How to make your data type reducible

Let's see how to make a vector-of-vector a reducible collection; i.e., a type that can be fed to foldl.

struct VecOfVec{T}
    vectors::Vector{Vector{T}}
end

Method 1: The __foldl__ interface:

We need @next and complete to invoke the reducing function rf.

using Transducers
using Transducers: @next, complete

Supporting foldl and similar only requires Transducers.__foldl__:

function Transducers.__foldl__(rf, val, vov::VecOfVec)
    for vector in vov.vectors
        for x in vector
            val = @next(rf, val, x)
        end
    end
    return complete(rf, val)
end

Note that it's often a good idea to implement Base.eltype:

Base.eltype(::VecOfVec{T}) where {T} = T

It can be then used as the input to the transducers:

vov = VecOfVec(collect.([1:n for n in 1:3]))
collect(Map(identity), vov)

6-element Vector{Int64}:
 1
 1
 2
 1
 2
 3

Macro @next is used instead of function next to avoid the boilerplate for supporting early termination (see the details in in @next documentation). In practice, using @next means that your __foldl__ supports early termination:

vov |> Take(3) |> collect

3-element Vector{Int64}:
 1
 1
 2

More complex example:

vov |> PartitionBy(isequal(1)) |> Zip(Map(copy), Map(sum)) |> collect

4-element Vector{Tuple{Vector{Int64}, Int64}}:
 ([1, 1], 2)
 ([2], 2)
 ([1], 1)
 ([2, 3], 5)

Method 2: In terms of existing transducers and reducibles with asfoldable

Transducers.jl has a function Transducers.asfoldable which can be overloaded to convert your existing type into a foldable before being fed into __foldl__. This allows us to express a new type in terms of existing structures:

struct VecOfVec2{T}
    vectors::Vector{T}
end
Transducers.asfoldable(vov::VecOfVec2) = vov.vectors |> Cat()

This simply tells Transducers.jl that a VecOfVec2 may be folded over by simply applying the Cat transducer to the .vectors field of the struct. This is enough to reproduce all the examples from the previous section:

vov2 = VecOfVec2(collect.([1:n for n in 1:3]))
collect(Map(identity), vov2)

6-element Vector{Int64}:
 1
 1
 2
 1
 2
 3

vov2 |> Take(3) |> collect

3-element Vector{Int64}:
 1
 1
 2

vov2 |> PartitionBy(isequal(1)) |> Zip(Map(copy), Map(sum)) |> collect

4-element Vector{Tuple{Vector{Int64}, Int64}}:
 ([1, 1], 2)
 ([2], 2)
 ([1], 1)
 ([2, 3], 5)

Comparison to defining an Iterator:

Notice that writing Transducers.__foldl__ is very straightforward comparing to how to define an iterator:

function Base.iterate(vov::VecOfVec, state=(1, 1))
    #Iterator `state` is a tuple of an index `i` to `vov.vectors` and an
    #index `j` to `vov.vectors[i]`:
    i, j = state
    #If `i` is larger than the number of items, we are done:
    i > length(vov.vectors) && return nothing
    #If `j` is in bound, we are iterating the same sub-vector:
    vi = vov.vectors[i]
    if j <= length(vi)
        return vi[j], (i, j + 1)
    end
    #Otherwise, find the next non-empty sub-vector and start iterating it:
    for k in i + 1:length(vov.vectors)
        vk = vov.vectors[k]
        if !isempty(vk)
            return vk[1], (k, 2)  # i=k, j=2
        end
    end
    return nothing
end

Base.length(vov::VecOfVec) = sum(length, vov.vectors)

collect(vov)

6-element Vector{Int64}:
 1
 1
 2
 1
 2
 3

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