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This post documents a problem with a very basic use case of JuliaDB.
using JuliaDB
acs = loadtable("psam_pusa.csv", type_detect_rows=200)
save(acs, "test")
yields an 8GB file, although psam_pusa.csv is only 4GB. The infered types are two Strings, many Int64s and many Union{Missing,Int64}s.
acs = loadtable("C:\\Users\\Max\\Desktop\\psam_pusa.csv",
colparsers = vcat(String, repeat([Union{Missing,Int64}], 95), String, repeat([Union{Missing,Int64}], 30), String, repeat([Union{Missing,Int64}], 158)))
yields a 2.5GB file.
Does the column type inference work properly? Or is it a storage problem of JuliaDB.
I am on Julia 1.1.0, TextParse 0.9.1+, and JuliaDB 0.12
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