Low-latency ComponentArrays

Hi, I really enjoy using ComponentArrays. I used it as the state vector in OrdinaryDiffEq. One issue that we ran into was that the size of our components change for each simulation, leading to having to recompile a lot of code each time. The issue can be summarized as such:

julia> a = ComponentVector(; a=[1], b=[2,3])
julia> b = ComponentVector(; a=[1,2], b=[3])
julia> typeof(a) == typeof(b)
false

This is because the Axes keys and values are both in the type domain. I was recently discussing this on Slack with @MasonProtter, @SouthEndMusic and @ChrisRackauckas.

I made a little prototype struct CArray as a possible replacement of the current ComponentArray and I want some early feedback to see if folks would be interested in this, or point out flaws in this design.

I haven't focused yet on matching the API, but do already make sure that range, integer and nested components all work. The hope is that this can be mostly compatible, but probably still breaking. Some quick possibly flawed benchmarks show similar performance.

struct CArray{T, N, A<:DenseArray{T,N}, NT} <: DenseArray{T, N}
    data::A
    axes::NT
end

The full prototype is here: https://gist.github.com/visr/dde7ab3999591637451341e1c1166533

Start by reading the linked CArray prototype gist and comparing it with the current ComponentArray behavior described in the issue. Establish whether the proposed type-domain change can meet the compatibility and performance goals, then define an agreed design and acceptance criteria before implementation.