# Performance of Key/Value Collections for Updating

I have been working on a simulation engine that requires a key/value collection for holding the flow rates through a network as part of a Push-Relabel algorithm. This is the most performance-critical code in the engine, so I needed to find the fastest way to perform a lookup, update, and store for a key/value pair. The prevailing wisdom is to use a .NET Dictionary, but I was curious how the performance would compare to the F# Map type. A Map is backed by an AVL Tree and has a read and write performance of $O(log(n))$ while Dictionary is a Hash Table with an algorithmic complexity of $O(1)$ for reads and writes.

For my use case, I need to read a value from the collection, perform a minor update, and then update the value for the key in the collection.

dictionary[key] <- dictionary[key] + 1.0 // Trivial work example


## Test Setup

To make it easier to set up tests across various collection sizes in benchmarkDotNet, I defined an Enum Size that would indicate the size of the collection I wanted to test against.

// Enum for the different size
[<Struct>]
type Size =
| 10 = 0
| 100 = 1
| 1_000 = 2
| 10_000 = 3
| 100_000 = 4
| 1_000_000 = 5


The Enum cases will map to the index of the data set that I want to test against. I now define a Benchmark class to hold my tests and generate the necessary data.

type Benchmarks () =

// The number of lookups I will perform in each test
let lookupCount = 10
// A random number generator to create random indices
// into the collections.
let rng = Random 1337

// Lookup array to map Size -> Count
let sizeToCount =
[|
10
100
1_000
10_000
100_000
1_000_000
|]

// An array of different Maps for each size in Size
let maps =
sizeToCount
|> Array.map (fun count ->
Map [for i in 0 .. count - 1 -> string i, 0.0]
)

// An array of different Dictionaries for each size in Size
let dictionaries =
sizeToCount
|> Array.map (fun count ->
Dictionary [for i in 0 .. count - 1 -> KeyValuePair (string i, 0.0)]
)


I then add a member to the Benchmarks class called Size so that benchmarkDotNet can update the field to automatically test across each of the cases in the Size Enum.

[<Params(Size.10, Size.100, Size.1_000, Size.10_000,
Size.100_000, Size.1_000_000)>]
member val Size = Size.10 with get, set


When benchmarkDotNet runs, it will see that the Size property has been decorated with the different values we want it to test. It will run each of our tests with every value we decorate the Size property with.

I now create the test for the Map collections. I index into the maps array and retrieve the Map associated with the case of Size. I then retrieve the keys associated with the Size. This ensures that all of the keys we will lookup can be found in the collection. You will see that I use mutable on the map value and then return it at the end of the method. This is to ensure that the CLR doesn’t compile the work away. This is not how I would typically use a Map.

[<Benchmark>]
member b.Map () =
// We using mutation to ensure the compiler doesn't eliminate unnecessary work
let mutable map = maps[int b.Size]
let keys = keysForSize[int b.Size]

// We are making memory access pattern as predictable as possible
// to eliminate cache hits from the work of getting the key. We don't use
// IEnumerable to reduce the overhead.
for i = 0 to keys.Length - 1 do
let key = keys[i]
let newValue = map[key] + 1.0
map <- map.Add (key, newValue) // Do a minimal amount of work

map


We iterate through each key in the keys array associated with the size we are testing. I wanted to try more than one lookup, so I wasn’t getting unexpected performance benefits from the CPU being lucky for a lookup of a single value.

I create the same test for the Dictionary type. The work is the same, even though it looks slightly different. This is due to Dictionary having a different API than Map.

[<Benchmark>]
member b.Dictionary () =
let dictionary = dictionaries[int b.Size]
let keys = keysForSize[int b.Size]

for i = 0 to keys.Length - 1 do
let key = keys[i]
dictionary[key] <- dictionary[key] + 1.0 // Do a minimal amount of work

dictionary


I now define my main method and run the benchmarks.

[<EntryPoint>]
let main _ =

// I don't care about what Run returns so I'm ignoring it
let _ = BenchmarkRunner.Run<Benchmarks>()
0


Another thing worth mentioning is that I am restricted to the x64 platform, so I update the .fsproj of the project to make sure that I only build and test for x64.

