f(model) - Functional Domain Modeling with Rust
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.
IOR<Library, Inspiration>
This project can be used as a library, or as an inspiration, or both. It provides just enough tactical Domain-Driven Design patterns, optimised for Event Sourcing and CQRS.
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.
Box<dyn Fn(&C, &S) -> Vec<E>>
type DecideFunction<'a, C, S, E> = Box<dyn Fn(&C, &S) -> Vec<E> + 'a + Send + Sync>
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/Son the input, - when
Command/Cis handled on the input, - expect
Vecof newEvents/Eto be published/emitted on the output
Box<dyn Fn(&S, &E) -> S>
type EvolveFunction<'a, S, E> = Box<dyn Fn(&S, &E) -> S + 'a + Send + Sync>
The new state is always evolved out of the current state S and the current event E:
- given the current
State/Son the input, - when
Event/Eis handled on the input, - expect new
State/Sto be published on the output
Two functions are wrapped in a datatype class (algebraic data structure), which is generalized with three generic parameters:
pub struct Decider<'a, C: 'a, S: 'a, E: 'a> {
pub decide: DecideFunction<'a, C, S, E>,
pub evolve: EvolveFunction<'a, S, E>,
pub initial_state: InitialStateFunction<'a, S>,
}
Decider is the most important datatype, but it is not the only one. There are others:
Decider
Decider is a datatype/struct that represents the main decision-making algorithm. It belongs to the Domain layer. It has three generic parameters C, S, E , representing the type of the values that Decider may contain or use. Decider can be specialized for any type C or S or E 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- CommandS- StateE- Event
pub type DecideFunction<'a, C, S, E> = Box<dyn Fn(&C, &S) -> Vec<E> + 'a + Send + Sync>;
pub type EvolveFunction<'a, S, E> = Box<dyn Fn(&S, &E) -> S + 'a + Send + Sync>;
pub type InitialStateFunction<'a, S> = Box<dyn Fn() -> S + 'a + Send + Sync>;
pub struct Decider<'a, C: 'a, S: 'a, E: 'a> {
pub decide: DecideFunction<'a, C, S, E>,
pub evolve: EvolveFunction<'a, S, E>,
pub initial_state: InitialStateFunction<'a, S>,
}
Additionally, initialState of the Decider is introduced to gain more control over the initial state of the Decider.
Event-sourcing aggregate
Event sourcing aggregate is using/delegating a Decider to handle commands and produce new events. It belongs to the Application layer. In order to handle the command, aggregate needs to fetch the current state (represented as a list/vector of events) via EventRepository.fetchEvents async 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 async function.
It is a formalization of the event sourced information system.
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 async 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 async function.
It is a formalization of the state stored information system.
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 two generic parameters S, E, representing the type of the values that View may contain or use. View can be specialized for any type of S, 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.
S- StateE- Event
pub struct View<'a, S: 'a, E: 'a> {
pub evolve: EvolveFunction<'a, S, E>,
pub initial_state: InitialStateFunction<'a, S>,
}
Materialized View
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.
Algebraic Data Types
In Rust, we can use ADTs to model our application's domain entities and relationships in a functional way, clearly defining the set of possible values and states. Rust has two main types of ADTs: enum and struct.
enumis used to define a type that can take on one of several possible variants - modeling asum/ORtype.structis used to express a type that has named fields - modeling aproduct/ANDtype.
ADTs will help with
- representing the business domain in the code accurately
- enforcing correctness
- reducing the likelihood of bugs.
In FModel, we extensively use ADTs to model the data.
C / Command / Intent to change the state of the system
// models Sum/Or type / multiple possible variants
pub enum OrderCommand {
Create(CreateOrderCommand),
Update(UpdateOrderCommand),
Cancel(CancelOrderCommand),
}
// models Product/And type / a concrete variant, consisting of named fields
pub struct CreateOrderCommand {
pub order_id: u32,
pub customer_name: String,
pub items: Vec<String>,
}
// models Product/And type / a concrete variant, consisting of named fields
pub struct UpdateOrderCommand {
pub order_id: u32,
pub new_items: Vec<String>,
}
// models Product/And type / a concrete variant, consisting of named fields
#[derive(Debug)]
pub struct CancelOrderCommand {
pub order_id: u32,
}
E / Event / Fact
// models Sum/Or type / multiple possible variants
pub enum OrderEvent {
Created(OrderCreatedEvent),
Updated(OrderUpdatedEvent),
Cancelled(OrderCancelledEvent),
}
// models Product/And type / a concrete variant, consisting of named fields
pub struct OrderCreatedEvent {
pub order_id: u32,
pub customer_name: String,
pub items: Vec<String>,
}
// models Product/And type / a concrete variant, consisting of named fields
pub struct OrderUpdatedEvent {
pub order_id: u32,
pub updated_items: Vec<String>,
}
// models Product/And type / a concrete variant, consisting of named fields
pub struct OrderCancelledEvent {
pub order_id: u32,
}
S / State / Current state of the system/aggregate/entity
struct OrderState {
order_id: u32,
customer_name: String,
items: Vec<String>,
is_cancelled: bool,
}
Modeling the Behaviour of our domain
- algebraic data types form the structure of our entities (commands, state, and events).
