f`(`model`)` - Functional and Reactive Domain Modeling

When you’re developing an information system to automate the activities of the business, you are modeling the business. The abstractions that you design, the behaviors that you implement, and the UI interactions that you build all reflect the business — together, they constitute the model of the domain.

Multiplatform

Support for multiplatform programming is one of Kotlin’s key benefits. It reduces time spent writing and maintaining the same code for different platforms while retaining the flexibility and benefits of native programming.

`IOR<Library, Inspiration>`

This project can be used as a multiplatform library, or as an inspiration, or both. It provides just enough tactical Domain-Driven Design patterns, optimised for Event Sourcing and CQRS.

The domain model library is fully isolated from the application layer and API-related concerns. It represents a pure declaration of the program logic. It is written in Kotlin programming language, without additional dependencies.
The application libraries orchestrates the execution of the logic by loading state, executing domain components and storing new state. It is written in Kotlin programming language. Two flavors ( extensions of Application module) are available:
- application-vanilla is using plain/vanilla Kotlin to implement the application layer in order to load the state, orchestrate the execution of the logic and save new state.
- application-arrow is using Arrow and Kotlin to implement the application layer in order to load the state, orchestrate the execution of the logic and save new state - managing errors much better (using Either).

The libraries are non-intrusive, and you can select any flavor, or choose both (vanila and arrow). You can use only domain library and model the orchestration (application library) on your own. Or, you can simply be inspired by this project :)

f(model) - Functional domain modeling

Abstraction and generalization

Abstractions can hide irrelevant details and use names to reference objects. It emphasizes what an object is or does rather than how it is represented or how it works.

Generalization reduces complexity by replacing multiple entities which perform similar functions with a single construct.

Abstraction and generalization are often used together. Abstracts are generalized through parameterization to provide more excellent utility.

`decide: (C, S) -> Flow<E>`

On a higher level of abstraction, any information system is responsible for handling the intent (Command) and based on the current State, produce new facts (Events):

given the current State/S on the input,
when Command/C is handled on the input,
expect flow of new Events/E to be published/emitted on the output

`evolve: (S, E) -> S`

The new state is always evolved out of the current state S and the current event E:

given the current State/S on the input,
when Event/E is handled on the input,
expect new State/S to be published on the output

Event-sourced or State-stored systems

State-stored systems are traditional systems that are only storing the current State by overwriting the previous State in the storage.
Event-sourced systems are storing the events in immutable storage by only appending.

A statement:

Both types of systems can be designed by using only these two functions and three generic parameters:

decide: (C, S) -> Flow<E>
evolve: (S, E) -> S

There is more to it! You can switch from one system type to another or have both flavors included within your systems landscape.

A proof:

We can fold/recreate the new state out of the flow of events by using evolve function (S, E) -> S and providing the initialState of type S as a starting point.

Flow<E>.fold(initialState: S, ((S, E) -> S)): S

Essentially, this fold is a function that is mapping a flow of Events to the State:

(Flow<E>) -> S

We can now use this function (Flow<E>) -> S to:

contra-map our decide function ((C, S) -> Flow<E>) over S type to: (C, Flow<E>) -> Flow<E> - ** this is an event-sourced system**
or to map our decide function ((C, S) -> Flow<E>) over E type to: (C, S) -> S - this is a state-stored system

We can verify that we can design any information system (event-sourced or/and state-stored) in this way by using these two functions wrapped in a datatype class (algebraic data structure), which is generalized with three generic parameters:

data class Decider<C, S, E>(
    val decide: (C, S) -> Flow<E>,
    val evolve: (S, E) -> S,
)

Decider is the most important datatype, but it is not the only one. There are others:

Decider

_Decider is a datatype that represents the main decision-making algorithm. It belongs to the Domain layer. It has five generic parameters C, Si, So, Ei, Eo , representing the type of the values that _Decider may contain or use. _Decider can be specialized for any type C or Si or So or Ei or Eo because these types do not affect its behavior. _Decider behaves the same for C=Int or C=YourCustomType, for example.

_Decider is a pure domain component.

