is a good motivating example for dataflow concurrency?

问题描述

我了解数据流编程的基本知识，并在 Clojure中遇到了一些问题API ，来自Jonas Boner的演讲< a>， GPars 在Groovy等，我知道它在像Io这样的语言中流行（虽然我没有研究Io）。

I understand the basics of dataflow programming and have encountered it a bit in Clojure APIs, talks from Jonas Boner, GPars in Groovy, etc. I know it's prevalent in languages like Io (although I have not studied Io).

我缺少的一个令人信服的理由是，在构建并发程序时将数据流作为范例。为什么要使用数据流模型而不是可变状态+线程+锁模型（通常在Java，C ++等）或actor模型（常见于Erlang或Scala）或其他？

What I am missing is a compelling reason to care about dataflow as a paradigm when building a concurrent program. Why would I use a dataflow model instead of a mutable state+threads+locks model (common in Java, C++, etc) or an actor model (common in Erlang or Scala) or something else?

特别是，虽然我知道上面的语言（和Scala和Ruby）的库支持，我不知道一个单一的程序或库是这个模型的海报儿童用户。谁在使用它？为什么他们发现它比我提到的其他模型更好？

In particular, while I know of library support in the languages above (and Scala and Ruby), I don't know of a single program or library that is a poster child user of this model. Who is using it? Why do they find it better than the other models I mentioned?

推荐答案

我也有一个错误的例子。它不实现干净的actor模型，它没有并发问题，但它使用DF架构，并且普遍：任何电子表格软件（例如MS Excel）。

I have a wrong example, too. It does not implement a clean actor model, and it has no concurrency issues, but it uses DF architecture, and extremly popular: any spreadsheet software (e.g. MS Excel).

修改单元格时，它会向引用它的单元格发送重新计算信号。 Altough，当你使用一个工作表，它变得越来越大，你可以感觉到数据流编程的真正味道 - 工作的重点将改变：

When you modify a cell, it sends "recalculate" signal to the cells which have reference to it. Altough, when you are working with a sheet, which becomes bigger and bigger, you can feel the real taste of the dataflow programming - the focus of the work will change:

• 公式创建失去其初始重要性（您会发现自己只是克隆了相同的3-4个公式），


• 布局变得更加重要：

如果我们意识到，公式是组件，引用是消息，我们得到数据流编程的通常方式：首先，我们创建一些组件，然后我们与他们构建一个数据流图。如果组件太大，我们将它们分成更小的组件。最后，我们选择一个可视化组件来显示一个眼睛结果的结果。

If we realise, that formulas are components, and references are messages, we get the usual way of dataflow programming: first, we're creating some components, then we're building a dataflow graph with them. If components are too big, we split them into smaller ones. Finally, we're picking a visualization component for an eye-candy result presentation.

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