Elixir Today: Combining a List of Map via Specific Field

Photo by Melyna Valle on Unsplash

Elixir Today: Combining a List of Map via Specific Field

And how to use Benchee as your benchmark tool in Elixir

Pau Riosa's photo
Pau Riosa
·Apr 8, 2022·

3 min read

Subscribe to my newsletter and never miss my upcoming articles

References

references

Intro

Recently, I've been working on a similar question about How to combine a list of map via specific field.

I looked at the elixir forum and gladly I found something. I saw that one of the answers there is using Enum.group_by + Enum.map.

Given my curiosity, How can I come up with a solution using Enum.reduce?

Curiosity Kicks In

So I started working out using Enum.reduce.


 @list [
    %{id: 1, list: [1]},
    %{id: 1, list: [2]},
    %{id: 2, list: [1]},
    %{id: 3, list: [1]},
    %{id: 1, list: [3, 4, 5]},
    %{id: 2, list: [2, 3]},
    %{id: 3, list: [2, 3, 4, 5]},
    %{id: 4, list: [2, 3, 4, 5]},
    %{id: 4, list: [1]}
  ]

    @list
    |> Enum.reverse()
    |> Enum.reduce(%{}, fn %{id: id, list: _list} = f, acc ->
      case acc do
        %{^id => existing} -> Map.put(acc, id, [f | existing])
        %{} -> Map.put(acc, id, [f])
      end
    end)
    |> Enum.map(fn {id, list} -> %{id: id, list: Enum.flat_map(list, & &1.list)} end)
  1. In this way, I created a sample list with specific id and their list.
  2. I reverse the order of the list
  3. I used Enum.reduce to sort out and combine the list of the ids accordingly.
  4. Finally, used Enum.map to transform the data.

Benchmarking!

I started to compare the result between using group_by and reduce. In order to that, I used Benchee.

To install, add benchee to your dependencies.

{:benchee, "~> 1.0", only: :dev}

Given the setup

 @list [
    %{id: 1, list: [1]},
    %{id: 1, list: [2]},
    %{id: 2, list: [1]},
    %{id: 3, list: [1]},
    %{id: 1, list: [3, 4, 5]},
    %{id: 2, list: [2, 3]},
    %{id: 3, list: [2, 3, 4, 5]},
    %{id: 4, list: [2, 3, 4, 5]},
    %{id: 4, list: [1]}
  ]

def groupby(list) do
    Enum.group_by(list, & &1.id)
    |> Enum.map(fn {id, list} -> %{id: id, list: Enum.flat_map(list, & &1.list)} end)
end

def reduce(list) do
    list
    |> Enum.reverse()
    |> Enum.reduce(%{}, fn %{id: id, list: _list} = f, acc ->
      case acc do
        %{^id => existing} -> Map.put(acc, id, [f | existing])
        %{} -> Map.put(acc, id, [f])
      end
    end)
    |> Enum.map(fn {id, list} -> %{id: id, list: Enum.flat_map(list, & &1.list)} end)
end

Then run

Benchee.run(
   %{
        "groupby_enum_map" => fn -> PracticeElixir.groupby(@list) end,
        "reduce_enum_map" => fn -> PracticeElixir.reduce(@list) end
      },
      print: [fast_warning: false]
)

Benchmark Result

Given my dev environment, here is the result.

available_memory: "16 GB",
cpu_speed: "Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz",
elixir: "1.12.3",
erlang: "24.0.6",
num_cores: 16,
os: :macOS
Benchmark suite executing with the following configuration:
warmup: 2 s
time: 5 s
memory time: 0 ns
reduction time: 0 ns
parallel: 1
inputs: none specified
Estimated total run time: 14 s

Benchmarking groupby_enum_map ...
Benchmarking reduce_enum_map ...

Name                       ips        average  deviation         median         99th %
reduce_enum_map         1.08 M        0.93 μs   ±208.21%        0.80 μs           5 μs
groupby_enum_map        0.97 M        1.03 μs   ±251.03%        0.90 μs           4 μs

Comparison:
reduce_enum_map         1.08 M
groupby_enum_map        0.97 M - 1.12x slower +0.109 μs

You can view the full implementation here!

Happy Coding!

 
Share this