StructArrays

This package introduces the type StructArray which is an AbstractArray whose elements are struct (for example NamedTuples, or ComplexF64, or a custom user defined struct). While a StructArray iterates structs, the layout is column based (meaning each field of the struct is stored in a separate Array).

Base.getproperty or the dot syntax can be used to access columns, whereas rows can be accessed with getindex.

The package was largely inspired by the Columns type in IndexedTables which it now replaces.

Example usage to store complex numbers

julia> using StructArrays, Random

julia> Random.seed!(4);

julia> s = StructArray{ComplexF64}((rand(2,2), rand(2,2)))
2×2 StructArray(::Array{Float64,2}, ::Array{Float64,2}) with eltype Complex{Float64}:
 0.680079+0.625239im   0.92407+0.267358im
 0.874437+0.737254im  0.929336+0.804478im

julia> s[1, 1]
0.680079235935741 + 0.6252391193298537im

julia> s.re
2×2 Array{Float64,2}:
 0.680079  0.92407
 0.874437  0.929336

julia> fieldarrays(s) # obtain all field arrays as a named tuple
(re = [0.680079 0.92407; 0.874437 0.929336], im = [0.625239 0.267358; 0.737254 0.804478])

Note that the same approach can be used directly from an Array of complex numbers:

julia> StructArray([1+im, 3-2im])
2-element StructArray(::Array{Int64,1}, ::Array{Int64,1}) with eltype Complex{Int64}:
 1 + 1im
 3 - 2im

Collection and initialization

One can also create a StructArrray from an iterable of structs without creating an intermediate Array:

julia> StructArray(log(j+2.0*im) for j in 1:10)
10-element StructArray(::Array{Float64,1}, ::Array{Float64,1}) with eltype Complex{Float64}:
 0.8047189562170501 + 1.1071487177940904im
 1.0397207708399179 + 0.7853981633974483im
 1.2824746787307684 + 0.5880026035475675im
 1.4978661367769954 + 0.4636476090008061im
  1.683647914993237 + 0.3805063771123649im
 1.8444397270569681 + 0.3217505543966422im
  1.985145956776061 + 0.27829965900511133im
 2.1097538525880535 + 0.24497866312686414im
 2.2213256282451583 + 0.21866894587394195im
 2.3221954495706862 + 0.19739555984988078im

Another option is to create an uninitialized StructArray and then fill it with data. Just like in normal arrays, this is done with the undef syntax:

julia> s = StructArray{ComplexF64}(undef, 2, 2)
2×2 StructArray(::Array{Float64,2}, ::Array{Float64,2}) with eltype Complex{Float64}:
 6.91646e-310+6.91646e-310im  6.91646e-310+6.91646e-310im
 6.91646e-310+6.91646e-310im  6.91646e-310+6.91646e-310im

julia> rand!(s)
2×2 StructArray(::Array{Float64,2}, ::Array{Float64,2}) with eltype Complex{Float64}:
 0.680079+0.874437im  0.625239+0.737254im
  0.92407+0.929336im  0.267358+0.804478im

Using custom array types

StructArrays supports using custom array types. It is always possible to pass field arrays of a custom type. The "custom array of structs to struct of custom arrays" transformation will use the similar method of the custom array type. This can be useful when working on the GPU for example:

julia> using StructArrays, CuArrays

julia> a = CuArray(rand(Float32, 10));

julia> b = CuArray(rand(Float32, 10));

julia> StructArray{ComplexF32}((a, b))
10-element StructArray(::CuArray{Float32,1}, ::CuArray{Float32,1}) with eltype Complex{Float32}:
  0.19555175f0 + 0.9604322f0im
  0.68348145f0 + 0.5778245f0im
  0.69664395f0 + 0.79825306f0im
 0.118531585f0 + 0.3031248f0im
  0.80057466f0 + 0.8964418f0im
  0.63772964f0 + 0.2923274f0im
  0.65374136f0 + 0.7932533f0im
   0.6043732f0 + 0.65964353f0im
   0.1106627f0 + 0.090207934f0im
    0.707458f0 + 0.1700114f0im

julia> c = CuArray(rand(ComplexF32, 10));

julia> StructArray(c)
10-element StructArray(::Array{Float32,1}, ::Array{Float32,1}) with eltype Complex{Float32}:
  0.7176411f0 + 0.864058f0im
   0.252609f0 + 0.14824867f0im
 0.26842773f0 + 0.9084332f0im
 0.33128333f0 + 0.5106474f0im
  0.6509278f0 + 0.87059164f0im
  0.9522146f0 + 0.053706646f0im
   0.899577f0 + 0.63242567f0im
   0.325814f0 + 0.59225655f0im
 0.56267905f0 + 0.21927536f0im
 0.49719965f0 + 0.754143f0im

