Reduce invalidations when loading JuliaData packages#47889
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timholy
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Dec 13, 2022
| function hex2bytes end | ||
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| hex2bytes(s) = hex2bytes!(Vector{UInt8}(undef, length(s) >> 1), s) | ||
| hex2bytes(s) = hex2bytes!(Vector{UInt8}(undef, length(s)::Int >> 1), s) |
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Possibly controversial. However, JuliaHub does not list an InfiniteStrings package (there is an InifiniteArrays package).
quinnj
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Dec 13, 2022
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Dec 13, 2022
timholy
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KristofferC
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Dec 14, 2022
This was referenced Dec 14, 2022
aviatesk
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aviatesk
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Dec 14, 2022
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The doctest failure seems to come from changes in this PR? |
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Hmm, passes for me locally. |
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They're failing only on the new AWS runners, I'm not sure those are good doctests if results can be slightly different on different CPUs |
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(cherry picked from commit e84634e)
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This fixes some invalidations that hinder both CSV (@quinnj) and DataFrames (@bkamins and @nalimilan). Both packages were benchmarked in the discussion of #47184 and @giordano noted that DataFrames had a large load-time regression.
This PR, on top of #47184, together with JuliaLang/Pkg.jl#3275 delivers an unqualified gain in the upcoming Julia 1.9 (workloads are defined in detail farther below):
using CSVCSV.File(...)using DataFrames...The substantial load-time penalty on "1.9" with just #47184 is explained by the fact that
Base.requireis among the invalidated targets, and therefore has to be recompiled while DataFrames is being loaded. This PR fixes that.Here are the workloads:
using CSV:@time using CSVCSV.File(...):@time @eval CSV.File(joinpath(pkgdir(CSV), "test", "testfiles", "precompile.csv"))using DataFrames...:@time begin using PooledArrays: PooledArrays, PooledArray; using DataFrames, Statistics; endDataFrames TTFX: uses the precompile workload.
CC @vchuravy, @vtjnash
@bkamins, one thing I also noted is that loading both DataFrames and CSV (either before or after, order shouldn't matter) invalidates some of the code in DataFrames. Happy to consult with you about fixing it if you need help.
precompile_blockersseems useful in this context, as it led me directly to some DataFrames code that wasn't very inferrable.