DataValues Inference

datavalues.jl

using DataValues
for n=1:20
  a=DataValueArray(rand(2^n))
  @time map(x->isnull(x) ? 0.0 : get(x), a);
end
  0.090726 seconds (15.83 k allocations: 838.608 KiB)
  0.000010 seconds (24 allocations: 1.219 KiB)
  0.000011 seconds (62 allocations: 3.156 KiB)
  0.000017 seconds (186 allocations: 9.531 KiB)
  0.000048 seconds (626 allocations: 33.531 KiB)
  0.000115 seconds (2.27 k allocations: 115.031 KiB)
  0.000460 seconds (8.64 k allocations: 434.031 KiB)
  0.001666 seconds (33.67 k allocations: 1.637 MiB)
  0.012756 seconds (132.87 k allocations: 6.383 MiB)
  0.066373 seconds (527.87 k allocations: 25.297 MiB, 55.52% gc time)
  0.317737 seconds (2.10 M allocations: 100.688 MiB, 65.82% gc time)
  2.003672 seconds (8.41 M allocations: 401.188 MiB, 79.76% gc time)
 10.821087 seconds (33.59 M allocations: 1.565 GiB, 85.94% gc time)
 62.074098 seconds (134.31 M allocations: 6.254 GiB, 90.62% gc time)


julia> a=DataValueArray(rand(100));

julia> typeof(map(x->isnull(x) ? 0.0 : get(x), a))
DataValues.DataValueArray{Any,1}

julia> typeof(map(x->isnull(x) ? 0.0 : x, a))
DataValues.DataValueArray{Float64,1}