Julia Object Oriented Programming

Julia is a nice and promising scientific language for high performance computing, with the central paradigm of multiple dispatch. However, Julia does only partially support object oriented programming (OOP) with dot notation. For example, "objects" can not have their own methods. Unfortunately, this happens to be the style of programming that I prefer.

This StackOverflow question
discusses the matter, however, the fields (if exposed) in the solution get wrapped in Core.Box, which kind of defeats the purpose of fields, as they can no longer be accessed in the manner that one would expect.

I figured out an undocumented way in which object oriented programming with dot notation, including methods and without boxing, can be implemented and decided to write this post so that the knowledge can be passed on and the relative common question of dot notational OOP in Julia could be answered with more than "No, it does not work". Additionally, I hope that this way of programming would become more widely supported in Julia.

Python Vs Julia Classes

Consider the following class in Python and how it's methods are called:

# Python
class ExampleClass:
    def __init__(self, field_0, field_1):
        self.field_0 = field_0
        self.field_1 = field_1

    def method_0(self):
        return self.field_0 * self.field_1

    def method_1(self, n):
        return (self.field_0 + self.field_1) * n

    def method_2(self, val_0, val_1):
        self.field_0 = val_0
        self.field_1 = val_1

ex = ExampleClass(10, 11)
ex.method_0()
ex.method_1(1)
ex.method_2(20, 22)

The common way to implemented this in Julia would look like this:

# Julia
mutable struct ExampleClass
    field_0
    field_1
end

function method_0(example_class)
    return example_class.field_0 * example_class.field_1
end

function method_1(example_class, n)
    return (example_class.field_0 + example_class.field_1) * n
end

function method_2(example_class, val_0, val_1)
    example_class.field_0 = val_0
    example_class.field_1 = val_1
end

ex = ExampleClass(10, 11)
method_0(ex)
method_1(ex, 1)
method_2(ex, 20, 22)

The key difference between these two examples is that the methods in Python belong to the object and the functions in Julia belong to the global scope. I do not like the concept of global scope and would like to avoid it as much as possible.

Julia OOP Methods With Dot Notation

By implementing the class in a different way we can replicate the Pythonic dot notation. Consider the following:

# Julia
mutable struct ExampleClass
    field_0
    field_1
    method_0
    method_1
    method_2

    function ExampleClass(field_0, field_1)
        this = new()

        this.field_0 = field_0
        this.field_1 = field_1

        this.method_0 = function()
            return this.field_0 * this.field_1
        end

        this.method_1 = function(n)
            return (this.field_0 + this.field_1) * n
        end

        this.method_2 = function(val_0, val_1)
            this.field_0 = val_0
            this.field_1 = val_1
        end

        return this
    end
end

ex = ExampleClass(10, 11)
ex.method_0()
ex.method_1(1)
ex.method_2(20, 22)

Now it works like in Python, just as I like it!

Private Variables

What about private variables? I see that there are two ways in which private variables can be implemented. One is to add new fields to the
struct, but prefix them with an underscore like this:

# Julia
mutable struct ExampleClass
    field_0
    field_1
    method_0
    method_1
    method_2
    _private_var

    function ExampleClass(field_0, field_1)
        this = new()
        this._private_var = 0
        ...
    end
end

Unfortunately, the variables are not really private and can still be accessed from the outside. Another way, with truly private variables
is to include them in the constructor like this:

# Julia
mutable struct ExampleClass
    field_0
    field_1
    method_0
    method_1
    method_2

    function ExampleClass(field_0, field_1)
        this = new()
        private_var = 0

        ...
    end
end

Now the private variables are really private, however, they can not be accessed with dot notation from this, which is unfortunate.
I prefer the underscore method.

How Does it Work?

The revised Julia code relies primarily on three things: closures, variable capturing and anonymous functions.

Caveats

Unfortunately this way of programming has some caveats, at least when compared to the "idiomatic" Julia way.

• The size of the classes grow, with each method increasing the size by 8 bytes (at least on 64 bit systems) and thus allocation becomes slightly slower.
• Calling the anonymous functions is slightly slower than calling the global functions.
• Capturing variables in Julia has some performance implications. See the Julia docs. Using let this = this may have some performance benefit when applied to the anonymous functions, but I am uncertain of its effects in this case.
• The classes (or structs) must be mutable, since they are modified in the constructor. This can perhaps be circumvented in some fashion.

Performance Evaluation

This section provides a quick performance comparison between the both styles in Julia. Note that the results are highly dependent on the fields and
methods of the classes and as such this test should not be considered to be representative of every situation. This test focuses on allocation
and method call performance. You should benchmark your own code for your self, which probably has other underlying assumptions. The benchmarks are executed
with these two types:

# Julia
mutable struct DotNotationType
    field_0::Int
    field_1::Int
    field_2::Int
    field_3::Int
    do_work::Function

    function DotNotationType(field_0::Int, field_1::Int, field_2::Int, field_3::Int)
        this = new()
        this.field_0 = field_0
        this.field_1 = field_1        
        this.field_2 = field_3
        this.field_2 = field_3


        this.do_work = function(n::Int)
            sum = 0

            for i in 1:n
                sum += this.field_0 * this.field_1 + this.field_2 * this.field_3 ^ n 
            end

            return sum
        end

        return this
    end
end

mutable struct GlobalScopeType
    field_0::Int
    field_1::Int
    field_2::Int
    field_3::Int
end

function do_work(gst::GlobalScopeType, n::Int)
    sum = 0

    for i in 1:n
        sum += gst.field_0 * gst.field_1 + gst.field_2 * gst.field_3 ^ n 
    end

    return sum
end

Boxing & Class Sizes

println(DotNotationType(1, 2, 3, 4).field_0)
println(DotNotationType(1, 2, 3, 4).method_0)

println(sizeof(DotNotationType))
println(sizeof(GlobalScopeType))

