Nw Fp Domain Modeling Agent Skill

FP Domain Modeling

Domain modeling with types. Make illegal states unrepresentable, workflows as pipelines, error handling at the type level.

Cross-references: fp-principles | fp-hexagonal-architecture | fp-algebra-driven-design

1. The Two Building Blocks

[STARTER]

All domain types compose from two operations:

• AND (Record Types): Value has ALL of these fields. Order requires CustomerInfo AND ShippingAddress AND OrderLines.
• OR (Choice Types): Value is ONE OF these alternatives. ProductCode is either WidgetCode OR GizmoCode.

Combined recursively, these express virtually any domain structure.

2. Domain Wrappers for Primitives

[STARTER]

Never use primitives directly in the domain model. Each domain concept gets its own wrapper type.

What: Wrap primitives so the compiler distinguishes CustomerId from OrderId.

When: Every primitive with domain meaning.

Why: Prevents accidental mixing (compiler rejects comparing CustomerId with OrderId). Each wrapper carries its own validation rules. The type name IS the documentation.

3. Validated Construction (Smart Constructors)

[STARTER]

Raw constructor is private. A create function validates input and returns a Result type, making validation failure explicit.

Pattern: UnitQuantity must be between 1 and 1000. Its create function rejects values outside that range. A companion value function provides read access to the inner primitive.

When: Every domain wrapper with validation rules.

Why: Once constructed, a value is guaranteed valid. No defensive checks deeper in the code.

4. Making Illegal States Unrepresentable

[STARTER]

The central design guideline. Instead of flags and runtime checks, model the domain so invalid states cannot be constructed.

Replace Flags with Distinct Types

Instead of { EmailAddress; IsVerified: bool }, create separate VerifiedEmailAddress and UnverifiedEmailAddress types. Functions requiring verification take VerifiedEmailAddress, making misuse a compile error.

Replace Optional Fields with Choice Types

Instead of { Email: option; Address: option } (where both could be None), create: EmailOnly | AddressOnly | EmailAndAddress. The "at least one required" rule becomes structurally enforced.

NonEmptyList for "At Least One" Rules

Define a type guaranteeing at least one element. Order with OrderLines: NonEmptyList<OrderLine> cannot have zero lines.

5. Workflows as Functions

[STARTER]

Every business workflow is a single function: Command in, Events out.

PlaceOrderWorkflow : PlaceOrderCommand -> AsyncResult<PlaceOrderEvent list>

Pipeline Composition

Workflows decompose into steps, each transforming one document type into the next:

UnvalidatedOrder -> ValidatedOrder -> PricedOrder -> Events

Each step is stateless, pure, has single input/output type, and is independently testable. The workflow assembles by piping steps together.

Why: Pipeline makes the business process visible. Each step name is a domain concept.

6. Document Lifecycle as State Types

[INTERMEDIATE]

Rather than one Order type with flags, create separate types for each lifecycle stage:

• UnvalidatedOrder (raw input, all fields strings)
• ValidatedOrder (all fields checked)
• PricedOrder (prices calculated)

A top-level Order choice type unifies all states. New states (e.g., Refunded) added without breaking existing code.

When: Domain entities with distinct lifecycle stages where different data is available at each stage.

7. State Machines with Types

[INTERMEDIATE]

When an entity has distinct states with different data and different allowed operations, model each state as a separate type.

ShoppingCart = EmptyCart | ActiveCart of ActiveCartData | PaidCart of PaidCartData

Transition functions take the choice type, pattern-match on current state, return new state.

Benefits: All states explicit | each state has own data | invalid transitions prevented by types | pattern matching warnings reveal unhandled edge cases.

