• Basics
• Destructuring sum types
• Avoiding namespace clutter
• Custom printing
• Performance

Sum types, sometimes called 'tagged unions' are the type system equivalent of the disjoint union operation (which is not a union in the traditional sense). In the Rust programming language, these are called "Enums", and they're more general than what Julia calls an enum.

At the end of the day, a sum type is really just a fancy word for a container that can store data of a few different, pre-declared types and is labelled by how it was instantiated.

Users of statically typed programming languages often prefer Sum types to unions because it makes type checking easier. In a dynamic language like julia, the benefit of these objects is less obvious, but there are cases where they're helpful, like performance sensitive branching on heterogeneous types, and enforcing the handling of cases.

The simplest version of a sum type is just a list of constant variants (i.e. basically a julia enum):

using SumTypes

@sum_type Fruit begin
    apple
    banana
    orange
end

julia> apple
apple::Fruit

julia> banana
banana::Fruit

julia> orange
brange::Fruit

julia> typeof(apple) == typeof(banana) == typeof(orange) <: Fruit
true

But this isn't particularly interesting. More interesting are sum types which can enclose data. Let's explore a very fundamental sum type (fundamental in the sense that all other sum types may be derived from it):

@sum_type Either{A, B} begin
    Left{A}(::A)
    Right{B}(::B)
end

This says that we have a sum type Either{A, B}, and it can hold a value that is either of type A or of type B. Either has two 'constructors' which we have called Left{A} and Right{B}. These exist essentially as a way to have instances of Either carry a record of how they were constructed by being wrapped in dummy structs named Left or Right.

Here is how these constructors behave:

julia> Left(1)
Left(1)::Either{Int64, Uninit}

julia> Right(1.0)
Right(1.0)::Either{Uninit, Float64}

Notice that because both Left{A} and Right{B} each carry one fewer type parameter than Either{A,B}, then simply writing Left(1) is not enough to fully specify the type of the full Either, so the unspecified field is SumTypes.Uninit by default.

In cases like this, you can rely on implicit conversion to get the fully initialized type. E.g.

julia> let x::Either{Int, Float64} = Left(1)
           x
       end
Left(1)::Either{Int64, Float64}

Typically, you'll do this by enforcing a return type on a function:

function foo() :: Either{Int, Float64}
    # Randomly return either a Left(1) or a Right(2.0)
    rand(Bool) ? Left(1) : Right(2.0)
end

julia> foo()
Left(1)::Either{Int64, Float64}

julia> foo()
Right(2.0)::Either{Int64, Float64}

This is particularly useful because in this case foo is type stable!

julia> Core.Compiler.return_type(foo, Tuple{})
Either{Int64, Float64}

julia> isconcretetype(ans)
true

Note that unlike Union{A, B}, A <: Either{A,B} is false, and Either{A, A} is distinct from A.

Okay, but how do I actually access the data enclosed in a Fruit or an Either? The answer is destructuring. SumTypes.jl exposes a @cases macro for efficiently unwrapping and branching on the contents of a sum type:

julia> myfruit = orange
orange::Fruit

julia> @cases myfruit begin
           apple => "Got an apple!"
           orange => "Got an orange!"
           banana => error("I'm allergic to bananas!")
       end
"Got an orange!"

julia> @cases banana begin
           apple => "Got an apple!"
           orange => "Got an orange!"
           banana => error("I'm allergic to bananas!")
       end
ERROR: I'm allergic to bananas!
[...]

@cases can automatically detect if you didn't give an exhaustive set of cases (with no runtime penalty) and throw an error.

julia> @cases myfruit begin
           apple => "Got an apple!"
           orange => "Got an orange!"
       end
ERROR: Inexhaustive @cases specification. Got cases (:apple, :orange), expected (:apple, :banana, :orange)
[...]

Furthermore, @cases can destructure sum types which hold data:

julia> let x::Either{Int, Float64} = rand(Bool) ? Left(1) : Right(2.0)
           @cases x begin
               Left(l) => l + 1.0
               Right(r) => r - 1
           end
       end
2.0

i.e. in this example, @cases took in an Either{Int,Float64} and if it contained a Left, it took the wrapped data (an Int) bound it do the variable l and added 1.0 to l, whereas if it was a Right, it took the Float64 and bound it to a variable r and subtracted 1 from r.

