Well, p in pmap does stand for parallel. :) The two are obviously linked, Storm does both parallel and concurrent processing on the data. Another example is PigPen (http://techblog.netflix.com/2014/01/introducing-pigpen-map-r...) from Netflix. As far as pure concurrency goes, I'm not sure any companies have articles discussing that.
Ok thank you. Implementing map reduce style parallzation doesn't need any language level support. I don't see how clojure is any better at this than say java ( hadoop is java after all).
I am looking for 'pure concurrency' in a app like videos games exctracting last ounce of processing power from a multicore machine. This what the original comment stated was the core competence of Clojure.
>I am looking for 'pure concurrency' in a app like videos games exctracting last ounce of processing power from a multicore machine. This what the original comment stated was the core competence of Clojure.
That's not actually what the parent comment stated though. It says that Clojure is designed to solve real-world problems in production environments.
In many cases this doesn't mean squeezing out the last ounce of processing power, but being able to reason about concurrent code safely. You pay an overhead of using immutability in order to facilitate higher correctness.
When your system grows it becomes important to be able to reason about parts of it in isolation. This is particularly important when you're running in a multithreaded environment.
That's what people generally mean when they say that Clojure has focus on concurrency. Since the data is immutable you can make assumptions about what the code is doing that you wouldn't be able to make otherwise.