<Project Sdk="Microsoft.NET.Sdk">

<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFramework>net6.0</TargetFramework>
<!--    Restricts to the x64 platform-->
<Platform>x64</Platform>
</PropertyGroup>

<ItemGroup>
<Compile Include="Program.fs" />
</ItemGroup>

<ItemGroup>
<PackageReference Include="BenchmarkDotNet" Version="0.13.1" />
<PackageReference Include="BenchmarkDotNet.Diagnostics.Windows" Version="0.13.1" />
</ItemGroup>

</Project>


I get the following table when I run these benchmarks with dotnet run -c Release.

Method Size Mean Error StdDev Median
Map 10 986.3 ns 19.51 ns 38.06 ns 972.5 ns
Dictionary 10 253.2 ns 3.57 ns 3.17 ns 252.4 ns
Map 100 2,062.4 ns 39.33 ns 36.79 ns 2,053.8 ns
Dictionary 100 325.2 ns 6.18 ns 6.34 ns 324.7 ns
Map 1_000 3,468.0 ns 68.38 ns 130.09 ns 3,394.7 ns
Dictionary 1_000 299.8 ns 5.99 ns 10.02 ns 298.1 ns
Map 10_000 4,383.1 ns 86.00 ns 80.45 ns 4,393.2 ns
Dictionary 10_000 322.4 ns 2.15 ns 1.79 ns 322.6 ns
Map 100_000 5,571.8 ns 90.32 ns 84.48 ns 5,569.3 ns
Dictionary 100_000 338.0 ns 2.71 ns 2.26 ns 338.6 ns
Map 1_000_000 7,695.1 ns 150.65 ns 263.86 ns 7,690.7 ns
Dictionary 1_000_000 369.2 ns 2.69 ns 2.10 ns 369.5 ns

This is in alignment with my expectations. Since Map is an immutable data structure, it needs to copy a significant amount of data when creating the updated Map. This scenario is one of the worst ways you can use a Map. Dictionary, on the other hand, is a mutable data structure, so in this case, all of the work is in computing the hash code to find the correct bucket in the backing array and then the equality check to make sure the key in the bucket is the same as the one you are looking up.

Map is a great data structure, but this is not the best use case for it. I knew this ahead of time, but it’s good to validate your assumptions.

## Even Faster?

Can we go even faster, though? You may notice that we have to look up the key twice for each update. Once to get the value so that we can add 1.0 to it and a second time to store the updated value. It’s all on this single line of code:

// First lookup is here to get the value to add 1.0 to it
//                             ↓
dictionary[key] <- dictionary[key] + 1.0
//          ↑
// Second lookup happens here to store the value


Wouldn’t it be nice if we didn’t have to do that work twice? What if instead of the Dictionary returning by value, it returns by reference? This way, we only need to perform the lookup once?

Now, some of you may start balking, saying that’s dangerous. You can create some hideous bugs if you misuse this. It’s difficult enough that you will not find it in the Dictionary class itself. You need to use a method found on the CollectionsMarshal class, in the System.Runtime.InteropServices namespace. The name of the method I want is GetValueRefOrAddDefault. This method has an unusual function signature, so I want to unpack what is happening.

CollectionsMarshal.GetValueRefOrAddDefault<'TKey,'TValue>(dictionary: Dictionary<'TKey,'TValue>, key: 'TKey, exists: byref<bool>) : byref<'TValue


F# does some interesting things for you implicitly regarding the ref types: byref, inref, and outref. I highly recommend you read the Microsoft docs on refs. The first time you read it, you may be confused. I was, but the more I work with the ref types, the more it makes sense.

Aside: F# is designed as a succinct, expressive, and efficient language. It sometimes gets a reputation for being slow. I will concede if you write entirely idiomatic F#, your performance may not be on the level of a C++ program. BUT, that’s not to say you can’t write fast F# code. F# has defaults and idioms, making it easier to compose correct programs quickly.

What people don’t talk about is that you can turn all the safeties in F# off if you want to. If want to drop down to raw native pointers, you can. F# forces you to be more explicit about wanting to violate the safeties which I think is a feature, not a hinderance.