The @cases macro still falls far short of a full on pattern matching system, lacking many features. For anything advanced, I'd recommend using @match from MLStyle.jl.

Generally, it's good to explicitly handle all cases of a sum type, but sometimes you just want one set of behaviour for a large set of cases. One option, is 'collections' of cases like so:

@sum_type Re begin
    Empty
    Class(::UInt8)
    Rep(::Re)
    Alt(::Re, ::Re)
    Cat(::Re, ::Re)
    Diff(::Re, ::Re)
    And(::Re, ::Re)
end;

isEmpty(x::Re) = @cases x begin
    Empty => true
    [Class, Rep, Alt, Cat, Diff, And] => false
end

This is the same as if one had manually written out

isEmpty(r::Re) = @cases r begin
    Empty => true
    Class => false
    Rep => false
    Alt => false
    Cat => false
    Diff => false
    And => false
end

You can also destructure repeated cases with the [] syntax:

count_classes(r::Re, c=0) = @cases r begin
    Empty => c
    Class => c + 1
    Rep(x) => c + count_classes(x)
   [Alt, Cat, Diff, And](x, y)  => c + count_classes(x) + count_classes(y)
end;

SumTypes also lets you use _ as a case predicate that accepts anything, but this only works in the final position, and does not allow destructuring:

isEmpty(x::Re) = @cases x begin
    Empty => true
    _     => false
end

julia> @sum_type Foo{T} :hidden begin
           A
           B{T}(::T)
       end

julia> A
ERROR: UndefVarError: A not defined

julia> B
ERROR: UndefVarError: B not defined

julia> Foo'
(A = A::Foo{Uninit}, B = var"#Foo#B")

julia> Foo'.A
A::Foo{Uninit}

julia> Foo'.B(1)
B(1)::Foo{Int64}

julia> let (; B) = Foo'
           B(1)
       end
B(1)::Foo{Int64}

julia> @sum_type Fruit2 begin
           apple
           orange
           banana
       end;

julia> apple
apple::Fruit2

julia> SumTypes.show_sumtype(io::IO, x::Fruit2) = @cases x begin
           apple => print(io, "apple")
           orange => print(io, "orange")
           banana => print(io, "banana")
       end

julia> apple
apple

julia> SumTypes.show_sumtype(io::IO, ::MIME"text/plain", x::Fruit2) = @cases x begin
           apple => print(io, "apple!")
           orange => print(io, "orange!")
           banana => print(io, "banana!")
       end

julia> apple
apple!

SumTypes.jl can provide some speedups compared to union-splitting when destructuring and branching on abstractly typed data.

module SumTypeTest

using SumTypes,  BenchmarkTools
@sum_type AT begin
    A(common_field::Int, a::Bool, b::Int)
    B(common_field::Int, a::Int, b::Float64, d::Complex)
    C(common_field::Int, b::Float64, d::Bool, e::Float64, k::Complex{Real})
    D(common_field::Int, b::Any)
end

foo!(xs) = for i in eachindex(xs)
    xs[i] = @cases xs[i] begin
        A(cf, a, b)       => B(cf+1, a, b, b)
        B(cf, a, b, d)    => C(cf-1, b, isodd(a), b, d)
        C(cf, b, d, e, k) => D(cf+1, isodd(cf) ? "hi" : "bye")
        D(cf, b)          => A(cf-1, b=="hi", cf)
    end
end

xs = rand((A(1, true, 10), 
           B(1, 1, 1.0, 1+1im), 
           C(1, 2.0, false, 3.0, Complex{Real}(1 + 2im)), 
           D(1, "hi")), 
	      10000);

display(@benchmark foo!($xs);)

end

BenchmarkTools.Trial: 10000 samples with 1 evaluation.
 Range (min … max):  300.541 μs …   2.585 ms  ┊ GC (min … max): 0.00% … 86.91%
 Time  (median):     313.611 μs               ┊ GC (median):    0.00%
 Time  (mean ± σ):   342.285 μs ± 242.158 μs  ┊ GC (mean ± σ):  8.29% ± 10.04%

  █                                                             ▁
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  301 μs        Histogram: log(frequency) by time       2.37 ms <

 Memory estimate: 620.88 KiB, allocs estimate: 19900.

