In a previous post I talked about how we at work simplified composition in an F# project because we found that we took a performance hit when we used monadic binds in our code.
I was a bit surprised how expensive the construct was so I wanted to dig deeper into it with this article.
The setup
Composition in F# can take a number of forms. Below we will take a closer look at the simple straight forward pipe (|>) operator and compare it to the Kleisli composition and monadic bind composition operating on the Result type.
In other words, we will compare the following operators:
// Monadic bind
let (>>=) m f =
Result.bind f m
// Monadic bind inline
let inline (>>==) m f =
Result.bind f m
// Kleisli
let inline (>=>) a b x =
match a x with
| Ok v -> b v
| Error e -> Error e
To test we compose five different functions. All the functions do is copy some data structure:
type Data =
{ Property1: string
Property2: int
Property3: DateTime
Property4: float
Property5: decimal }
let processA data = { data with Property1 = "some new string" }
let processB data = { data with Property2 = 100 }
let processC data = ...
let processResultA data = Ok { data with Property1 = "some new string" }
let processResultB data = Ok { data with Property2 = 100 }
let processResultC data = ...
We use function processData for testing |> and processDataResult for testing the compositions that operate on Result.
Now we can create the functions we wish to time:
let withPiping data =
data
|> processA
|> processB
|> processC
|> processD
|> processE
let withBinding data =
data
|> processResultA
|> Result.bind processResultB
|> Result.bind processResultC
|> Result.bind processResultD
|> Result.bind processResulte
let withOperatorWithoutInlining data =
data
|> processResultA
>>= processResultB
>>= processResultC
>>= processResultD
>>= processResultE
let withOperatorWithInlining data =
data
|> processResultA
>>== processResultB
>>== processResultC
>>== processResultD
>>== processResultE
let withKleisli data =
data
|> (processResultA
>=> processResultB
>=> processResultC
>=> processResultD
>=> processResultE)
Note that withBinding, withOperatorWithoutInlining and withOperatorWithInlining are essentially the same but as we will see later there is a difference in performance between them.
Let's also define a function for timing a number of calls to a function f:
let timeFunction f data =
let sw = Stopwatch()
sw.Start()
[1 .. 10_000_000]
|> List.iter (fun _ -> f data |> ignore)
sw.Stop()
sw.Elapsed.TotalMilliseconds
That way we can time 10 million calls to a function like so:
data |> timeFunction withKleisli
To get a smaller variance on those 10 millions calls, let us define a run function that runs timeFunction a number of times and prints the average:
let run f fName =
let data =
{ Property1 = "some string"
Property2 = 42
Property3 = DateTime.Today
Property4 = 23.2
Property5 = 23m }
[1 .. 100]
|> List.averageBy (fun i -> timeFunction f data)
|> print fName
Now we can write the final setup:
run withPiping (nameof withPiping)
run withBinding (nameof withBinding)
run withOperatorWithInlining (nameof withOperatorWithInlining)
run withOperatorWithoutInlining (nameof withOperatorWithoutInlining)
run withKleisli (nameof withKleisli)
Granted, the setup is a bit lame but it does give some indication of differences in performance between the different types of composition.
The results are in
They say that an image is worth a thousand words, so here is a graph for you. I set withPiping as index 100, so the graph shows how the other methods compare to withPiping.
It shouldn't come as a surprise that withPiping is the fastest.
I am a bit surprised that withBinding is that much faster than withOperatorWithoutInlining, i.e. the >>= operator.
Kleisli composition is surprisingly slow, but monadic bind is more useful in F# anyways so you would probably not use Kleisli that often.
Code
I put the code on Github if you want to have a look. I created it on .NET 6 RC using the preview of Visual Studio 2022.
Top comments (2)
Have you considered using github.com/dotnet/BenchmarkDotNet for this comparison? There's a bit of a learning curve to get started with it, and it can take a while to run, but the reward is more robust, statistically significant results.
Nice, thank you. I did not know about that one.