1
#61

40
32

The fields of DotNotationType are not boxed within Core.Box, as in this question on StackOverflow.
Additionally, the size of DotNotationType is 8 bytes, or 1.25x, larger than the size of GlobalScopeType. Note that in "real" OOP languages, methods on a class do not generally make its memory imprint larger. This is due to the fact that the methods are conceptually placed in the global scope at compile time (not accounting for anonymous functions in other languages).

Allocation Performance

using Pkg
Pkg.add("BenchmarkTools")
using BenchmarkTools

function allocate_DotNotationType()
    types = DotNotationType[]

    for i in 1:100000
        push!(types, DotNotationType(i, i * i - i))
    end

    return types
end

function allocate_GlobalScopeType()
    types = GlobalScopeType[]

    for i in 1:100000
        push!(types, GlobalScopeType(i, i * i - i))
    end

    return types
end

# Assuming execution from a Jupyter Notebook
display(@benchmark allocate_DotNotationType())
display(@benchmark allocate_GlobalScopeType())

BenchmarkTools.Trial: 
  memory estimate:  78.89 KiB
  allocs estimate:  2010
  --------------
  minimum time:     23.629 μs (0.00% GC)
  median time:      26.958 μs (0.00% GC)
  mean time:        34.836 μs (17.34% GC)
  maximum time:     3.753 ms (96.40% GC)
  --------------
  samples:          10000
  evals/sample:     1
BenchmarkTools.Trial: 
  memory estimate:  63.27 KiB
  allocs estimate:  1010
  --------------
  minimum time:     17.818 μs (0.00% GC)
  median time:      20.827 μs (0.00% GC)
  mean time:        26.720 μs (15.82% GC)
  maximum time:     3.183 ms (95.79% GC)
  --------------
  samples:          10000
  evals/sample:     1

Allocating DotNotationType is about 1.29x (comparing medians) slower than allocating GlobalScopeType. This seems to be directly dependent on the
fact that the size of DotNotationType is 1.25x larger than the size of GlobalScopeType.

Method Performance

using Pkg
Pkg.add("BenchmarkTools")
using BenchmarkTools
dnts = allocate_DotNotationType()
gsts = allocate_GlobalScopeType()

function call_DotNotationType(dnts)
    sum = 0.0

    for d in dnts
        sum += d.do_work(4)
    end

    return sum
end

function call_GlobalScopeType(gsts)
    sum = 0.0

    for g in gsts
        sum += do_work(g, 4)
    end

    return sum
end

# Assuming execution from a Jupyter Notebook
display(call_DotNotationType(dnts))
display(call_GlobalScopeType(gsts))

display(@benchmark call_DotNotationType(dnts))
display(@benchmark call_GlobalScopeType(gsts))

-6.042282548123895e20
-6.042282548123895e20
BenchmarkTools.Trial: 
  memory estimate:  31.25 KiB
  allocs estimate:  2000
  --------------
  minimum time:     72.623 μs (0.00% GC)
  median time:      74.246 μs (0.00% GC)
  mean time:        79.119 μs (1.95% GC)
  maximum time:     2.326 ms (96.27% GC)
  --------------
  samples:          10000
  evals/sample:     1
BenchmarkTools.Trial: 
  memory estimate:  16 bytes
  allocs estimate:  1
  --------------
  minimum time:     24.066 μs (0.00% GC)
  median time:      31.175 μs (0.00% GC)
  mean time:        30.374 μs (0.00% GC)
  maximum time:     377.014 μs (0.00% GC)
  --------------
  samples:          10000
  evals/sample:     1

Both methods get the same result, which implies that both methods do the same work. However, calling the method of DotNotationType is about 2.38x (comparing medians) slower than calling the function of GlobalScopeType. I am not entirely sure what is causing the slow down, but it may have something to do with the issues mentioned in the Julia docs about the performance of capturing variables or it may have something to do with the optimization of anonymous functions.

Conclusions

• OOP is possible in Julia.
• OOP with dot notation methods is possible in Julia.
• OOP without boxing of fields is possible.
• OOP with dot notation does have caveats and generally worse performance.
• OOP dot notation methods in Julia are implemented in a generally inferior approach than that of most mainstream programming languages. Note that this is due to the fact that the way of programming presented in this post declares the functions as anonymous functions. This is not how the most common "real" OOP languages implement class methods.

Great, we got a working solution for object oriented programming with dot notational methods! The issues with the dot notational way of programming may or may not affect the general performance of your application, but I expect that this is highly domain specific. For example, in the "Method Performance" section I specifically focused on measuring the performance of method calls. Measuring the performance of actual work within a method may behave drastically different. Regardless, this may at least be a good way of porting dot notational code from, for example, Python to Julia and hopefully this will encourage Julia to implement true classes with true dot notation!