C - Command
Si - input State
So - output State
Ei - input Event
Eo - output Event

We make a difference between input and output types, and we are more general in this case. We can always specialize down to the 3 generic parameters: typealias Decider<C, S, E> = _Decider<C, S, S, E, E>

data class _Decider<C, Si, So, Ei, Eo>(
    val decide: (C, Si) -> Flow<Eo>,
    val evolve: (Si, Ei) -> So,
    val initialState: So
) : I_Decider<C, Si, So, Ei, Eo>

typealias Decider<C, S, E> = _Decider<C, S, S, E, E>
typealias IDecider<C, S, E> = I_Decider<C, S, S, E, E>

Additionally, initialState of the Decider is introduced to gain more control over the initial state of the Decider. Notice that Decider implements an interface IDecider to communicate the contract.

Decider extensions and functions

Contravariant

Decider<C, Si, So, Ei, Eo>.mapLeftOnCommand(f: (Cn) -> C): Decider<Cn, Si, So, Ei, Eo>

Profunctor (Contravariant and Covariant)

Decider<C, Si, So, Ei, Eo>.dimapOnEvent( fl: (Ein) -> Ei, fr: (Eo) -> Eon ): Decider<C, Si, So, Ein, Eon>
Decider<C, Si, So, Ei, Eo>.mapLeftOnEvent(f: (Ein) -> Ei): Decider<C, Si, So, Ein, Eo>
Decider<C, Si, So, Ei, Eo>.mapOnEvent(f: (Eo) -> Eon): Decider<C, Si, So, Ei, Eon>
Decider<C, Si, So, Ei, Eo>.dimapOnState( fl: (Sin) -> Si, fr: (So) -> Son ): Decider<C, Sin, Son, Ei, Eo>
Decider<C, Si, So, Ei, Eo>.mapLeftOnState(f: (Sin) -> Si): Decider<C, Sin, So, Ei, Eo>
Decider<C, Si, So, Ei, Eo>.mapOnState(f: (So) -> Son): Decider<C, Si, Son, Ei, Eo>

Applicative

rjustOnS(so: So): Decider<C, Si, So, Ei, Eo>
Decider<C, Si, So, Ei, Eo>.applyOnState(ff: Decider<C, Si, (So) -> Son, Ei, Eo>): Decider<C, Si, Son, Ei, Eo>
Decider<C, Si, So, Ei, Eo>.productOnState(fb: Decider<C, Si, Son, Ei, Eo>): Decider<C, Si, Pair<So, Son>, Ei, Eo>

Monoid

Decider<in C?, in Si, out So, in Ei?, out Eo>.combine( y: Decider<in Cn?, in Sin, out Son, in Ein?, out Eon> ): Decider<C_SUPER, Pair<Si, Sin>, Pair<So, Son>, Ei_SUPER, Eo_SUPER>
with identity element Decider<Nothing?, Unit, Nothing?>

A monoid is a type together with a binary operation (combine) over that type, satisfying associativity and having an identity/empty element. Associativity facilitates parallelization by giving us the freedom to break problems into chunks that can be computed in parallel.

We can now construct event-sourcing or/and state-storing aggregate by using the same decider.

Event-sourcing aggregate

Event sourcing aggregate is using/delegating a Decider to handle commands and produce events. It belongs to the Application layer. In order to handle the command, aggregate needs to fetch the current state (represented as a list of events) via EventRepository.fetchEvents function, and then delegate the command to the decider which can produce new events as a result. Produced events are then stored via EventRepository.save suspending function.

EventSourcingAggregate extends IDecider and EventRepository interfaces, clearly communicating that it is composed out of these two behaviours.

The Delegation pattern has proven to be a good alternative to implementation inheritance, and Kotlin supports it natively requiring zero boilerplate code. eventSourcingAggregate function is a good example:

fun <C, S, E> eventSourcingAggregate(
    decider: IDecider<C, S, E>,
    eventRepository: EventRepository<C, E>
): EventSourcingAggregate<C, S, E> =
    object :
        EventSourcingAggregate<C, S, E>,
        EventRepository<C, E> by eventRepository,
        IDecider<C, S, E> by decider {}

State-stored aggregate

State stored aggregate is using/delegating a Decider to handle commands and produce new state. It belongs to the Application layer. In order to handle the command, aggregate needs to fetch the current state via StateRepository.fetchState function first, and then delegate the command to the decider which can produce new state as a result. New state is then stored via StateRepository.save suspending function.

StateStoredAggregate extends IDecider and StateRepository interfaces, clearly communicating that it is composed out of these two behaviours.

