Julia: writing matrix with NaN values to binary file - julia

Question

I have a matrix containing Float64's and possibly some NaN entries. I would like to save it to file as a file in binary (as this is fastest and speed is important here) e.g.,
io = open(string(saveto_dir,"/arr"),"w")
#time write(io,arr)
close(io)
However Julia gives the error message:
`write` is not supported on non-isbits arrays
Is there a workaround?

Answer 1

It sounds like this is due to (perhaps inadvertently) using an Array of type Any (i.e., Array{Any}), but if your data is as described you can use an Array{Float64} instead, in which case you will not have this problem.
To give a concrete example, writing
arr = Any[1.0, NaN]
io = open("./arr","w")
write(io,arr)
close(io)
gives exactly the error you describe, but if you just change that to
arr = Float64[1.0, NaN]
io = open("./arr","w")
write(io,arr)
close(io)
there is no error.
In general, one should vehemently avoid Array{Any} (or really anything involving Any) when there is any other practicable option, effectively due to type instability. For example:
julia> A = rand(10000); # Array{Float64}
julia> B = Any[A...]; # Array{Any}
julia> using BenchmarkTools
julia> #benchmark sum($A)
BechmarkTools.Trial: 10000 samples with 10 evaluations.
Range (min … max): 1.318 μs … 12.705 μs ┊ GC (min … max): 0.00% … 0.00%
Time (median): 1.347 μs ┊ GC (median): 0.00%
Time (mean ± σ): 1.400 μs ± 269.376 ns ┊ GC (mean ± σ): 0.00% ± 0.00%
▇█▅▂▂▂ ▃ ▂▂ ▁ ▂
██████▇▆▁▅▅███▇▁▃▃▁▁██▆▅▃▄▃▃▁▁▁▁▁▁▁▁▁▁▁▁▃▁▃▃▁▃▁▁▃▁▁▁▃▁▁▃▁▇█ █
1.32 μs Histogram: log(frequency) by time 2.62 μs <
Memory estimate: 0 bytes, allocs estimate: 0.
julia> #benchmark sum($B)
BechmarkTools.Trial: 10000 samples with 1 evaluations.
Range (min … max): 185.967 μs … 1.664 ms ┊ GC (min … max): 0.00% … 75.01%
Time (median): 195.609 μs ┊ GC (median): 0.00%
Time (mean ± σ): 213.615 μs ± 62.394 μs ┊ GC (mean ± σ): 1.31% ± 4.62%
▅██▅▄▅▃▃▂▂▄▃▁▂▁▁▂▄▁▁ ▃▃ ▂ ▂
███████████████████████▇████▆▆▇▆▄▄▄▃▄▃▅▄▄▅▄▁▅▃▄▅▅▃▄▃▃▄▄▅▁▄▅█ █
186 μs Histogram: log(frequency) by time 413 μs <
Memory estimate: 156.23 KiB, allocs estimate: 9999.
About a 100x performance difference is fairly typical (notice also the difference in number of allocations).