VersionBenchmarks

A package to run benchmarks of different versions, branches, or commits of packages against each other on multiple Julia versions.

It is especially useful for measuring time to first plot (TTFP) or using latency of packages, because these can only be measured once per Julia session. VersionBenchmarks.jl runs each code file multiple times with a fresh Julia process for each run.

Here is a simple example testing latest master against v1.0.0 for the DataFrames package, once for the default julia command and once with Julia 1.9.

using VersionBenchmarks

df = benchmark(
    [
        Config("master", (name = "DataFrames", rev = "master")),
        Config("1.0", (name = "DataFrames", version = "v1.0.0")),
        Config("master", (name = "DataFrames", rev = "master"),
            `/Applications/Julia-1.9.app/Contents/Resources/julia/bin/julia`),
        Config("1.0", (name = "DataFrames", version = "v1.0.0"),
            `/Applications/Julia-1.9.app/Contents/Resources/julia/bin/julia`),
    ],
    "dataframes.jl",
    repetitions = 10,
)

The Config struct has three arguments.

1. A name that summarizes the packages being used.
2. A NamedTuple or a Vector{NamedTuple} (if multiple different packages should be tested together) that serve as input arguments for Pkg.PackageSpecs which should be installed. Typical keys are name, url, path, rev, version and subdir. For example, to use a local repository where you want to check a specific commit with hash "a7e55f3", you could use (path = "path/to/repo", rev = "a7e55f3").
3. Optionally, a command to run Julia (julia by default).

You can supply one code file or a vector of code files. Each file is run for n repetitions, with Configs alternating so that the samples for one Config are spaced apart in time. This guards against misinterpreting system performance fluctuations over time as significant differences between Configs.

There are two macros you can use in your test code. They measure timings and allocations and write the results out to the collecting process automatically.

The first macro is @vbtime which behaves similarly to @time, measuring runtime, garbage collection time, and allocations. This macro is run once with each repetition and can be used to measure things like using latency or time to first plot.

The second macro is @vbbenchmark which is a wrapper for BenchmarkTools.@benchmark. This macro should be used with any code that can be run multiple times per session, so everything that doesn't measure first-use compilation latencies. Because it already runs its code multiple times to get a more accurate estimate, it's not necessary to run it on each repetition. Therefore, @vbbenchmark runs only on repetition 1.

The example code file, dataframes.jl, could look like this. The using statement and the first DataFrame construction can only be measured once per repetition, so they use @vbtime. The DataFrame constructor can be measured with @vbbenchmark.

@vbtime "using" begin
    using DataFrames
end

@vbtime "first_df" begin
    DataFrame(a = 1:10)
end

@vbbenchmark "DataFrame" begin
    DataFrame()
end

You can call these functions on the resulting DataFrame:

VersionBenchmarks.summarize_repetitions(df)
VersionBenchmarks.plot_summary(df [, :time_s]) # can change second arg to :allocations or :gctime

For example:

VersionBenchmarks.plot_summary(df)

VersionBenchmarks.plot_summary(df, :allocations)