The usual way of working with a .NET API which uses a byref as one of the parameters for the method in F# is to use a match...with statement to unpack the values. The most common method I use with this behavior is the TryGetValue method of Dictionary. TryGetValue has the following signature:

Dictionary.TryGetValue(key: string, value: byref<'T>) : bool


You will see that the method expects you to pass a byref<'T>. If the dictionary has the value, it will put it in the location byref<'T> points and return a true. If it does not find the value, it will not update the value the byref<'T> points to and returns false. Instead of declaring a byref<'T>, we instead use the match ... with syntax, and F# will implicitly do the work of creating the byref<'T> for us.

match dictionary.TryGetValue key with
| true, value -> () // Do something with value
| false, _ -> () // Do something without the value


value, in this case, will be the value that was found. It will NOT be a byref<'T> pointing to the value found. F# implicitly dereferences the byref for us. This implicit dereferencing is nice most of the time but, in our case, is the opposite of what we want. Therefore we must define our byrefs and pass them to the method.

// dictionary is a Dictionary<int, float>
// key is a int we are wanting to the look up the float for
let mutable wasFound = Unchecked.defaultof<_>
let valueRef = &CollectionsMarshal.GetValueRefOrAddDefault (dictionary, key, &wasFound)
//             ↑ Notice this &


wasFound is a byref<bool> that we pass to the method. You’ll notice that we are not giving in a byref<float> for the method to fill in for us. Instead, we are using the & operator prepended to the method to tell F# that we want it to return the byref for us, not the value. If we did not prepend the & to the method call, F# would implicitly dereference the byref for us. This is another case of the F# defaults leaning toward safety. Fortunately, we can turn the safeties off.

Now that we know how to work with the GetValueRefOrAddDefault method, we create a test and compare the performance to our other tests.

[<Benchmark>]
member b.DictionaryGetRef () =
let dictionary = dictionaries[int b.Size]
let keys = keysForSize[int b.Size]
let mutable wasFound = Unchecked.defaultof<_>

for i = 0 to keys.Length - 1 do
let key = keys[i]
let valueRef = &CollectionsMarshal.GetValueRefOrAddDefault (dictionary, key, &wasFound)
valueRef <- valueRef + 1.0

dictionary


When we want to add 1.0 to our value, you’ll notice that we don’t have to dereference the byref<float>. F# is doing that work for us. This contrasts with C++, where you would need to dereference a pointer explicitly.

We get the following result if we run our benchmarks with this new test.

Method Size Mean Error StdDev Median
Map 10 986.3 ns 19.51 ns 38.06 ns 972.5 ns
Dictionary 10 253.2 ns 3.57 ns 3.17 ns 252.4 ns
DictionaryGetRef 10 119.3 ns 1.58 ns 1.48 ns 119.0 ns
Map 100 2,062.4 ns 39.33 ns 36.79 ns 2,053.8 ns
Dictionary 100 325.2 ns 6.18 ns 6.34 ns 324.7 ns
DictionaryGetRef 100 124.5 ns 2.52 ns 3.10 ns 124.2 ns
Map 1_000 3,468.0 ns 68.38 ns 130.09 ns 3,394.7 ns
Dictionary 1_000 299.8 ns 5.99 ns 10.02 ns 298.1 ns
DictionaryGetRef 1_000 130.9 ns 2.52 ns 2.69 ns 130.1 ns
Map 10_000 4,383.1 ns 86.00 ns 80.45 ns 4,393.2 ns
Dictionary 10_000 322.4 ns 2.15 ns 1.79 ns 322.6 ns
DictionaryGetRef 10_000 141.4 ns 2.12 ns 1.77 ns 140.9 ns
Map 100_000 5,571.8 ns 90.32 ns 84.48 ns 5,569.3 ns
Dictionary 100_000 338.0 ns 2.71 ns 2.26 ns 338.6 ns
DictionaryGetRef 100_000 130.5 ns 1.13 ns 1.05 ns 130.2 ns
Map 1_000_000 7,695.1 ns 150.65 ns 263.86 ns 7,690.7 ns
Dictionary 1_000_000 369.2 ns 2.69 ns 2.10 ns 369.5 ns
DictionaryGetRef 1_000_000 152.1 ns 1.09 ns 0.91 ns 152.1 ns

We see that the GetValueRefOrAddDefault method approach is more than twice as fast. A word of warning before you go and rewrite how you use Dictionary though. The ref types in F# are managed pointers. This article by Konrad Kokosa gives you a glimpse into the implications of managed pointers. I strongly encourage you to read the article and check out his book Professional .NET Memory Management before you make extensive use of them.

