> it’s better to choose Rust for software engineering and Python for scripting.
Rust and Python work in some scenarios but not all. Unless the native components are all developed already, you will need a Rust programmer on your team. Rust expertise may be available to organizations of a sufficient size and a narrow focus. In my experience, this sort of arrangement requires a team with dedicated resources.
What I encountered more frequently are attempts to use Python as a full stack language. People are not just using it to implement the scripting frontend interface but also trying to implement the backend that does the heavy processing and the numerical computation. As a developer this is a terrible experience. I'm in the middle of replacing for loops in Python with vectorized numpy code but most of my efficiency gains are erased because I still need Python tuples in the end. Yesterday, I had to consider whether exceptions I throw in Cython code can be caught properly in Python.
Research software engineering is one field where you really do need a full stack language. That kind of software engineering requires high performance with limited resources. Julia with some initial patience does deliver a better developer experience in this scenario than the equivalent Python experience for me partly because I do not need to play vectorizarion games, and I can do everything in one integrated environment.
While, yes, interfaces and static tooling could be better, I do think the situation has gotten better over time. There are interface schemes available and additional static tooling is available.
Julia could deliver a better user experience though. Admittedly, the Python tooling to deploy complex solutions has significantly improved lately via tools such as pixi. Julia tools could deliver generic binary code with packages to ease the initial user experience. Advanced users could re-precompile packages to optimize.
The most promising success I have had with Julia is putting notebook interfaces in front of scientists or deploying web apps. My observation in this scenario is that Julia code can be presented as simple enough for a non-professional programmer to modify themselves. Previously, I have only seen that work well with MATLAB and Python. I have not seen this happen with Rust, and I do not expect that to change.
The other observation is that users appreciate the speed of Julia in two ways.
1. Advanced data visualizations are responsive to changes to data. In Python, I would typically need to precompute or prerender these visualizations. With Julia, this can be done dynamically on the fly.
2. Julia user interfaces respond quickly to user input.
While I think Julia has plenty of room to improve, I do think those improvements are possible. I have also greatly appreciated how much Julia has improved in the past five years.
The 0 or 1 based indexing is actually a very superficial debate for people not very familiar with Julia. Note that 1-based indexing is a standard library feature not inherent to the Julia language itself.
The real indexing issue is whether arbitrary-base abstraction is too easily available.
# Correct, Vector is 1-based
function mysum(v::Vector{T}) where {T <: Integer}
s = zero(T)
for i in 1:length(v)
s += v[i]
end
return s
end
#Incorrect, AbstractVector is not necessarily one based
function mysum(v::AbstractVector{T}) where {T <: Integer)
s = zero(T)
for i in 1:length(v)
s += v[i]
end
return s
end
#Correct
function mysum(v::AbstractVector{T}) where {T <: Integer)
s = zero(T)
for e in v
s += e
end
return s
end
Basically, the concrete `Vector` type is 1-based. However, `AbstractVector` is could have an arbitrary first index. OffsetArrays.jl is a non-standard package that provides the ability to create arrays with indexes that can start at an arbitrary point including 0.
