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Comment by vitaut | Hacker News Reader

> what scenario are you imagining where startup times would be a significant concern?

Starting a REPL or running a computation that doesn't take long time, adjusting parameters, re-running. Startup time may not be too big in absolute numbers but it's noticeable and adds up quickly especially if you start using more packages rather than toy programs that I used as an example.

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Ok that puts some bounds on it; we are talking 1/10ths of seconds as significant? Or 1/2 seconds? There is work on this (https://github.com/JuliaLang/IJulia.jl/issues/346) and later versions of Julia 0.4 feel pretty snappy to me compared to R (which is the other tool I generally use nowadays)

Sorry if I was being vague, but what situation would you re-run a short computation by executing the whole program again instead of using subroutines? Are we talking about a data scientist performing initial, exploratory analysis on a very small subset of data?

I agree. I'd estimate I restart the REPL about three or four times a day while developing. My analysis runs are minutes or longer, so a few seconds spinning up Julia+packages just doesn't ammount to much.

> Are we talking about a data scientist performing initial, exploratory analysis on a very small subset of data?

Yes, that's one example. Also when debugging one usually uses small data sets. There are plenty of cases where runtime is short.

I think the problem is that Julia is somewhat vague on how it should be used. If it stated explicitly that it is intended to be used in MATLAB-like fashion with one long-running instance that would save people from trying to use it as Python or other dynamic language.

In that case, it should seem like a fix is as easy as keeping an IJulia kernel running in the background all day.

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