Category Archives: Scala

Functional Domain Programming Cookbook

Posted on by

In this article, I will demonstrate how Typed Functional Programming can be used to effectively represent a domain. We will walk through a step-by-step example where different domains are modeled using types and subtypes, and functions are used to transform one domain into another.

Let’s consider the process of making a cake as described in a recipe. The first step is the list of ingredients.

sealed trait Ingredient
case object Flour extends Ingredient
case object BakingPowder extends Ingredient
case object Water extends Ingredient


This list is defined in the recipe, but we cannot be certain that we have all these ingredients in our kitchen. To express this uncertainty, we create another type:

sealed trait NeededIngredient {
  val ingredient: Ingredient
}
case class MissingIngredient(ingredient: Ingredient)
  extends NeededIngredient
case class PresentIngredient(ingredient: Ingredient)
  extends Ingredient


With these types, we can define the first operation needed to start the cake:

def getIngredients(ingredients: Ingredient*): List[NeededIngredient]


The result is a list of NeededIngredient, which can be either present or missing. While we could have used the standard library’s Option type, it wouldn’t offer the same level of expressivity. In this case, we explicitly want to know which ingredient is missing; a None would have hidden that information.

The next part of the recipe involves mixing those ingredients.

def checkAndMix(ingredients: List[NeededIngredient]):
  Either[MissingIngredients, Mixture]


The checkAndMix function results in either a successful mixture or a collection of missing ingredients.

case class Mixture(ingredients: List[Ingredient])
case class MissingIngredients(ingredients: List[Ingredient])


We could have represented this result differently. Instead of Either, we could have created a custom sealed trait:

sealed trait MixtureResult
case class FullMixture(ingredients: List[Ingredient])
  extends MixtureResult
case class PartialMixture(present: List[Ingredient],
                          missing: List[Ingredient])
  extends MixtureResult


While this looks elegant, it is actually invalid for our domain: if you don’t have all the ingredients for a cake, you don’t start mixing – that would be a waste!

Either is the correct solution here because it represents a domain disjunction. This means two completely different domains have only one thing in common: they are both valid results of the function, but they cannot exist at the same time.

The final step is baking the mixture.

def bake(mixture: Mixture): Cake


This function only takes a Mixture as input, which is the correct definition from a domain perspective. We want to express that we can only bake after we have successfully mixed the ingredients. The Either type is perfect for this via a map operation:

val neededIngredients = getIngredients(Flour, Water, BakingPowder)
val possibleMixture = checkAndMix(neededIngredients)
val possibleCake = possibleMixture.map(bake)


The function is applied only if the result is a “Right” value (the mixture exists); otherwise, the “Left” value (the missing ingredients) remains unchanged.

However, a different implementation of the bake function might account for potential baking issues:

def bake(mixture: Mixture): Either[BakingIssue, Cake]


In this case, the result introduces another domain disjunction. If we chain these operations, we end up with nested types:

val possibleCake:
  Either[MissingIngredients, Either[BakingIssue, Cake]]


Comparing this to our first implementation, the type has become complex and difficult to read. It would be much cleaner to have a type that clearly shows the final result – the cake – alongside any potential problems that occurred during preparation.

val idealCake:
  Either[OneOfTwo[MissingIngredients, BakingIssue], Cake]


This ideal solution combines the different negative paths into a single type where only one issue can be present at a time. Here is a simple implementation that converts a nested Either into a “flat” one:

sealed trait OneOfTwo[+A, +B]
case class First[A](a: A) extends OneOfTwo[A, Nothing]
case class Second[B](b: B) extends OneOfTwo[Nothing, B]
// convert
def convert[A, B, C](either: Either[A, Either[B, C]]):
  Either[OneOfTwo[A, B], C] = either.fold(
    a => Left(First(a)), {
      case Left(b) => Left(Second(b))
      case Right(c) => Right(c)
    }
)


This approach keeps our domain disjunctions organized on the left-hand side, leaving the right-hand side clear for our successful result: the cake.

Functional.Programming

Posted on by

I like to think of Functional Programming as another level of abstraction that pulls code further away from the physical machine, much like how automatic memory management once hid raw memory addresses from the developer. To me, Functional Programming is a guide for building applications without the need for manual memory assignments.

It is all about Immutability

What is the problem with memory assignment, or more specifically, mutable data?

In a world where software runs in parallel, shared mutable states are a massive problem. Countless solutions have been built to address this, but they usually involve synchronization, locks, and waiting – all of which lead to performance and scalability bottlenecks.

Another way to solve the problem is to avoid it entirely by writing software without shared mutable states. This means using exclusively immutable data structures. In this context, a paradigm that thrives on immutability becomes very attractive. Functional Programming fits this role perfectly; it acts as a discipline that abstracts software away from the constant need for memory assignments.

Be Functional

The core principle of Functional Programming is transformation: a function takes an input, applies a logic, and returns an output. This approach has the same expressive power as imperative code but offers tools and patterns that guide developers to work within this different paradigm.

Let’s compare an imperative example of finding the maximum value in a list with a functional version:

Imperative Approach:

List<Integer> list = Arrays.asList(3, 6, 5);
int max = Integer.MIN_VALUE;
for (int value : list) {
  if (value > max) max = value;
}


In this example, the imperative code uses a max variable to store and update the result.