module AbstractTypeTest

using BenchmarkTools

abstract type AT end
Base.@kwdef struct A <: AT
    common_field::Int
    a::Bool 
    b::Int
end
Base.@kwdef struct B <: AT
    common_field::Int
    a::Int
    b::Float64 
    d::Complex  # not isbits
end
Base.@kwdef struct C <: AT
    common_field::Int
    b::Float64 
    d::Bool 
    e::Float64
    k::Complex{Real}  # not isbits
end
Base.@kwdef struct D <: AT
    common_field::Int
    b::Any  # not isbits
end

foo!(xs) = for i in eachindex(xs)
    @inbounds x = xs[i]
    @inbounds xs[i] = x isa A ? B(x.common_field+1, x.a, x.b, x.b) :
        x isa B ? C(x.common_field-1, x.b, isodd(x.a), x.b, x.d) :
        x isa C ? D(x.common_field+1, isodd(x.common_field) ? "hi" : "bye") :
        x isa D ? A(x.common_field-1, x.b=="hi", x.common_field) : error()
end


xs = rand((A(1, true, 10), 
           B(1, 1, 1.0, 1+1im), 
           C(1, 2.0, false, 3.0, Complex{Real}(1 + 2im)), 
           D(1, "hi")), 
	      10000);
display(@benchmark foo!($xs);)

end

BenchmarkTools.Trial: 10000 samples with 1 evaluation.
 Range (min … max):  366.510 μs …   4.504 ms  ┊ GC (min … max):  0.00% … 90.65%
 Time  (median):     386.470 μs               ┊ GC (median):     0.00%
 Time  (mean ± σ):   478.369 μs ± 571.525 μs  ┊ GC (mean ± σ):  18.62% ± 13.77%

  █                                                           ▂ ▁
  █▇▄▅▅▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁█ █
  367 μs        Histogram: log(frequency) by time        4.1 ms <

 Memory estimate: 1.06 MiB, allocs estimate: 31958.

Unityper.jl is a somewhat similar package, with some overlapping goals to SumTypes.jl. However, In this test, Unityper.jl ends up doing much worse than abstract containers or SumTypes.jl:

module UnityperTest

using Unityper, BenchmarkTools

@compactify begin
    @abstract struct AT
        common_field::Int = 0
    end
    struct A <: AT
        a::Bool = true
        b::Int = 10
    end
    struct B <: AT
        a::Int = 1
        b::Float64 = 1.0
        d::Complex = 1 + 1.0im # not isbits
    end
    struct C <: AT
        b::Float64 = 2.0
        d::Bool = false
        e::Float64 = 3.0
        k::Complex{Real} = 1 + 2im # not isbits
    end
    struct D <: AT
        b::Any = "hi" # not isbits
    end
end

foo!(xs) = for i in eachindex(xs)
    @inbounds x = xs[i]
    @inbounds xs[i] = @compactified x::AT begin
        A => B(;common_field=x.common_field+1, a=x.a, b=x.b, d=x.b)
        B => C(;common_field=x.common_field-1, b=x.b, d=isodd(x.a), e=x.b, k=x.d)
        C => D(;common_field=x.common_field+1, b=isodd(x.common_field) ? "hi" : "bye")
        D => A(;common_field=x.common_field-1, a=x.b=="hi", b=x.common_field)
    end
end

xs = rand((A(), B(), C(), D()), 10000);
display(@benchmark foo!($xs);)

end

BenchmarkTools.Trial: 2539 samples with 1 evaluation.
 Range (min … max):  1.847 ms …   5.341 ms  ┊ GC (min … max): 0.00% … 64.05%
 Time  (median):     1.890 ms               ┊ GC (median):    0.00%
 Time  (mean ± σ):   1.968 ms ± 478.604 μs  ┊ GC (mean ± σ):  3.93% ±  9.68%

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  1.85 ms      Histogram: log(frequency) by time       4.9 ms <

 Memory estimate: 1.14 MiB, allocs estimate: 27272.

SumTypes.jl has some other advantages relative to Unityper.jl such as:

• SumTypes.jl allows parametric types for much greater container flexibility.
• SumTypes.jl does not require default values for every field of the struct.
• SumTypes.jl's @cases macro is more powerful and flexible than Unityper's @compactified.
• SumTypes.jl allows you to hide its variants from the namespace (opt in).

One advantage of Unityper.jl is:

• If you're not modifying the data and just re-using old heap allocated data, there are cases where Unityper.jl can avoid an allocation that SumTypes.jl would have incurred.