- functions/lambda offers the algebra of manipulating the entities in a compositional manner, effectively modeling the behavior.
This leads to modularity in design and a clear separation of the entity’s structure and functions/behaviour of the entity.
Fmodel library offers generic and abstract components to specialize in for your specific case/expected behavior:
- Decider - data type that represents the main decision-making algorithm.
fn decider<'a>() -> Decider<'a, OrderCommand, OrderState, OrderEvent> {
Decider {
// Your decision logic goes here.
decide: Box::new(|command, state| match command {
// Exhaustive pattern matching on the command
OrderCommand::Create(create_cmd) => {
vec![OrderEvent::Created(OrderCreatedEvent {
order_id: create_cmd.order_id,
customer_name: create_cmd.customer_name.to_owned(),
items: create_cmd.items.to_owned(),
})]
}
OrderCommand::Update(update_cmd) => {
// Your validation logic goes here
if state.order_id == update_cmd.order_id {
vec![OrderEvent::Updated(OrderUpdatedEvent {
order_id: update_cmd.order_id,
updated_items: update_cmd.new_items.to_owned(),
})]
} else {
// In case of validation failure, return empty list of events or error event
vec![]
}
}
OrderCommand::Cancel(cancel_cmd) => {
// Your validation logic goes here
if state.order_id == cancel_cmd.order_id {
vec![OrderEvent::Cancelled(OrderCancelledEvent {
order_id: cancel_cmd.order_id,
})]
} else {
// In case of validation failure, return empty list of events or error event
vec![]
}
}
}),
// Evolve the state based on the event(s)
evolve: Box::new(|state, event| {
let mut new_state = state.clone();
// Exhaustive pattern matching on the event
match event {
OrderEvent::Created(created_event) => {
new_state.order_id = created_event.order_id;
new_state.customer_name = created_event.customer_name.to_owned();
new_state.items = created_event.items.to_owned();
}
OrderEvent::Updated(updated_event) => {
new_state.items = updated_event.updated_items.to_owned();
}
OrderEvent::Cancelled(_) => {
new_state.is_cancelled = true;
}
}
new_state
}),
// Initial state
initial_state: Box::new(|| OrderState {
order_id: 0,
customer_name: "".to_string(),
items: Vec::new(),
is_cancelled: false,
}),
}
}
- View - represents the event handling algorithm responsible for translating the events into the denormalized state, which is adequate for querying.
// The state of the view component
struct OrderViewState {
order_id: u32,
customer_name: String,
items: Vec<String>,
is_cancelled: bool,
}
fn view<'a>() -> View<'a, OrderViewState, OrderEvent> {
View {
// Evolve the state of the `view` based on the event(s)
evolve: Box::new(|state, event| {
let mut new_state = state.clone();
// Exhaustive pattern matching on the event
match event {
OrderEvent::Created(created_event) => {
new_state.order_id = created_event.order_id;
new_state.customer_name = created_event.customer_name.to_owned();
new_state.items = created_event.items.to_owned();
}
OrderEvent::Updated(updated_event) => {
new_state.items = updated_event.updated_items.to_owned();
}
OrderEvent::Cancelled(_) => {
new_state.is_cancelled = true;
}
}
new_state
}),
// Initial state
initial_state: Box::new(|| OrderViewState {
order_id: 0,
customer_name: "".to_string(),
items: Vec::new(),
is_cancelled: false,
}),
}
}
The Application layer
The logic execution will be orchestrated by the outside components that use the domain components (decider, view) to do the computations. These components will be responsible for fetching and saving the data (repositories).
The arrows in the image (adapters->application->domain) show the direction of the dependency. Notice that all dependencies point inward and that Domain does not depend on anybody or anything.
Pushing these decisions from the core domain model is very valuable. Being able to postpone them is a sign of good architecture.
Event-sourcing aggregate
let repository = InMemoryOrderEventRepository::new();
let aggregate = EventSourcedAggregate::new(repository, decider());
let command = OrderCommand::Create(CreateOrderCommand {
order_id: 1,
customer_name: "John Doe".to_string(),
items: vec!["Item 1".to_string(), "Item 2".to_string()],
});
let result = aggregate.handle(&command).await;
assert!(result.is_ok());
assert_eq!(
result.unwrap(),
[(
OrderEvent::Created(OrderCreatedEvent {
order_id: 1,
customer_name: "John Doe".to_string(),
items: vec!["Item 1".to_string(), "Item 2".to_string()],
}),
0
)]
);
State-stored aggregate
let repository = InMemoryOrderStateRepository::new();
let aggregate = StateStoredAggregate::new(repository, decider());
let command = OrderCommand::Create(CreateOrderCommand {
order_id: 1,
customer_name: "John Doe".to_string(),
items: vec!["Item 1".to_string(), "Item 2".to_string()],
});
let result = aggregate.handle(&command).await;
assert!(result.is_ok());
assert_eq!(
result.unwrap(),
(
OrderState {
order_id: 1,
customer_name: "John Doe".to_string(),
items: vec!["Item 1".to_string(), "Item 2".to_string()],
is_cancelled: false,
},
0
)
);
Fearless Concurrency
Splitting the computation in your program into multiple threads to run multiple tasks at the same time can improve performance. However, programming with threads has a reputation for being difficult. Rust’s type system and ownership model guarantee thread safety.
