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Posted 1 Mar 2013

AFP: Almost Functional Programming in C#, part 1

, 1 Mar 2013
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Introducing a programming style by creating an incremental calculation framework.


Functional programming is getting more and more interest nowadays, as the silver bullet solving all of the problems of humanity; the knife which can partition complexity into thin, manageable slices; the prophet who can show us the way out of the Kingdom of Nouns. Unfortunately, the proponents of Functional Programming usually only talk about the advantages of this paradigm, without concrete examples how it can be applied to real life problems. This article is an attempt to convince you that it is worth considering, especially when writing multi-threaded code.

If you are wondering what this FP is all about, it can be reassuring that you are already actively practicing it whenever you write LINQ queries. Not too many people disputes that LINQ is powerful, completely general and very easy to use. This power comes from the fact that there are general functions in the System.Linq namespace which can be tailored to the exact problem we are solving by passing in problem specific functions. With the elegant lambda notation of C# we can write very powerful algorithms, while still keeping the code succinct and easy to read. This similarity between LINQ and FP is not too surprising however, as LINQ's extension methods are very old functions from FP languages in disguise: Select is map(), Aggregate is reduce(), the map/reduce algorithm probably sounds familiar to you.

Functional Programming basics 

In this article we will use only the subset of Functional Programming, if you are interested how you can emulate most of the machinery which exist in FP languages, you can read this excellent article from Jovan Popovic: Functional programming in C#. There are two properties we are about to use from FP, pure functions and higher order functions. The latter is explained in the linked article, we are talking about functions taking other functions as parameters (or returning them as results). We will write generic algorithms expressed as these higher order functions, and will turn them into specific program code by using lambdas. Just like LINQ does.

Pure functions on the other hand are functions which always return the same result for the same parameters and have no side effects. The latter means that they cannot modify any state nor using any I/O, as that would be a side effect. (As we are not in the race for purity, we can break the no side effects rule for logging, but only for logging!) Pure functions are interesting for us since they have some very useful attributes (called referential transparency):

  1. As they depend only on the input parameters, they are usually easier to understand and easier to reason about. You do not have to check every method which can modify the object's state, everything is in front of you whenever you look at the source code of the function.

  2. They are trivial to test, you just pass in some values and compare the result to the expected one. If you pass in just values, there is no need to create fakes at all, you do not have to write “extracted” classes either.

  3. We can easily compose pure functions as the evaluation of those functions cannot interfere with each other. Compare this to the experience what you get when you call a simple function in an Object Oriented Programming style: after the call, every object you hold a reference to, can potentially be altered.

  4. If two pure functions do not depend on each other, so the result of one is not used as a parameter of the other, then we can evaluate them in any order. We can even execute them on different threads without locking as they cannot interfere with each other.

  5. Since we do not mutate object state (every field in every class is readonly), state is built from constant (immutable) values, which can be shared between multiple threads without any synchronization.

All the above sounds very convincing, however there is a small problem with pure functions is that they are good only for heating the room. Once we calculate some expression we have to throw away the result, since by definition we cannot store it anywhere, effectively wasting all the work done by the CPU, other than the heat it generated. Remember, pure functions have no side effects, we cannot write the result to an I/O device or store it in some variable! As the world is full of state, and our programs must model and handle that state, obviously every FP language developed some method coping with that. Haskell has pure functions and monads, which are little bags where all the non-pure garbage (like I/O) is stored. Clojure has pure functions and ref, which is a Software Transactional Memory implementation, which can hold multiple versions of values. Non-pure languages such as C# can mix and match pure and non-pure functions, and it is the responsibility of the developer to make sure that pureness and immutability constraints are not violated. In this article we will do exactly that, by separating state representing immutable objects, state processing pure functions and state holding mutable infrastructure.

The problem 

In order not to fall into the same trap as Functional Programming proponents usually do, the problem we will solve will not be an exclusively mathematical one but will have some twist. Everybody seems to be sick of factorials and such so here is some real world task. Imagine that we are running our pension fund and need real time data about the current value of our stock portfolio so we can compare it to the price of the current value of our gold pile under the bed, so we can decide whether to buy more gold or buy more stocks. The twist is that we want to run this price calculating algorithm within soft real-time time constraints to beat High Frequency Traders, which means that we cannot allocate too much memory for processing as it implies too much garbage collections. Garbage collections are considered bad things in soft real-time algorithms, as everybody hates if somebody else interrupts them during speech, you know what I mean.