Functional Approach:

int calculateMax(List<Integer> list, int max) {
  if (list.isEmpty()) return max;
  else {
    int head = list.get(0);
    List<Integer> tail = list.subList(1, list.size() - 1);
    int newMax = head > max ? head : max;
    return calculateMax(tail, newMax);
  }
}
List<Integer> list = Arrays.asList(3, 6, 5);
calculateMax(list, Integer.MIN_VALUE);


While the imperative version relies on re-assignment, the functional version relies on passing the state through parameters. Concepts like pipelines, higher-order functions, and composition simply make this data transformation more efficient and easier to manage.

Be Pure

An entire application can be viewed as a single, massive function. Since a thousand-line function is obviously bad practice, we modularize: functions are composed to create larger transformations. Simply put, composition is using the output of one function as the input for the next.

int f(double input) { ... }
String g(int input) { ... }
String output = g(f(1.2));


Pure functions are functions with no side effects. This means every observable effect is contained within the output. When you compose functions, the output defines the “interface” between them. If a function throws an unhandled exception, it breaks that interface and falls out of the transformation flow.

Be Typed

A typed language helps define a function’s signature. The more precise the type, the safer the composition. The type system acts as the contract between functions, allowing the compiler to handle a significant portion of the software validation for you.

Actor.Types

Posted on by

The actor in the actor model is defined in Wikipedia as “the universal primitives of concurrent computation”. This definition places actors at the same level of language primitives like if, for, while, etc… in a concurrency context.

Like a primitive, an actor can be used to solve the problems in many way and, like the name suggests, can act a different role that is specific to solve the problem.

Here a not exhaustive list of type of possible types of actor.

Actor as Worker

The actor acts as worker of a specific operation, it has not state and it is specialized to solve the problem.

class Worker extends Actor {
  def receive = {
    case Operation(data) => ...
  }
}

The same operation can be parallelized creating many instances of the same actor with a router (see the documentation).

val router: ActorRef =
  context.actorOf(RoundRobinPool(5).props(Props[Worker]),"router")

The worker runs in a different context from the invoker (the component that has sent the operation) and the other workers. This isolation is used for obtaining parallelism but it also has benefit to separate blocking or failing operations.

Mapping Resources

When a worker (or a groups of workers) maps an external resource, like a connection pool, it creates a protection from the rest of the application. This protection can isolate an external blocking resource if the worker runs in a different thread pool or dispatcher (link to the documentation).

worker-dispatcher {
  type = Dispatcher
  executor = "fork-join-executor"
  throughput = 100
}

In this way only the worker thread is blocked and not the rest of the application.

The dispatcher (thread pool) can be easily associated to a router.

val router: ActorRef =
  context.actorOf(
     RoundRobinPool(5).props(Props[Worker])
     .withDispatcher("my-dispatcher"), "router")

In case of a pool of resources it is possible to match the size of the pool with the number of workers (connection pool of 10 = a router of 10 actors).

Failure of a Worker

The isolation of the worker can be useful to handle failures without the risk of propagation to the rest of the application. The worker can for example implement a retry logic or a circuit breaker or can benefit of the supervisor model, escalating the failure to the parent actor (see the documentation).

Domain Actor

The domain actor represents an instance of a single domain object like for example a person identified by first and second name. The life cycle of the object and the actor is the same, as is the status. The actor messages are the way the object interacts with the rest of the world.

class Person(firstName: String, secondName: String) extends Actor {
  var status = Status()
  def receive = {
    case Create(status) => ...
    case Read() => ...
    case Update(change) => ...
    case Delete() => ...
  }
}

An example of domain actor is the one that maps a single entity, like a row in a database table. Due to the nature of the actor all the operations on the entity are atomic, so this model can be adopted to implement transaction when the storage system doesn’t support it.

The domain actor status is important because it is the entity status and must be protected to not lose it in case of failure. So linking the actor to a storage system is a natural consequence and can be done synchronizing when a change occurs. In this way the actor can crash, can be stopped and restarted without loosing any data.

An actor can persist its status storing all the events that change it. This is know as the event sourcing model and it has been implemented into the Akka Persistence module.

class Person(firstName: String, secondName: String) extends PersistentActor {
  def persistenceId = firstName + "-" + secondName
  val receiveCommand: Receive = {
    case create: Create => persist(create) { event => ... }
    case Read() => ...
    case update: Update => persist(update) { event => ... }
    case delete: Delete => persist(delete) { event => ... }
  }
  val receiveRecover: Receive = {
    case Create(status) => ...
    case Update(change) => ...
    case Delete() => ...
  }
}

The actor persists the event before to update the internal status. This operation is not needed in case of read operations. The persistence actor is identified by the persitenceId that is used as key during the storing and recovery process.

Event Sourcing vs Status Persistence

The vantage of an event sourcing model is the possibility to rebuild the internal status of the actor from the events have contributed to build it. During this process the actor may have changed its behavior that can differ to the initial one. Replacing only the status would not help in this case.

Request Actor

The request actor is an actor created to satisfy a single request. Its status represents the initial request and the progress. A new actor is created for each request and when the request is completed the actor is stopped.

class RequestActor(<request parameters>) extends Actor {
  val requestProgress = ...
  override def preStart() {
    <start interacting with the rest of the system>
  }
  def receive = {
    case MessageFromSystem() => ...
    case LastMessageNeeded() =>
      <send request response back>
      context stop (self)
  }
}

Binding a request to an actor finds vantages in the status. The actor can track the progress, reacts to failures, implements retry logic and can make decisions that influence the result of the request.