$ time julia -e "exit"
real 0m0.156s
user 0m0.096s
sys 0m0.100s
$ time julia -e "using Plots"
real 0m1.219s
user 0m0.981s
sys 0m0.408s
$ time julia -e "using Plots; display(plot(rand(10)))"
real 0m1.581s
user 0m1.160s
sys 0m0.400s
Worse, there are still way too many compilation traps. Splatted a large collection into a function? Compiler chokes. Your code accidentally moves a value from the value to the type domain? You end up with millions of new types, compiler chokes. Accidentally pirate a method? Huge latency. Chose to write type unstable code? Invalidations tank your latency.
It's the most annoying thing about hn that people will regularly declare/proclaim some thing like this as if it's a Nobel prize winning discovery when it's actually just some incremental improvement. I have no idea how this works in these people's lives - aren't we all SWEs where the specifics actually matter. My hypothesis is these people are just really bad SWEs.
The problem comes from Julia trying to be two languages at once -- the dynamic language that is useful for quickly generating plots and prototyping; and the production language that's running production code on the backend server, or running the HPC simulation on the supercomputer. They've deliberately staked out the middle point here, which comes with the benefit of speed but the tradeoff is in the ttfp latency. It might be considered the leak of the multiple dispatch abstraction. Yes it can feel like magic when it works, but when it doesn't it manifests as spikes in latency and an explosion of complexity.
In the end I don't know how big the ttfp issue is for Julia. But they've certainly branded it and the existence of the problem has made its way to people who don't use the language, which is an issue for community growth. They've also left themselves open for a language to come on that's "Julia but without ttfp issues".
All I can say is that many of "us" live in that tension between high level and low level every day. It's actually going to become more pronounced with `--trim` and the efforts on static compilation in the near term. The fact that Julia can span both is why I'm a part of it.
- Plots.jl, 1.4 seconds (include package loading)
- CairoMakie.jl, 4 seconds (including package loading)
julia> @time @eval (using Plots; display(plot(rand(3))))
1.477268 seconds (1.40 M allocations: 89.648 MiB, 2.70% gc time, 7.16% compilation time: 5% of which was recompilation)