As we are working with pure functions, and since pure functions cannot modify state, they have to create a new state. In order to avoid memory allocations, we will use value types to represent the state.

The value of our stock can be calculated by using the following very simple expression:

value := SUM(stock_price[i] * number_of_stock[i] * currency_exchange_rate[i])

The value of the gold is the same, we can assume a stock portfolio which has only one stock, the gold, and the stock price is the current gold price.

The problem is that we can have thousands of stocks in our portfolio and we have to recalculate this formula a million times per second in order to beat the HFT guys. We can reorder the expression, grouping by currencies like this:

value := SUM(SUM(stock_price[i] * number_of_stock[i]) * currency_exchange_rate[j])

That way we only have to recalculate one multiplication when the currency rate changes but we still have to add up all the numbers. If we think about the SUM function, we can notice that because addition is associative, the following is true:

a1+a2+a3 = (a1+a2)+a3

So if only a3 changes, and we somehow would remember what was the sum of a1+a2, then we would have to do only one addition instead of two. For 10000 values, it would mean doing one addition instead of 9999 and it is a huge difference. We can get a1+a2 by subtracting the old a3 value from the old sum, but it will result in worse and worse result quality as the rounding errors accumulate. It will not be generic enough either, as we will be able to use only functions which have inverse functions as well (like the subtraction is an inverse of addition).

The solution in Functional Programming is called Memoization, which is a way to remember the result of a function call, so when we call it next time with the same parameters, the result does not have to be recalculated, we can use the memoized one. Of course it can consume a lot of memory to remember everything, and it is also slower than adding two numbers together, so we will develop a simpler but faster version of it. This will be the infrastructure which will enable us to incrementally calculate anything, and will store and cache the results for us.

The solution

In order to be able to cache the last value of a sum, we will arrange every function into a tree hierarchy like an Abstract Syntax Tree. In case of a sum, we arrange like this:

a1+a2+a3+a4 = ( (a1+a2) + (a3+a4) )

Once we do not have to distinguish between simple operators like multiplication and list operations like SUM(), the whole problem is simplified to caching and updating. The following pictures show how the system works. 

Here you can see how we group (A+B)*(C+D), but we would group and handle A+B+C+D exactly the same way, only the result would be 50 instead of 400. A, B, C and D are Sources of the calculation as they provide values. A+B, C+D and (A+B)*(C+D) are Dependents of the sources above them but also Sources for the Dependents below them.


It easy to see that if we change one Source value (C from 11 to 1 in this case, shown with yellow), then we have to recalculate all Dependents. The number of calculations is the depth of the tree, so in the worst case, when every node has only two Sources, it will be log2(n). In our problem it means that in case of 10000 stocks, it will be 14 additions instead of 9999.


Every node in the tree maintains a Ready and a Cached flag, and it is very important not to mix these two states together. Ready means that the value of the calculation can be read from the node to be given as parameters to further calculations (which are pure functions themselves). In the graph Ready is shown as green. Cached means that we do not have to evaluate the calculation as we hold the last value. In the graph Cached is shown as a concrete value instead of question marks. Cached implies Ready, but Ready does not imply Cached!!!


In case of Sources, Ready means that the value they hold are valid. They can became not Ready only when we store an invalid value into them, like null. Not Ready is shown as red. Note that Ready is transitive, so if something is Ready, then it implies that all the Sources of the calculations are Ready, otherwise we could not calculate the value which would mean not Ready. In order to keep this invariant, when we make something not Ready then we have to make every Dependents not Ready. We stop only if we either reach the bottom of the tree, so there are no more Dependents, or when we encounter a node which is not Ready. In the latter case as Ready is already transitive, we can be sure that all of its Dependents are already not Ready. As you can see on the graph, all not Ready states imply not Cached, holding question marks.


Unlike as the second, gray/yellow graph suggested, when we change one Source, we do not have to recalculate everything until we reach the bottom, as it would exercise wasted calculations in case we would like to change several Sources simultaneously. So when we change B from 7 to 2 in this case, we just invalidate all Dependents (A+B becomes not Cached and (A+B)*(C+D) is already not Cached). We recalculate the value only when we access the value of (A+B)*(C+D), which in this case will throw an exception as it is not Ready. If it would be Ready, then accessing the value would recalculate and cache the values of A+B and C+D, then by using those values would recalculate and cache the value of (A+B)*(C+D).

So in the end we flow Ready/not Ready changes and Cached invalidation from top to bottom, and recalculate and cache values from bottom to top recursively.

