My advice is to treat channel ops (<! and >!) as IO, which they are. In
functional programming we try to stay away from doing IO deep down inside a
call stack. So don't do that. Instead use async/pipeline functions and
channel transducers to create pipelines and flow the data through them.

A sort of anti-pattern I see a lot is creating a lot of one-shot channels
and go blocks inside every function. The problem, as you see is that this
creates a lot of garbage. A much more efficient plan is to stop using
core.async as a RPC-like system, and start using it more like a dataflow
language: Identity data sources and sinks, and then transform and flow the
data between them via core.async.

It's interesting to note that core.async started as something that looked a
lot like C#'s Async/Await, but that was dropped in favor of CSP pretty
quickly. So there's reasons why the language isn't optimized for this sort
of programming style.

Timothy

On Thu, Jul 30, 2015 at 9:17 AM, Martin Raison <martinrai...@gmail.com>
wrote:

> Hey Thomas,
>
> Thanks for the great feedback! A few clarifications below.
>
>
>> It should be noted that your async macro does in fact use the dispatcher
>> just like a normal go would, the only difference is that it will start
>> executing immediately in the current thread until the first "pause" instead
>> of dispatching. After the "pause" the dispatcher will run it though which
>> is not guaranteed to be the same thread.
>>
>
> I agree that the dispatcher will eventually be used, but the idea is that
> if your code is deeply nested, this will only happen at the deepest level
> (when an actual go block is used). So the dispatcher will only be run
> there, instead of running for every single wrapping function call. The idea
> is for the codebase to look like this (across multiple functions):
>
> (async ... (<! (async ... (<! (async ... (<! (async ... (<! (async ... (<!
> (go <long-running stuff, run in a different thread>)))))))))))
>
> Here the dispatcher is only run once, instead of 6 times. All the "glue
> code" is run in the calling thread.
>
>
>> Therefore I'm not actually sure a ThreadLocal will work. Did you test
>> that you get the actual correct result?
>>
>
> I do get the correct result, although your concern is valid. I think it
> works because the generated state machine calls ioc/return-chan in the
> curent thread. If part of the computation has been dispatched to an other
> thread, then the result of that dispatched computation will be brought back
> into the current thread with an actual channel before being fed to
> ioc/return-chan. I would love to know if there are situations where
> return-chan would be called directly from another thread, since I haven't
> observed that so far and I'm not familiar enough with the state machine
> internals.
>
>
>> Your benchmark is also not very useful, you are basically just measuring
>> the frameworks overhead which is tiny to what it provides. In actual
>> programs most of the time will not be spent in core.async internals, at
>> least not from what I have observed so far. go blocks are actually pretty
>> cheap.
>>
>
> Measuring the framework overhead was in fact my intention, but I agree it
> would be useful to know if the core.async overhead can actually be
> significant in a real-world application. Typically an IO-bound application
> passing lots of requests to other servers and doing very light
> transformation on the results.
>
>
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-- 
“One of the main causes of the fall of the Roman Empire was that–lacking
zero–they had no way to indicate successful termination of their C
programs.”
(Robert Firth)

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