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Is Julia at the level of stability to be useable by large projects? I've tried to follow and it always looked promising but there were a lot of reports about th

by dzdt 2y ago

Is Julia at the level of stability to be useable by large projects? I've tried to follow and it always looked promising but there were a lot of reports about things being subtly broken in hard-to-debug ways . Anyone have more recent experience?

jarbus 2y ago
Julia’s been stable for my work, from what I understand, things have gotten better over the years on that front. The biggest issue Julia gives me is packaging, just because two libraries that you import can use different versions of the same dependency (among other things)
kunstmord 2y ago
Have been writing all my numerical code in Julia for the last 1.5 years, haven’t run into any issues at all. Actively use things like Trixi.jl (CFD framework), Jump/IPOPT/PRIMA for numerical optimisation, OrdinaryDifferentialEquations for ODE solutions; a few colleagues actively use Gridap.jl for their work. Not sure how large-scale these libraries can be considered, but they all seem to work fine, fast, and stable (potential issues with LoopVectorization.jl in upcoming versions of Julia are somewhat concerning, but IIRC, for 1.11 this has been resolved)
ssfrr 2y ago
I did a bunch of contract work last year at a company that was all-in on Julia and it was a really pleasant experience. IMO one of the issues with Julia is that it’s easy to get nerd-sniped trying to do clever things with the type system and to make as much of your code as possible statically-inferable. Code and libraries that rely heavily on type dispatch ends up throwing MethodErrors deep into the call stack, far away from your code, which makes it harder to debug them. More mature Julia developers tend to keep things simpler, and make better use of dynamic types instead of contorting to treat it like a statically-typed language.
pjmlp 2y ago
Stable enough for these folks, https://info.juliahub.com/case-studies https://info.juliahub.com/case-studies

by dzdt 2y ago

jarbus 2y ago

Julia’s been stable for my work, from what I understand, things have gotten better over the years on that front. The biggest issue Julia gives me is packaging, just because two libraries that you import can use different versions of the same dependency (among other things)

kunstmord 2y ago

Have been writing all my numerical code in Julia for the last 1.5 years, haven’t run into any issues at all. Actively use things like Trixi.jl (CFD framework), Jump/IPOPT/PRIMA for numerical optimisation, OrdinaryDifferentialEquations for ODE solutions; a few colleagues actively use Gridap.jl for their work. Not sure how large-scale these libraries can be considered, but they all seem to work fine, fast, and stable (potential issues with LoopVectorization.jl in upcoming versions of Julia are somewhat concerning, but IIRC, for 1.11 this has been resolved)

ssfrr 2y ago

I did a bunch of contract work last year at a company that was all-in on Julia and it was a really pleasant experience. IMO one of the issues with Julia is that it’s easy to get nerd-sniped trying to do clever things with the type system and to make as much of your code as possible statically-inferable. Code and libraries that rely heavily on type dispatch ends up throwing MethodErrors deep into the call stack, far away from your code, which makes it harder to debug them. More mature Julia developers tend to keep things simpler, and make better use of dynamic types instead of contorting to treat it like a statically-typed language.

pjmlp 2y ago

Stable enough for these folks, https://info.juliahub.com/case-studies https://info.juliahub.com/case-studies