The code

We define three minimal interfaces to capture the design we just described. 

interface ISource {
   bool IsReady { get; }
   void AddDependent(IDependent dependency, int key);
interface IValue<T> : ISource {
   T Value { get; }
interface IDependent {
   void Notify(int readyChange, int key);
const int BecomeNotReady = -1;
const int ValueChanged = 0;
const int BecomeReady = +1;  

ISource is implemented by every node which wants to be a Source in the graph. IsReady is the Ready state we talked about, AddDependent is used when building the graph. Since we do not want to change the graph once we have built it, there is no RemoveDependent here. Key is just a number

remembers, so when it calls Notify later, it can pass this value as the second parameter. The use case is when we put a lot of
references into an array, we can use this number as the index in the Notify callback.

IValue<T> is the typed version of ISource, Value recalculates the value of the node, or gives back the cached one. In case the IValue is not IsReady then it throws an exception.

IDependent is implemented by every node which wants to be a Dependent in the graph. Notify is called every time IsReady changes in one of the ISources, or when the value changes and this node must be invalidated. The constant numbers -1, 0 and +1 are important, as we do not check every ISource every time IsReady is called, we just keep an always up-to-date count of the not IsReady ones, so those constant values can be used to directly modify the counter. When the counter reaches 0, it means that the node became IsReady, otherwise it is not IsReady.

Now lets implement all the nodes in the graphs by implementing these interfaces. First the top row, which are the Source values, the root of all calculations. 

 public class Source<T> : IValue<T> {
   protected bool _isValid;
   protected T _value;
   protected DependentList _dependentList;
   public Source() {
       _isValid = false; _value = default(T);
       _dependentList = new DependentList();
   bool ISource.IsReady { get { return _isValid; } }
   void ISource.AddDependent(IDependent dependency, int key) { ... }
   public bool IsValid { get { return _isValid; } }
   public T Value {
       get { if (!_isValid) throw new Exception(); return _value; }
       set {
           if (!_isValid) {
               _value = value; _isValid = true;
           } else {
               _value = value;
   public void Invalidate() { ... }

In case of a Source<T>, IsReady is the same as IsValid. Reading Value does not involve recalculating and caching anything, as it is just a stored instance of T, whatever it is. Implementing AddDependent and Invalidate is trivial, not shown here. Note however the NotifyAll calls in the Value setter, which will call all the Notify handlers stored in DependentList in a list of IDependents. In case we just changed to !_isValid to _isValid, it uses BecomeReady for the notification, otherwise it uses ValueChanged. In the case of Invalidate, it uses BecomeNotReady as expected.

There is also a SourceValue<T> and a Constant<T> in the real code base, the former notifies Dependents only when the value not only set but also different from the prior value, while the latter is a constant, which is always IsReady.

The next things are the lower nodes in the graph, first creating Dependent, which handles notifications correctly, then the function nodes will come.

public abstract class Dependent : IDependent, ISource {
    protected int _notReadyCount;
    protected bool _isCached;
    protected readonly DependentList _dependentList;
    protected Dependent(params ISource[] sourceList)
        _notReadyCount = 0; _isCached = false;
        _dependentList = new DependentList();
        for (int i = 0; i < sourceList.Length; ++i) {
            sourceList[i].AddDependent(this, i);
            if (!sourceList[i].IsReady) ++_notReadyCount;
    bool ISource.IsReady { get { return _notReadyCount == 0; } }
    void IDependent.Notify(int readyChange, int key) {
        var oldReady = _notReadyCount == 0;
        _notReadyCount -= readyChange;
        var newReady = _notReadyCount == 0;
        if (oldReady != newReady) {
            _isCached = false;
            _dependentList.NotifyAll(newReady ? DependentList.BecomeReady : DependentList.BecomeNotReady);
        } else if (_isCached) {
            _isCached = false;

In the constructor we add ourselves as an IDependent to all ISource nodes, and make the invariant true, so that _notReadyCount is always equal with the count of the not IsReady ISource nodes. We keep this invariant true by the line: _notReadyCount -= readyChange; later.

The node is considered IsReady if and only if _notReadyCount == 0, which is trivial.