If you already have your data in a StructArray with field arrays of a given format (say plain Array) you can change them with replace_storage:

julia> s = StructArray([1.0+im, 2.0-im])
2-element StructArray(::Array{Float64,1}, ::Array{Float64,1}) with eltype Complex{Float64}:
 1.0 + 1.0im
 2.0 - 1.0im

julia> replace_storage(CuArray, s)
2-element StructArray(::CuArray{Float64,1}, ::CuArray{Float64,1}) with eltype Complex{Float64}:
 1.0 + 1.0im
 2.0 - 1.0im

Example usage to store a data table

julia> t = StructArray((a = [1, 2], b = ["x", "y"]))
2-element StructArray(::Array{Int64,1}, ::Array{String,1}) with eltype NamedTuple{(:a, :b),Tuple{Int64,String}}:
 (a = 1, b = "x")
 (a = 2, b = "y")

julia> t[1]
(a = 1, b = "x")

julia> t.a
2-element Array{Int64,1}:
 1
 2

julia> push!(t, (a = 3, b = "z"))
3-element StructArray(::Array{Int64,1}, ::Array{String,1}) with eltype NamedTuple{(:a, :b),Tuple{Int64,String}}:
 (a = 1, b = "x")
 (a = 2, b = "y")
 (a = 3, b = "z")

Lazy row iteration

StructArrays also provides a LazyRow wrapper for lazy row iteration. LazyRow(t, i) does not materialize the i-th row but returns a lazy wrapper around it on which getproperty does the correct thing. This is useful when the row has many fields only some of which are necessary. It also allows changing columns in place.

julia> t = StructArray((a = [1, 2], b = ["x", "y"]));

julia> LazyRow(t, 2).a
2

julia> LazyRow(t, 2).a = 123
123

julia> t
2-element StructArray(::Array{Int64,1}, ::Array{String,1}) with eltype NamedTuple{(:a, :b),Tuple{Int64,String}}:
 (a = 1, b = "x")
 (a = 123, b = "y")

To iterate in a lazy way one can simply iterate LazyRows:

julia> map(t -> t.b ^ t.a, LazyRows(t))
2-element Array{String,1}:
 "x"
 "yy"

Advanced: structures with non-standard data layout

StructArrays support structures with non-standard data layout (where getproperty has been overloaded or where the constructors are non-standard). The user is required to provide an overloaded staticschema for their type (to give the names and types of the properties of a given type) as well as a createinstance method. Here is an example of a type MyType that has as properties either its field data or properties of its field rest (which is a named tuple):

using StructArrays
struct MyType{NT<:NamedTuple}
    data::Float64
    rest::NT
end

MyType(x; kwargs...) = MyType(x, values(kwargs))

Base.getproperty(b::MyType, s::Symbol) = s == :data ? getfield(b, 1) : getproperty(getfield(b, 2), s)

getnamestypes(::Type{NamedTuple{names, types}}) where {names, types} = (names, types)
getnamestypes(::Type{MyType{NT}}) where NT = getnamestypes(NT)

function StructArrays.staticschema(::Type{T}) where {T<:MyType}
    names, types = getnamestypes(T)
    NamedTuple{(:data, names...), Base.tuple_type_cons(Float64, types)}
end

function StructArrays.createinstance(::Type{T}, x, args...) where {T<:MyType}
    names, types = getnamestypes(T)
    MyType(x, NamedTuple{names, types}(args))
end

s = [MyType(rand(), a=1, b=2) for i in 1:10]
StructArray(s)

Advanced: mutate-or-widen style accumulation

StructArrays provides a function StructArrays.append!!(dest, src) (unexported) for "mutate-or-widen" style accumulation. This function can be used via BangBang.append!! and BangBang.push!! as well.

StructArrays.append!! works like append!(dest, src) if dest can contain all element types in src iterator; i.e., it mutates dest in-place:

julia> dest = StructVector((a=[1], b=[2]))
1-element StructArray(::Array{Int64,1}, ::Array{Int64,1}) with eltype NamedTuple{(:a, :b),Tuple{Int64,Int64}}:
 (a = 1, b = 2)

julia> StructArrays.append!!(dest, [(a = 3, b = 4)])
2-element StructArray(::Array{Int64,1}, ::Array{Int64,1}) with eltype NamedTuple{(:a, :b),Tuple{Int64,Int64}}:
 (a = 1, b = 2)
 (a = 3, b = 4)

julia> ans === dest
true

Unlike append!, append!! can also widen element type of dest array:

julia> StructArrays.append!!(dest, [(a = missing, b = 6)])
3-element StructArray(::Array{Union{Missing, Int64},1}, ::Array{Int64,1}) with eltype NamedTuple{(:a, :b),Tuple{Union{Missing, Int64},Int64}}:
 NamedTuple{(:a, :b),Tuple{Union{Missing, Int64},Int64}}((1, 2))
 NamedTuple{(:a, :b),Tuple{Union{Missing, Int64},Int64}}((3, 4))
 NamedTuple{(:a, :b),Tuple{Union{Missing, Int64},Int64}}((missing, 6))

julia> ans === dest
false

Since the original array dest cannot hold the input, a new array is created (ans !== dest).

Combined with function barriers, append!! is a useful building block for implementing collect-like functions.