Dictionary-based arrays, useful to represent wide heterogeneous tables while enjoying the familiar Julia collection and Tables interfaces.

Use DictArrays when you need a lean table type, but the compilation overhead of type-stable solutions (Vector{NamedTuple}, NamedTuple{Vector}, StructArray) is too much.

DictArrays are similar to StructArrays and have the same interface where possible, with the defining difference that DictArrays do not encode columns in the table type. This get rids of the prohibitive compilation overhead for wide tables with 100s of columns or more. Despite the inherent type instability, regular Julia data manipulation functions such as map and filter are fast for DictArrays: almost no overhead compared to StructArrays, orders of magnitude faster than plain Vectors of Dicts.

DictArrays are fast even for tables with hundreds of thousands of columns:

# 1000 columns - almost instant
julia> da = @time DictArray(Dictionary(Symbol.(:a, 1:10^3), fill(1:1, 10^3)))
  0.001211 seconds (5.50 k allocations: 313.422 KiB)

# StructArrays struggle:
julia> @time StructArray(da);
  7.496190 seconds (626.85 k allocations: 37.730 MiB, 0.30% gc time, 99.52% compilation time)

# DictArray compilation doesn't depend on the number of columns
# even absurd hundreds of thousands of columns are fine:
julia> @time DictArray(Dictionary(Symbol.(:a, 1:10^5), [fill(1:1, 2*10^4); fill([1.], 2*10^4); fill([:a], 2*10^4); fill(["a"], 2*10^4); fill([false], 2*10^4)]))
  0.228542 seconds (878.81 k allocations: 39.484 MiB, 11.63% gc time, 52.54% compilation time)

At the same time, common Julia functions such as map and filter work, and are performant for long and wide tables despite the inherent type-instability:

julia> da = DictArray(a=1:10^6, b=collect(1.0:10^6), c=fill("hello", 10^6));

# DictArray
julia> @btime map(x -> x.a + x.b, $da)
  1.430 ms (300 allocations: 7.65 MiB)

# baseline: StructArray
# basically the same timings
julia> @btime map(x -> x.a + x.b, $(StructArray(da)))
  1.314 ms (2 allocations: 7.63 MiB)

# baseline: plain Vector of Dictionaries
# orders of magnitude slower, many allocations
julia> @btime map(x -> x.a + x.b, $(collect(da)))
  100.512 ms (1000022 allocations: 22.89 MiB)

DictArrays follow array-like collection interfaces. They are not AbstractArrays though: this is a deliberate decision so that not to trigger generic AbstractArray fallbacks anywhere. Type instability is fundamental to the design, and requires explicit function barriers for performance.

Still, lots of common functionality works as you would expect for an array of NamedTuples: length(da), da[5], da[5].colname, keys(da), map, filter, and others. StructArray-like behavior is also available with the same semantics: most notably, da.colname to retrieve the whole column.

DictArray is a Tables.jl-compatible table type. It can be constructed from a table, or passed anywhere a table is expected.

julia> da = CSV.read(IOBuffer("a,b,c\n1,2,3\n4,5,6\n7,8,9\n"), DictArray)
DictArray({:a = [1, 4, 7], :b = [2, 5, 8], :c = [3, 6, 9]})

julia> da.a
3-element Vector{Int64}:
 1
 4
 7

julia> Tables.rowtable(da)
3-element Vector{NamedTuple{(:a, :b, :c), Tuple{Int64, Int64, Int64}}}:
 (a = 1, b = 2, c = 3)
 (a = 4, b = 5, c = 6)
 (a = 7, b = 8, c = 9)

Column selection:

• da[Cols(:a, :b, :c)]::StructArray (varargs or tuple of columns) selects these columns and converts to a StructArray of NamedTuples
• da[Cols([:a, :b, :c])]::DictArray (array of columns) selects these columns and keeps it a DictArray

Conversion:

• Dictionary(da) retrieves the underlying dictionary of columns
• Dict(da), NamedTuple(da) convert to the corresponding type
• StructArray(da) converts to a StructArray of NamedTuples without copying columns

Modification: uses Accessors, same interface as StructArray.

• @set da.colname = 1:length(da) replace a column
• @insert da.colname = ... insert a new column
• @delete da.colname delete a column
• Properties() are supported. Eg, normalize all numeric columns:

@modify(da |> Properties() |> If(c -> eltype(c) <: Number)) do col
    col .- mean(col)
end