Loading CSV makes tab completion in the REPL extremely slow

[KristofferC]

Doing using CSV and then foo("<TAB in the REPL makes the REPL lag for ~10 seconds. Running with --trace-compile and timing the different precompile statement there is one that takes 10(!) seconds to run. MWE is:

julia> using CSV; using REPL; @time precompile(Tuple{typeof(REPL.REPLCompletions.completions), String, Int64})
 10.879856 seconds (25.56 M allocations: 1.508 GiB, 3.41% gc time)

Entering the Pkg REPL also takes like 20 seconds after using CSV.

[laborg]

See also: JuliaData/CSV.jl#537

[KristofferC]

Weird, on my 1.3.1 backport branch built locally it only takes 0.4 seconds... But downloading the binary from the buildbot with the same commit, it takes 30 seconds!

[KristofferC]

Ok, Jameson helped me figured that one out. The binary from the buildbot has asserts on so it is slower (significantly so in this particular case).

[KristofferC]

Ok, this seems fixed on 1.3.1 and master so I guess good to close 🤷‍♂ .

[evanfields]

@KristofferC I'm still seeing this behavior on 1.3.1 (macOS). Is this just an inevitable consequence of asserts always being enabled?

[KristofferC]

I'll try it again

[KristofferC]

Yes, this does indeed happen on 1.3.1 release. Strange, maybe julia asserts are accidentally enabled for releases(!)?

[KristofferC]

And now it seems to happen when building 1.3.1 locally as well so maybe it isn't about asserts. I have no idea what is going on, would be good for someone else to do some testing, perhaps I am just crazy.

[KristofferC]

Still happens in 1.4 RC1.

julia> using CSV; using REPL; @time precompile(Tuple{typeof(REPL.REPLCompletions.completions), String, Int64})
 49.933756 seconds (33.86 M allocations: 2.063 GiB, 2.78% gc time)
true

(different machine so numbers not directly comparable).

[timholy]

Possibly related: https://discourse.julialang.org/t/slowdown-after-package-load/33533

The weirdest part is that using MCMCChains on its own causes the problem, but if I first load all of MCMCChain's dependencies:

julia> using AbstractMCMC, AxisArrays, DataFrames, Distributions, KernelDensity, LinearAlgebra, Random, RecipesBase, Serialization, Showoff, SpecialFunctions, Statistics, StatsBase

julia> using MCMCChains

then it's fine.

[KristofferC]

Yeah, I have also noticed differences depending on order of package loads. The weird thing here is that I had julia builds when the case here didn't exist at all so there seems to be some pathological case that causes this to happen.