JuliaData / JuliaData/JSONTables.jl
arraytable significantly slows down when passed table with heterogenous columns
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- Julia
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Description
Here is the test code:
using JSONTables, DataFrames
results = DataFrame(cols=Int[], rows=Int[], arraytable=Float64[], objecttable=Float64[])
for cols in (100, 200, 300, 400, 500), rows in (10^3, 10^4, 10^5)
@show (cols, rows)
df = DataFrame(ones(rows, cols))
df[!, 1] .= "a"
df[!, 2] .= 1
df[!, 3] .= true
open(io -> objecttable(io, df), "test.json", "w")
x1 = @elapsed open(io -> arraytable(io, df), "test.json", "w")
open(io -> objecttable(io, df), "test.json", "w")
x2 = @elapsed open(io -> objecttable(io, df), "test.json", "w")
push!(results, [cols, rows, x1, x2])
end
and here is the benchmark result:
julia> results
15×4 DataFrame
│ Row │ cols │ rows │ arraytable │ objecttable │
│ │ Int64 │ Int64 │ Float64 │ Float64 │
├─────┼───────┼────────┼────────────┼─────────────┤
│ 1 │ 100 │ 1000 │ 0.178669 │ 0.0328257 │
│ 2 │ 100 │ 10000 │ 1.65927 │ 0.26272 │
│ 3 │ 100 │ 100000 │ 16.2332 │ 2.36529 │
│ 4 │ 200 │ 1000 │ 0.349468 │ 0.0498271 │
│ 5 │ 200 │ 10000 │ 3.49195 │ 0.595482 │
│ 6 │ 200 │ 100000 │ 34.8853 │ 4.9485 │
│ 7 │ 300 │ 1000 │ 0.547324 │ 0.0803132 │
│ 8 │ 300 │ 10000 │ 5.16746 │ 0.759614 │
│ 9 │ 300 │ 100000 │ 52.3498 │ 7.48296 │
│ 10 │ 400 │ 1000 │ 0.714898 │ 0.104794 │
│ 11 │ 400 │ 10000 │ 6.91257 │ 1.00389 │
│ 12 │ 400 │ 100000 │ 73.8235 │ 11.4878 │
│ 13 │ 500 │ 1000 │ 0.947894 │ 0.146453 │
│ 14 │ 500 │ 10000 │ 10.5129 │ 1.42812 │
│ 15 │ 500 │ 100000 │ 94.0811 │ 13.1131 │
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by running the provided Julia benchmark and compare the arraytable and objecttable entry points on heterogeneous DataFrames. Trace the arraytable path to identify why its runtime grows so much faster, then verify the change against the same combinations of columns and rows. Done means arraytable handles heterogeneous columns without the reported performance gap.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- json, julia
- Domain
- data, performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100