The Delegation pattern has proven to be a good alternative to implementation inheritance, and Kotlin supports it natively requiring zero boilerplate code. stateStoredAggregate function is a good example:

fun <C, S, E> stateStoredAggregate(
    decider: IDecider<C, S, E>,
    stateRepository: StateRepository<C, S>
): StateStoredAggregate<C, S, E> =
    object :
        StateStoredAggregate<C, S, E>,
        StateRepository<C, S> by stateRepository,
        IDecider<C, S, E> by decider {}

View

_View is a datatype that represents the event handling algorithm, responsible for translating the events into denormalized state, which is more adequate for querying. It belongs to the Domain layer. It is usually used to create the view/query side of the CQRS pattern. Obviously, the command side of the CQRS is usually event-sourced aggregate.

It has three generic parameters Si, So, E, representing the type of the values that _View may contain or use. _View can be specialized for any type of Si, So, E because these types do not affect its behavior. _View behaves the same for E=Int or E=YourCustomType, for example.

_View is a pure domain component.

Si - input State
So - output State
E - Event

We make a difference between input and output types, and we are more general in this case. We can always specialize down to the 2 generic parameters: typealias View<S, E> = _View<S, S, E>

data class _View<Si, So, E>(
    val evolve: (Si, E) -> So,
    val initialState: So,
) : I_View<Si, So, E>

typealias View<S, E> = _View<S, S, E>
typealias IView<S, E> = I_View<S, S, E>

Notice that View implements an interface IView to communicate the contract.

View extensions and functions

Contravariant

View<Si, So, E>.mapLeftOnEvent(f: (En) -> E): View<Si, So, En>

Profunctor (Contravariant and Covariant)

View<Si, So, E>.dimapOnState( fl: (Sin) -> Si, fr: (So) -> Son ): View<Sin, Son, E>
View<Si, So, E>.mapLeftOnState(f: (Sin) -> Si): View<Sin, So, E>
View<Si, So, E>.mapOnState(f: (So) -> Son): View<Si, Son, E>

Applicative

View<Si, So, E>.applyOnState(ff: View<Si, (So) -> Son, E>): View<Si, Son, E>
justOnState(so: So): View<Si, So, E>

Monoid

View<in Si, out So, in E?>.combine(y: View<in Si2, out So2, in E2?>): View<Pair<Si, Si2>, Pair<So, So2>, E_SUPER>
with identity element View<Unit, Nothing?>

A monoid is a type together with a binary operation (combine) over that type, satisfying associativity and having an identity/empty element. Associativity facilitates parallelization by giving us the freedom to break problems into chunks that can be computed in parallel.

We can now construct materialized view by using this view.

Materialized View

A Materialized view is using/delegating a View to handle events of type E and to maintain a state of denormalized projection(s) as a result. Essentially, it represents the query/view side of the CQRS pattern. It belongs to the Application layer.

In order to handle the event, materialized view needs to fetch the current state via ViewStateRepository.fetchState suspending function first, and then delegate the event to the view, which can produce new state as a result. New state is then stored via ViewStateRepository.save suspending function.

MaterializedView extends IView and ViewStateRepository interfaces, clearly communicating that it is composed out of these two behaviours.

The Delegation pattern has proven to be a good alternative to implementation inheritance, and Kotlin supports it natively requiring zero boilerplate code. materializedView function is a good example:

fun <S, E> materializedView(
    view: IView<S, E>,
    viewStateRepository: ViewStateRepository<E, S>,
): MaterializedView<S, E> =
    object : MaterializedView<S, E>, ViewStateRepository<E, S> by viewStateRepository, IView<S, E> by view {}

Saga

_Saga is a datatype that represents the central point of control, deciding what to execute next (A). It is responsible for mapping different events from many aggregates into action results AR that the _Saga then can use to calculate the next actions A to be mapped to commands of other aggregates.

_Saga is stateless, it does not maintain the state.

It has two generic parameters AR, A, representing the type of the values that _Saga may contain or use. _Saga can be specialized for any type of AR, A because these types do not affect its behavior. _Saga behaves the same for AR=Int or AR=YourCustomType, for example.

_Saga is a pure domain component.

AR - Action Result
A - Action

data class _Saga<AR, A>(
    val react: (AR) -> Flow<A>
) : I_Saga<AR, A>

typealias Saga<AR, A> = _Saga<AR, A>
typealias ISaga<AR, A> = I_Saga<AR, A>

Notice that Saga implements an interface ISaga to communicate the contract.

