Container type declaration when computing equivalence classes for performance vs genericity

New to Julia
1

It is advised in the performance section of the documentation that container type should not be AbstractType for better performance. I wonder what is the correct way to declare container partition, data in the following pseudocode and if these proper declaration will boost my performance. Note that I do know and control the output type of computeEquivalenceClass, computeAdditionalDatum.

baseSet = collect(1:10e10)
partition = [] # Array{Any}
data = [] # Array{Any}
while !isempty(baseSet)
    class = computeEquivalenceClass()
    datum = computeAdditionalDatum()
    push!(partition, class)
    push!(data, datum)
    filter!(k->!(k in class), baseSet)
end

Thank you.

2

If, for example, class is of type MyClass initialize partition with:

partition = MyClass[]

or, if you feel to be more explicit,

partition = Vector{MyClass}(undef,0)

But all this should be inside a function, otherwise partition will be type unstable anyway.

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