We can run a performance test with these two different types, depicted here:

Our benchmark test function will compute the center of all points from an array, as follows:

using Statistics: mean

function center(points::AbstractVector{T}) where T
    return T(
        mean(p.x for p in points), 
        mean(p.y for p in points))
end

In addition, we will also define a function that can be used to make an array of points for whatever type we want:

make_points(T::Type, n) = [T(rand(), rand()) for _ in 1:n]

Let's start with a PointAny type.

We will generate 100,000 points and use BenchmarkTools to measure the time:

Next, we will run the performance test for the Point type:

As we can see, there is a huge difference between the two. Using the parametric Point type is approximately 25 times faster than the one that uses Any as a field type.

What we have learned from this anti-pattern is that we should use concrete types for fields defined in composite types. It is quite easy to factor out the abstract type we want into a type parameter. Doing this allows us to gain performance benefits from concrete types without sacrificing the ability to support other data types.