Help with allocations (variable length least squares)

New to Julia
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All those allocations are from the x = A\b:

 ──────────────────────────────────────────────────────────────────
                           Time                   Allocations      
                   ──────────────────────   ───────────────────────
 Tot / % measured:      415ms / 0.01%           60.9MiB / 0.11%    

 Section   ncalls     time   %tot     avg     alloc   %tot      avg
 ──────────────────────────────────────────────────────────────────
 solve          1   26.0μs  90.2%  26.0μs   69.1KiB  100%   69.1KiB
 loop           1   2.82μs  9.76%  2.82μs     0.00B  0.00%    0.00B
 ──────────────────────────────────────────────────────────────────

This is the output of:

Code 
using StaticArrays
using LinearAlgebra
using BenchmarkTools
using TimerOutputs


function LS5(p,points,values,neighbours)
    numNeighbours = length(neighbours[p])::Int;
    vec1=SA_F64[1.0,2.0,3.0]; vec2=SA_F64[2.0,3.0,4.0];
    A=zeros(MMatrix{7,2});
    b=zeros(MVector{7});
    @timeit tmr "loop" for n in 1:numNeighbours
        neighbour = neighbours[p][n]::Int;
        A[n,1] = dot(vec1,points[neighbour]-points[p]);
        A[n,2] = dot(vec2,points[neighbour]-points[p]);
        b[n] = values[neighbour]-values[p];
    end
    @timeit tmr "solve" x = A \ b
    return x[1]*vec1 + x[2]*vec2;
end


const tmr=TimerOutput()

points = [SVector{3}(rand(3)) for _ in 1:100];
neighbours = []
for i in 1:100
    numNeighbours = rand(4:7);
    push!(neighbours,(rand(1:100,numNeighbours)));
end
values = rand(100);

LS5(1,points,values,neighbours)

@show tmr

Indeed, I don’t know why is that. Maybe a separate thread to understand that is important. Minimal working example:

Example 
julia> A = MMatrix{2,2,Float64}(rand(2,2))
2×2 MMatrix{2, 2, Float64, 4} with indices SOneTo(2)×SOneTo(2):
 0.768859  0.337918
 0.338688  0.791925

julia> b = MVector{2,Float64}(rand(2))
2-element MVector{2, Float64} with indices SOneTo(2):
 0.23421653608437043
 0.4320080108699409

julia> A\b
2-element MVector{2, Float64} with indices SOneTo(2):
 0.07988759245908458
 0.5113500762999404

julia> ldiv!(qr(A),b)
ERROR: MethodError: no method matching ldiv!(::StaticArrays.QR{MMatrix{2, 2, Float64, 4}, MMatrix{2, 2, Float64, 4}, MVector{2, Int64}}
, ::MVector{2, Float64})
Closest candidates are:

Yes, indeed. With variable sizes you will have problems in general. Particularly, since static arrays have the size in their type, variables sizes means variable types. Data structures like that one I think are better approached with different strategies (vectors containing all data, and linked lists to point where each set starts, and the number of elements of each one).

(also it is a good idea to use neighbours = Vector{Int}[], so that this is not a vector of Any)