Skip to content

JuliaAPlavin/UnionCollections.jl

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

20 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

UnionCollections.jl

Efficient collections (arrays and dictionaries) with Union element types. Compared to Vector{Union{...}}, more operations are type-stable: most notably, map().

Under the hood, elements of different types are stored in separate sub-collections. This allows for operations on the collection as a whole to be type-stable, while X[i] fundamentally remain type-unstable.

Usage

# create a regular vector and a union vector:
julia> V = Union{Int, String}[1, 2, "x", 3, "yy"]
5-element Vector{Union{Int64, String}} <...>

julia> A = unioncollection(V)
5-element UnionVector{Union{Int64, String}} <...>

# these two work basically the same, but the union vector is more efficient
## Vector:

julia> map(x -> x^3, V)
5-element Vector{Any}:
  1
  8
   "xxx"
 27
   "yyyyyy"

julia> @code_warntype map(x -> x^3, V)
Body::Union{Vector{Any}, Vector{Int64}, Vector{String}}

julia> @btime map(x -> x^2, map(x -> x^3, $V))
  3.445 μs (19 allocations: 792 bytes)

## UnionVector:

julia> map(x -> x^3, A)
5-element UnionVector{Union{Int64, String}, Tuple{Vector{Int64}, Vector{String}}}:
  1
  8
   "xxx"
 27
   "yyyyyy"

julia> @code_warntype map(x -> x^3, A)
Body::UnionVector{Union{Int64, String}}

julia> @btime map(x -> x^2, map(x -> x^3, $A))
  197.597 ns (8 allocations: 392 bytes)

Releases

No releases published

Packages

No packages published

Languages