Know when to fold 'em: visualizing the streaming, concurrent reduce

Holy cow! Can you believe your luck? What better way to spend some portion of whatever day of the week it is than to think about parallelizing reduction algorithms?

Really? You can't think of one? In that case, I'll kick off our inevitable friendship by contributing a clojure implementation of a fancy algorithm that asynchronously reduces arbitrary input streams while preserving order. Then I'll scare you with glimpses of my obsessive quest to visualize what this algorithm is actually doing; if you hang around, I'll show you what I eventually cobbled together with clojurescript and reagent ...

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Yak herding for misers - wrangling hundreds of AWS Instances with Clojure

yak herding

Back in August, I wrote two posts about an experimental framework for distributed functional programming called Girder. The idea, in summary, was to make distributed code look as much as possible like ordinary Clojure, as opposed to structuring it explicitly around message passing (as in the actor model) or data flows (as in map/reduce, storm, et al). As I say, it was an experiment, but it was also something a cri de coeur (a mini-manifesto, if you will) against extraneous impingements on my code. Anyway, it sounds interesting, go back and read the posts, but you don't need ...

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vanholes - Van Laarhoven Lenses in Clojure

In two previous posts, I went on about lenses in Clojure. Pinholes comprised a small library of higher order functions to formalize and simplify the viewing and manipulation of complex nested structures. Tinholes did essentially the same thing, but with macros instead. In both cases, there's recursion going on, as we burrow through layers of nesting, but macros had the advantage of doing it all before compilation, giving core.typed a chance to check our work.

The macro post was inexplicably popular, catapulting me to levels of fame I never expected to achieve without consciously emulating an early De ...

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tinholes - performant, strongly typed lenses in Clojure

In a previous post, I built up a framework for lens-like constructs in Clojure: essentially some fancified versions of assoc-in and get-in to allow for bidirectional transformations along the nesting path and some utilities to generate special-purpose getter/setter functions. The name, "pinhole," is supposed to suggest a more primitive, utilitarian mechanism for achieving focus.

While still ruing (sort of) other mistakes, I found myself worrying that a triumphal sentence near the end of the piece

What's more, thanks to the expressive power of dynamic Clojure,
and higher order functions, these lenses are not just simple to
use but ...
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Pinholes 2 - Ignorance, misrepresentation and prejudice edition

Sseveral comments regarding the pinholes post, have forced me, against the deepest elements of my nature, to engage in thought. Since that might never happen again, I thought it meet to record the event.

I'm going to say "I" a lot, because this is mostly my opinions.

Bidirectional programming

As pointed out by Christian Schuhegger in a comment on the original post, lenses were originally introduced to computer science in the context of bidirectional programming, rather than as a tool for dealing with deeply nested immutable structures. He points to a good list of papers on the subject on ...

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Pinholes - Idiomatic Clojure Lenses

Lenses are a construct for getting, "setting" or "modifying" values within data structures, especially deeply nested data structures. The quotation marks have the usual meaning when they show up in funktionsprache:1 not mutating anything per se, but instead producing an object, or reference thereto, that is identical except for the requested change.

In Scala, the need for lenses is pretty glaring, as illustrated in Eugene Yokota's great explanation of lenses in Scalaz, because of the centrality of case classes. In his example problem, a turtle is represented with three case classes:

  case class Point(x: Double, y: Double ...
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Distributed purely functional programming, part 2

Update 2015-01-12

The algorithm as it exists in HEAD is somewhat different from the below, in ways that I'll describe (eventually) in an another post. In some ways, it's closer to Fork-Join, but with important differences to support reentrancy, share results of duplicate requests and adjust for the costs of distribution.

Recap of recap

In a previous post, I introduced a framework called Girder (the code is on github), which aims to facilitate Plain Old Functional Programming on distributed systems. By POFP, I mean code that, as much as possible, consists of normal looking calls to normal looking ...

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Beauty and the Beast - Distributed functional programming, part 1

Update 2015-01-12

The algorithm as it exists in HEAD is somewhat different from the below, in ways that I'll describe (eventually) in an another post. In some ways, it's closer to Fork-Join, but with important differences to support reentrancy, share results of duplicate requests and adjust for the costs of distribution.

OSS and Commercial Grids

Grid computing has always suffered the reputation of a buzzword that one suspects might not actually mean anything, but it has become especially ill-defined with the rise of open-source distributed computation frameworks like Spark, Storm and Grampa Hadoop. These extensively documented systems don ...

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FUNCTIONAL functional reactive programming, state monads and all that, in Clojure

In my last post, I talked a bit about how FRP might look if state were maintained explicitly in persistent data structures, rather than in hidden mutable structures. The accompanying code was in Scala, but my first implementation was actually in Clojure. I was originally going to use the Clojure code in the post, but, having taken motivating example code from a Scala paper, it felt lazy to switch to Clojure just because I felt like it.

That said, it really was more fun to write in Clojure, and in some ways I think it is clearer. Additionally, it seems ...

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FUNCTIONAL functional reactive programming

I totally agree with Paul Chiusano that the Reactive Manifesto not even wrong. In addition to the breezy non-falsifiability of its assertions, I have trouble with the name itself. Manifestos seldom work out well, in the sense of there not being a lot of corpses. (Plus which, "reactive" is acually a word, and a "reactive manifesto" doesn't sound like it would be very proactive, like.)

BUT reactive programming is important, it's useful, and I need to understand it better. Herewith, then, I shall:

  1. Very briefly introduce reactive programming as I understand it.
  2. Complain that I don't understand ...
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