The Notify call simply forwards the state change, if IsReady changed then BecomeReady or BecomeNotReady (both imply ValueChanged), otherwise just

which invalidates the cached value in Dependents. Of course, it we was not Cached then there is no point forwarding the ValueChanged as our Dependents cannot be Cached either. 

public abstract class FunctionBase<R> : Dependent, IValue<R> {
    protected R _cachedValue;
    protected abstract void UpdateCached();
    public R Value {
        get {
            if (base._isCached)
                return _cachedValue;
            base._isCached = true;
            return _cachedValue;

FunctionBase<R> simply caches the result of a calculation, and if the value is cached then it does not update it by calling UpdateCached(). Note that it uses the inherited _isCached field from Dependent, as the base class sets the field to false in Notify, while FunctionBase sets it to true in the Value getter.

public sealed class Function<T1, T2, R> : FunctionBase<R> {
    IValue<T1> _param1;
    IValue<T2> _param2;
    Func<T1, T2, R> _func;
    public Function(IValue<T1> param1, IValue<T2> param2,
        Func<T1, T2, R> pureFunction) : base(param1, param2) {
        _param1 = param1; _param2 = param2; _func = pureFunction;
    protected override void UpdateCached() {
        base._cachedValue = _func(_param1.Value, _param2.Value);

Function<T1, T2, R> finally calculates the pure function f(T1,T2) -> R which is passed to it in the constructor and stored in _func. Of course we have to implement one such class for every arity (number of function arguments). 

static IValue<T> RecurseApply<T>(IList<IValue<T>> sourceList, Func<T, T, T> associativeFunction, int startIndex, int length)
    if (length == 1)
        return sourceList[startIndex];
    int newlen1 = length / 2;
    int newstart2 = startIndex + newlen1;
    int newlen2 = length - newlen1;
    return new Function<T, T, T>(
        RecurseApply(sourceList, associativeFunction, startIndex, newlen1),
        RecurseApply(sourceList, associativeFunction, newstart2, newlen2),

public static IValue<T> Apply<T>(IList<IValue<T>> sourceList, Func<T, T, T> associativeFunction)
    return RecurseApply(sourceList, associativeFunction, 0, sourceList.Count);

Finally this is the magic which turns the SUM() function into a tree. All it expects is the list we want to operate on (the numbers we want to add up), and the function which must be associative, and addition clearly is. Note that we do not have to use mathematical operations here, any function will do as long as it is associative, like string concatenation.

That is it, we have finished creating the state holding mutable infrastructure, the next step is putting it to good use by writing state representing immutable objects and state processing pure functions.


First we have to define a value type which holds the price of stocks. Unfortunately stocks have more than one prices, so a double will not suffice, we have to create a struct called Price. The reason to keep both prices is that when we want to sell stocks, we get only the lower BuyPrice, and when we want to buy stocks, we have to pay the higher SellPrice. Of course having two prices is also a good excuse to introduce immutable value types into this article.

public struct Price
    public readonly double BuyPrice;
    public readonly double SellPrice;

    public Price(double buyPrice, double sellPrice)
        BuyPrice = buyPrice;
        SellPrice = sellPrice;

    public static Price operator +(Price a, Price b)
        return new Price(a.BuyPrice + b.BuyPrice, a.SellPrice + b.SellPrice);

    public static Price operator *(Price a, double b)
        return new Price(a.BuyPrice * b, a.SellPrice * b);

As you can see, we also define addition and multiplication operators so the next piece of code will look like a little bit better. 

public class Portfolio : Model
    readonly Dictionary<string, Source<Price>> _stocks;
    readonly Dictionary<string, Source<double>> _rates;
    public readonly IValue<Price> Result;

    public Portfolio(IEnumerable<Tuple<string, int, string>> stocklist)
        _stocks = new Dictionary<string, Source<Price>>();
        _rates = new Dictionary<string, Source<double>>();

        var totallist = new List<IValue<Price>>();
        foreach (var stockDataGroup in stocklist.GroupBy(x => x.Item3)) {
            _rates.Add(stockDataGroup.Key, Source<double>());

            var addlist = new List<IValue<Price>>();

            foreach (var stockData in stockDataGroup) {
                var stockID = stockData.Item1;
                var stockNum = stockData.Item2;
                var currencyID = stockData.Item3;

                _stocks.Add(stockID, Source<Price>());

                var stockValueInCurrency = Function(
                    _stocks[stockID], Constant(stockNum), (stock, num) => stock * num);

            var sumInCurrency = Apply(addlist, (p1, p2) => p1 + p2);
            var stockSumInEUR = Function(
                sumInCurrency, _rates[stockDataGroup.Key], (sum, curr) => sum * curr);

        Result = Apply(totallist, (p1, p2) => p1 + p2);

This is the class which holds the complete expression we want to incrementally calculate. We derive from Model, which is a simple utility class containing wrappers around the constructors of Function, Source and similar things, just like Tuple.Create wraps the Tuple constructor. The reason of this is that type inference does not work in case of constructors, and without wrappers we would always had to use

Function<Price, int>(...)
instead of simply writing Function(...).