## A Safer Approach

Instead of getting a reference to the value, we could wrap our values in a ValueWrapper class and store those in the Dictionary. This was proposed in a GitHub discussion where people were debating the addition of the GetValueRefOrAddDefault method. I decided to code one up and compare the performance out of curiosity.

type ValueWrapper<'T when 'T : struct> (value: 'T) =
member val Value = value with get, set


I test this approach; I now have to wrap my values in the ValueWrapper type.

let wrappedValueDictionaries =
sizeToCount
|> Array.map (fun count ->
Dictionary [for i in 0 .. count - 1 -> KeyValuePair (string i, ValueWrapper 0.0)]
)


And create a test for it…

[<Benchmark>]
member b.ValueWrappedDictionary () =
let valueWrappedDictionary = wrappedValueDictionaries[int b.Size]
let keys = keysForSize[int b.Size]

for i = 0 to keys.Length - 1 do
let key = keys[i]
let v = valueWrappedDictionary[key]
v.Value <- v.Value + 1.0 // Do a minimal amount of work

valueWrappedDictionary


We see that this WrappedValue approach is just as fast when we run the benchmarks.

Method Size Mean Error StdDev Median
Map 10 986.3 ns 19.51 ns 38.06 ns 972.5 ns
Dictionary 10 253.2 ns 3.57 ns 3.17 ns 252.4 ns
ValueWrappedDictionary 10 104.8 ns 1.99 ns 1.86 ns 104.0 ns
DictionaryGetRef 10 119.3 ns 1.58 ns 1.48 ns 119.0 ns
Map 100 2,062.4 ns 39.33 ns 36.79 ns 2,053.8 ns
Dictionary 100 325.2 ns 6.18 ns 6.34 ns 324.7 ns
ValueWrappedDictionary 100 132.3 ns 2.08 ns 1.95 ns 131.7 ns
DictionaryGetRef 100 124.5 ns 2.52 ns 3.10 ns 124.2 ns
Map 1_000 3,468.0 ns 68.38 ns 130.09 ns 3,394.7 ns
Dictionary 1_000 299.8 ns 5.99 ns 10.02 ns 298.1 ns
ValueWrappedDictionary 1_000 132.2 ns 1.06 ns 0.94 ns 132.2 ns
DictionaryGetRef 1_000 130.9 ns 2.52 ns 2.69 ns 130.1 ns
Map 10_000 4,383.1 ns 86.00 ns 80.45 ns 4,393.2 ns
Dictionary 10_000 322.4 ns 2.15 ns 1.79 ns 322.6 ns
ValueWrappedDictionary 10_000 150.8 ns 0.90 ns 0.80 ns 150.6 ns
DictionaryGetRef 10_000 141.4 ns 2.12 ns 1.77 ns 140.9 ns
Map 100_000 5,571.8 ns 90.32 ns 84.48 ns 5,569.3 ns
Dictionary 100_000 338.0 ns 2.71 ns 2.26 ns 338.6 ns
ValueWrappedDictionary 100_000 151.3 ns 0.75 ns 0.63 ns 151.4 ns
DictionaryGetRef 100_000 130.5 ns 1.13 ns 1.05 ns 130.2 ns
Map 1_000_000 7,695.1 ns 150.65 ns 263.86 ns 7,690.7 ns
Dictionary 1_000_000 369.2 ns 2.69 ns 2.10 ns 369.5 ns
ValueWrappedDictionary 1_000_000 153.3 ns 1.21 ns 1.13 ns 153.2 ns
DictionaryGetRef 1_000_000 152.1 ns 1.09 ns 0.91 ns 152.1 ns

The nice thing about this approach is that even when the Dictionary gets reorganized, our ValueWrapper will still point to the correct data piece. The downside is that this will allocate more memory since each ValueWrapper is an object that needs to be allocated on the heap. You also lose any cache locality benefits since the ValueWrapper objects could spread all over memory. We aren’t observing any downsides in this tiny benchmark, but it’s essential to be aware. There could be some performance implications in the context of a larger program.

If you feel like playing with the code yourself, you can find all the tests here. Let me know if you have ideas for going faster or other collections I should test. I can be found on Twitter @McCrews, or you can email matthew@crews.email.