8. Error-Track Pipelines (Railway Pattern)

[INTERMEDIATE]

Each function returns a Result type. Pipeline short-circuits on first failure.

rawInput
  |> validateOrder           -- Result<ValidOrder, Error>
  |> bind calculateTotal     -- Result<PricedOrder, Error>
  |> bind checkInventory     -- Result<ConfirmedOrder, Error>
  |> bind chargePayment      -- Result<PaidOrder, Error>
  |> map generateReceipt     -- Result<Receipt, Error>

Key combinators:

• map: Transform success value (one-track into two-track)
• bind: Chain a function that itself returns Result
• mapError: Transform error value
• tee: Perform side effect without changing value (logging)

Error Classification

| Category | Examples | Strategy |

|---|---|---|

| Domain Errors | Validation failure, out of stock | Model as types, return via Result |

| Panics | Out of memory, null reference | Throw exceptions, catch at top level |

| Infrastructure Errors | Network timeout, auth failure | Case-by-case |

Unifying Error Types

Each step may have its own error type. Define a common error choice type and use mapError to lift step errors before composing.

Collecting All Errors (Applicative Validation)

[ADVANCED]

Standard bind short-circuits on first error. For validation where you want ALL errors, use Applicative style -- runs all validations and accumulates errors into a list. See fp-principles section 5.

When: Form validation | batch input checking | any place user needs all errors at once.

9. Modeling Dependencies

[INTERMEDIATE]

Each workflow step declares exactly the functions it needs as parameters:

CheckProductCodeExists : ProductCode -> bool
GetProductPrice        : ProductCode -> Price

Convention: Dependencies first in parameter list, primary input last. Enables partial application (functional DI).

Public vs. internal: Top-level workflow hides dependencies from callers. Internal steps make them explicit.

See fp-hexagonal-architecture section 5 for full DI decision tree.

10. Persistence Ignorance

[INTERMEDIATE]

Domain model has no awareness of databases. Two distinct type hierarchies:

• Domain types: Rich, nested, use choice types, validated wrappers. Not serialization-friendly.
• DTO types: Flat, primitives, nullable, arrays. Designed for serialization.

Conversion Pattern

• fromDomain: Domain -> DTO. Always succeeds.
• toDomain: DTO -> Result<Domain, Error>. May fail (validation happens here).

Serialization as Pipeline Steps

JSON -> deserialize -> DTO -> toDomain -> [WORKFLOW] -> fromDomain -> DTO -> serialize -> JSON

Domain workflow never sees JSON or DTOs directly.

11. Bounded Contexts

[ADVANCED]

Each context has its own dialect of domain language. Contexts communicate only through events and DTOs. Design for autonomy.

Trust boundaries: Input gate validates and converts incoming DTOs to domain types. Output gate converts domain types to DTOs, deliberately dropping private information.

Inter-context relationships: Shared Kernel (shared design) | Customer/Supplier (downstream defines contract) | Anti-Corruption Layer (translator preventing external model from corrupting internal domain).

Decision Tree: How to Model This Domain Concept?

Is it a simple value with validation rules?
  YES --> Domain Wrapper + Smart Constructor
  NO -->
    Does it represent one of several alternatives?
      YES --> Choice Type (sum type)
      NO -->
        Does it group several values together?
          YES --> Record Type (product type)
          NO -->
            Does it have distinct lifecycle stages?
              YES --> State Machine with Types (section 7)
              NO -->
                Is it an operation that transforms data through stages?
                  YES --> Workflow Pipeline (section 5)
                  NO --> Evaluate whether it needs modeling at all

12. Key Design Heuristics

• Start from events and workflows, not data structures
• Let the domain expert drive naming -- their vocabulary, not technical jargon
• Use types to enforce business rules at compile time -- every rule in the type system needs no unit test
• Document effects in signatures -- Result for errors | Async for I/O | Option for missing data
• Separate domain types from serialization types -- domain is pure; serialization is infrastructure
• Prefer explicit over implicit -- every input and dependency is a function parameter

Naming Patterns

• Types as nouns: Order | ProductCode | CustomerInfo
• Workflows as verbs: ValidateOrder | PriceOrder | PlaceOrder
• Events in past tense: OrderPlaced | OrderShipped
• Commands in imperative: PlaceOrder | ShipOrder | CancelOrder
• Lifecycle prefixes: UnvalidatedOrder | ValidatedOrder | PricedOrder