mruoss

mruoss

Guards vs. generated functions: which is faster/more performant?

Hey there

Regarding performance, is there a difference between guards and generated functions using pattern matching? Or does the compiler generate the same byte code for both?

And if there is a difference, which one is more performant?

Using Guards

defmodule UsingGuards do
  def do_work(arg) when arg in big_list do
    # ...
  end
end

Generated Functions

defmodule UsingFunctionGeneration do
  for arg in big_list do
    def do_work(unquote(arg)) do
      # ...
    end
  end
end

Most Liked

mruoss

mruoss

So I wrote the following simple Elixir script that uses benchee. I did not give it much thought to be honest. Let me know what you think.

TLDR; Performance for both approaches indeed seem to be equivalent.

Script

defmodule Test do
  @numbers Range.to_list(1..1000)

  def work(input, fun) do
    Enum.map(input, fun)
  end

  for number <- @numbers do
    def do_work_generated(unquote(number)), do: unquote(number)
  end

  def do_work_guards(number) when number in @numbers, do: number
end

Mix.install([:benchee, :benchee_markdown])
list_of_random_numbers = fn -> Enum.map(1..500_000, fn _ -> :rand.uniform(1000) end) end

inputs =
  list_of_random_numbers
  |> Stream.repeatedly()
  |> Stream.take(5)
  |> Stream.with_index()
  |> Stream.map(fn {numbers, idx} -> {"Batch #{idx + 1}", numbers} end)

Benchee.run(
  %{
    "Generated Functions" => fn input -> Test.work(input, &Test.do_work_generated/1) end,
    "Guards" => fn input -> Test.work(input, &Test.do_work_guards/1) end
  },
  memory_time: 60,
  inputs: inputs,
  formatters: [
    {Benchee.Formatters.Markdown, file: "BENCHMARK.md"},
    Benchee.Formatters.Console
  ]
)

Output

Configuration

Benchmark suite executing with the following configuration:

:time 5 s
:parallel 1
:warmup 2 s

Statistics

Input: Batch 1

Run Time

Name IPS Average Devitation Median 99th %
Guards 177.07 5.65 ms ±4.15% 5.62 ms 5.87 ms
Generated Functions 175.18 5.71 ms ±4.30% 5.67 ms 6.30 ms

Run Time Comparison

Name IPS Slower
Guards 177.07  
Generated Functions 175.18 1.01x

Memory Usage

Name Average Factor
Guards 7.63 MB  
Generated Functions 7.63 MB 1.0x

Input: Batch 2

Run Time

Name IPS Average Devitation Median 99th %
Guards 174.66 5.73 ms ±4.56% 5.69 ms 6.14 ms
Generated Functions 174.12 5.74 ms ±4.46% 5.71 ms 6.19 ms

Run Time Comparison

Name IPS Slower
Guards 174.66  
Generated Functions 174.12 1.0x

Memory Usage

Name Average Factor
Guards 7.63 MB  
Generated Functions 7.63 MB 1.0x

Input: Batch 3

Run Time

Name IPS Average Devitation Median 99th %
Guards 174.93 5.72 ms ±4.77% 5.68 ms 6.23 ms
Generated Functions 174.21 5.74 ms ±4.07% 5.71 ms 5.96 ms

Run Time Comparison

Name IPS Slower
Guards 174.93  
Generated Functions 174.21 1.0x

Memory Usage

Name Average Factor
Guards 7.63 MB  
Generated Functions 7.63 MB 1.0x

Input: Batch 4

Run Time

Name IPS Average Devitation Median 99th %
Guards 174.18 5.74 ms ±4.48% 5.71 ms 6.16 ms
Generated Functions 172.05 5.81 ms ±4.21% 5.79 ms 6.14 ms

Run Time Comparison

Name IPS Slower
Guards 174.18  
Generated Functions 172.05 1.01x

Memory Usage

Name Average Factor
Guards 7.63 MB  
Generated Functions 7.63 MB 1.0x

Input: Batch 5

Run Time

Name IPS Average Devitation Median 99th %
Generated Functions 174.62 5.73 ms ±4.37% 5.69 ms 6.19 ms
Guards 174.34 5.74 ms ±4.52% 5.71 ms 6.10 ms

Run Time Comparison

Name IPS Slower
Generated Functions 174.62  
Guards 174.34 1.0x

Memory Usage

Name Average Factor
Generated Functions 7.63 MB  
Guards 7.63 MB 1.0x
LostKobrakai

LostKobrakai

in/2 doesn’t compile to an Enum.member?/2 call when used in a guard:

LostKobrakai

LostKobrakai

You can use GitHub - michalmuskala/decompile · GitHub to take a look how things will look at lower levels.

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