Example of the concurrent execution of the aggregate:
async fn es_test() {
let repository = InMemoryOrderEventRepository::new();
let aggregate = Arc::new(EventSourcedAggregate::new(repository, decider()));
// Makes a clone of the Arc pointer. This creates another pointer to the same allocation, increasing the strong reference count.
let aggregate2 = Arc::clone(&aggregate);
// Lets spawn two threads to simulate two concurrent requests
let handle1 = thread::spawn(|| async move {
let command = OrderCommand::Create(CreateOrderCommand {
order_id: 1,
customer_name: "John Doe".to_string(),
items: vec!["Item 1".to_string(), "Item 2".to_string()],
});
let result = aggregate.handle(&command).await;
assert!(result.is_ok());
assert_eq!(
result.unwrap(),
[(
OrderEvent::Created(OrderCreatedEvent {
order_id: 1,
customer_name: "John Doe".to_string(),
items: vec!["Item 1".to_string(), "Item 2".to_string()],
}),
0
)]
);
let command = OrderCommand::Update(UpdateOrderCommand {
order_id: 1,
new_items: vec!["Item 3".to_string(), "Item 4".to_string()],
});
let result = aggregate.handle(&command).await;
assert!(result.is_ok());
assert_eq!(
result.unwrap(),
[(
OrderEvent::Updated(OrderUpdatedEvent {
order_id: 1,
updated_items: vec!["Item 3".to_string(), "Item 4".to_string()],
}),
1
)]
);
let command = OrderCommand::Cancel(CancelOrderCommand { order_id: 1 });
let result = aggregate.handle(&command).await;
assert!(result.is_ok());
assert_eq!(
result.unwrap(),
[(
OrderEvent::Cancelled(OrderCancelledEvent { order_id: 1 }),
2
)]
);
});
let handle2 = thread::spawn(|| async move {
let command = OrderCommand::Create(CreateOrderCommand {
order_id: 2,
customer_name: "John Doe".to_string(),
items: vec!["Item 1".to_string(), "Item 2".to_string()],
});
let result = aggregate2.handle(&command).await;
assert!(result.is_ok());
assert_eq!(
result.unwrap(),
[(
OrderEvent::Created(OrderCreatedEvent {
order_id: 2,
customer_name: "John Doe".to_string(),
items: vec!["Item 1".to_string(), "Item 2".to_string()],
}),
0
)]
);
let command = OrderCommand::Update(UpdateOrderCommand {
order_id: 2,
new_items: vec!["Item 3".to_string(), "Item 4".to_string()],
});
let result = aggregate2.handle(&command).await;
assert!(result.is_ok());
assert_eq!(
result.unwrap(),
[(
OrderEvent::Updated(OrderUpdatedEvent {
order_id: 2,
updated_items: vec!["Item 3".to_string(), "Item 4".to_string()],
}),
1
)]
);
let command = OrderCommand::Cancel(CancelOrderCommand { order_id: 2 });
let result = aggregate2.handle(&command).await;
assert!(result.is_ok());
assert_eq!(
result.unwrap(),
[(
OrderEvent::Cancelled(OrderCancelledEvent { order_id: 2 }),
2
)]
);
});
handle1.join().unwrap().await;
handle2.join().unwrap().await;
}
You might wonder why all primitive types in Rust aren’t atomic and why standard library types aren’t implemented to use Arc<T> by default. The reason is that thread safety comes with a performance penalty that you only want to pay when you really need to.
You choose how to run it! You can run it in a single-threaded, multi-threaded, or distributed environment.
Install the crate as a dependency of your project
Run the following Cargo command in your project directory:
cargo add fmodel-rust
Or add the following line to your Cargo.toml file:
fmodel-rust = "0.2.0"
FModel in other languages
Further reading
- https://doc.rust-lang.org/book/
- https://fraktalio.com/fmodel/
- https://fraktalio.com/fmodel-ts/
- https://xebia.com/blog/functional-domain-modeling-in-rust-part-1/
Credits
Special credits to Jérémie Chassaing for sharing his research and Adam Dymitruk for hosting the meetup.
Created with ❤️ by Fraktalio
5 Nov 5, 2022
6 Nov 15, 2022
4 Mar 11, 2023
59 Sep 5, 2022
58 Dec 31, 2022
5 Oct 23, 2023
4 Dec 19, 2023
161 Dec 29, 2022
548 Jan 3, 2023
2 Mar 28, 2022
4 Nov 6, 2022
4 Sep 18, 2023
101 Dec 27, 2022
70 Nov 27, 2022
31 Sep 20, 2022
4 Feb 27, 2023
4 Mar 5, 2023
13 Aug 14, 2023