Looking at the code from the inside to the outside, we first define stockValueInCurrency, which is a simple multiplication we have just defined. We add those together by Applying addition into sumInCurrency, then multiply by the currency rate into stockSumInEUR. Finally we add all of them together by Applying addition again into Result. Note that in this constructor we do not calculate the value of the portfolio, just build a graph which will do that when we read Result.Value.

There is one interesting thing is that we use Constant to capture the value of stockNum. It is used here only for completeness, we can use its value directly in the lambda we pass to Function(), and in that case the C# compiler automatically allocates a closure object to hold it. If we do not capture any value in the lambda then the C# compiler will use a single static instance of the lambda, which one you prefer should depend only on your taste.

public void UpdateStockPrice(string stockID, Price price)
    _stocks[stockID].Value = price;

public void UpdateCurrencyRate(string currencyID, double rate)
    _rates[currencyID].Value = rate;

The remaining two methods simply update the Source values which will force the graph to invalidate itself so next time we read Result.Value, it will be recalculated.

class Program
    static void Main(string[] args)
        var p = new Portfolio(new[] {
            Tuple.Create("VOD LN", 100, "GBP"),
            Tuple.Create("BP LN", 1000, "GBP"),
            Tuple.Create("BMW GY", 200, "EUR"),
        p.UpdateCurrencyRate("EUR", 1.0);
        p.UpdateCurrencyRate("GBP", 1.3);
        p.UpdateStockPrice("VOD LN", new Price(60.0, 61.0));
        p.UpdateStockPrice("BP LN", new Price(55.1, 55.2));
        p.UpdateStockPrice("BMW GY", new Price(155.1, 155.2));
        if (p.Result.IsReady) {
        } else {
            Console.WriteLine("NOT READY");

The remaining code is very straightforward. If we remove any of the p.Update... function calls, we will see that the expression value is NOT READY.

Final words

Now if we would profile this application by updating big portfolios millions of times, we would notice that the majority of the time is spent in NotifyAll in a foreach loop. So in the real library code we have EmptyDependentList, SingleDependentList and MultiDependentList, which are optimized for 0, 1 and more elements. With this optimization we can process 3 million updates per second per core, which is considered good enough for our little pension fund. If it is not fast enough, we can reduce the tree depth and the number of Dependents by having 16 or 32 element fat nodes, and then we cache the result of the function applied to all elements in a loop. That way we would evaluate the pure functions 4-6 times more but overall it could still result in speed gains. Another possibility is to process the expression on multiple threads simultaneously, but of course in our simple <code><code><code><code>Portfolio model, thread synchronization would eat all the speed-ups. It can be considered for other algorithms though. Note that all of these optimization possibilities exist only because we decoupled storing data from calculating it using only pure functions, and as we know, pure functions can be evaluated in any order.  

If we look at the constructor of Portfolio, we can wonder how can we call it Functional Programming when everything we do is modifying the state of the object we are constructing? Another thing that can be strange is that everything in Source, Dependent and FunctionBase is good old Object Oriented Programming... 

Do you remember the title of the article? It is Almost functional programming. In the next part we will create a server to execute our little financial model, and we will discover what exactly the word Almost means in this context. We will also rewrite that imperative constructor and see if the result will become worse or better.  



This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)


About the Author

Andrew Rafas
Software Developer (Senior)
New Zealand New Zealand
I am a senior software developer with almost 20 years of experience. I have extensive knowledge of C#, C++ and Assembly languages, working mainly on Windows and embedded systems. Outside of work I am interested in a wider variety of technologies, including learning 20 programming languages, developing Linux kernel drivers or advocating Functional Programming recently.

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Comments and Discussions

Generalthis is what I am looking for long time Pin
Southmountain3-Oct-15 11:10
memberSouthmountain3-Oct-15 11:10 
GeneralRe: this is what I am looking for long time Pin
Andrew Rafas3-Oct-15 15:45
memberAndrew Rafas3-Oct-15 15:45 
Questionvery nice Pin
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professionalBillW3327-Jan-15 6:10 
AnswerRe: very nice Pin
Andrew Rafas1-Feb-15 12:06
memberAndrew Rafas1-Feb-15 12:06 
GeneralRe: very nice Pin
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