Saga extensions and functions

Contravariant

Saga<AR, A>.mapLeftOnActionResult(f: (ARn) -> AR): Saga<ARn, A>

Covariant

Saga<AR, A>.mapOnAction(f: (A) -> An): Saga<AR, An>

Monoid

Saga<in AR?, out A>.combine(y: _Saga<in ARn?, out An>): Saga<AR_SUPER, A_SUPER>
with identity element Saga<Nothing?, Nothing?>

We can now construct Saga Manager by using this saga.

Saga Manager

Saga manager is a stateless process orchestrator. It is reacting on Action Results of type AR and produces new actions A based on them.

Saga manager is using/delegating a Saga to react on Action Results of type AR and produce new actions A which are going to be published via ActionPublisher.publish suspending function.

It belongs to the Application layer.

SagaManager extends ISaga and ActionPublisher interfaces, clearly communicating that it is composed out of these two behaviours.

The Delegation pattern has proven to be a good alternative to implementation inheritance, and Kotlin supports it natively requiring zero boilerplate code. sagaManager function is a good example:

fun <AR, A> sagaManager(
    saga: ISaga<AR, A>,
    actionPublisher: ActionPublisher<A>
): SagaManager<AR, A> =
    object : SagaManager<AR, A>, ActionPublisher<A> by actionPublisher, ISaga<AR, A> by saga {}

Experimental features

Actors (only on JVM)

Coroutines can be executed parallelly. It presents all the usual parallelism problems. The main problem being synchronization of access to shared mutable state. Actors to the rescue!

Dive into the implementation ...

private fun <C, E> CoroutineScope.commandActor(
    fanInChannel: SendChannel<E>,
    capacity: Int = Channel.RENDEZVOUS,
    start: CoroutineStart = CoroutineStart.DEFAULT,
    context: CoroutineContext = EmptyCoroutineContext,
    handle: (C) -> Flow<E>
) = actor<C>(context, capacity, start) {
    for (msg in channel) {
        handle(msg).collect { fanInChannel.send(it) }
    }
}

Actors are marked as @ObsoleteCoroutinesApi by Kotlin at the moment.

Kotlin

"Kotlin has both object-oriented and functional constructs. You can use it in both OO and FP styles, or mix elements of the two. With first-class support for features such as higher-order functions, function types and lambdas, Kotlin is a great choice if you’re doing or exploring functional programming."

Start using the libraries

All fmodel components/libraries are released to Maven Central

Maven coordinates

 <dependency>
    <groupId>com.fraktalio.fmodel</groupId>
    <artifactId>domain</artifactId>
    <version>3.1.0</version>
 </dependency>

 <dependency>
    <groupId>com.fraktalio.fmodel</groupId>
    <artifactId>application-vanilla</artifactId>
    <version>3.1.0</version>
 </dependency>
 
 <dependency>
    <groupId>com.fraktalio.fmodel</groupId>
    <artifactId>application-arrow</artifactId>
    <version>3.1.0</version>
 </dependency>

Examples

Browse the tests

Envision how information system will look like and behave like by modeling the flow of information
- event modeling
The result is a blueprint of the overall solution

Translate the blueprint into the source code

Valuable resources:

Deploy to Maven Central

Manually

mvn clean deploy -Dgpg.passphrase="YOUR_PASSPHRASE" -Pci-cd

References and further reading

Credits

Special credits to Jérémie Chassaing for sharing his research and Adam Dymitruk for hosting the meetup.

Created with ❤️ by Fraktalio

gwokudasam/fmodel

f(model) - Functional and Reactive Domain Modeling

Multiplatform

IOR<Library, Inspiration>

Table of Contents

Abstraction and generalization

decide: (C, S) -> Flow<E>

evolve: (S, E) -> S

Event-sourced or State-stored systems

A statement:

A proof:

Decider

Decider extensions and functions

Contravariant

Profunctor (Contravariant and Covariant)

Applicative

Monoid

Event-sourcing aggregate

State-stored aggregate

View

View extensions and functions

Contravariant

Profunctor (Contravariant and Covariant)

Applicative

Monoid

Materialized View

Saga

Saga extensions and functions

Contravariant

Covariant

Monoid

Saga Manager

Experimental features

Actors (only on JVM)

Kotlin

Start using the libraries

Maven coordinates

Examples

Deploy to Maven Central

Manually

References and further reading

Credits

f`(`model`)` - Functional and Reactive Domain Modeling

`IOR<Library, Inspiration>`

`decide: (C, S) -> Flow<E>`

`evolve: (S, E) -> S`