Domain Driven Design

Domain-Driven Design Framework

Framework for tackling software complexity by modeling code around the business domain. The greatest risk in software is not technical failure -- it is building a model that does not reflect how the business actually works.

Core Principle

The model is the code; the code is the model. Software should embody a deep, shared understanding of the business domain. When domain experts and developers speak the same language and that language is directly expressed in the codebase, complexity becomes manageable and the system evolves gracefully as the business changes.

Scoring

Goal: 10/10. Score a domain model by awarding 1 point per satisfied row of the Quick Diagnostic (7 rows) plus up to 3 points for depth: +1 if the Core Domain has a genuinely rich model (not just CRUD), +1 if invariants live inside aggregates rather than in services, +1 if the ubiquitous language is consistent across conversation, code, and tests. Bands: 9-10 = expert-readable names, explicit context boundaries with ACLs, small aggregates, behavior-rich entities, events for cross-aggregate flow, an identified Core Domain; 5-6 = some domain language but leaky boundaries or anemic objects; <=3 = technical naming, one model for everything, logic scattered in services. Report the score and the specific diagnostic rows failing.

Framework

1. Ubiquitous Language

Core concept: A shared, rigorous language between developers and domain experts, used consistently in conversation, documentation, and code. When the language changes, the code changes -- and awkward naming in code feeds back into refining the language.

Why it works: Ambiguity is the root cause of most modeling failures. When a developer says "order" and an expert means "purchase request," bugs are inevitable; a ubiquitous language forces every name in code to map to a concept the business recognizes and validates.

Key insights:

• The language emerges from deep collaboration, not a glossary bolted on after the fact
• If a concept is hard to name, the model is likely wrong -- naming difficulty is a design signal
• Technical jargon (DataProcessor vs. ClaimAdjudicator) hides domain logic from the experts who could correct it
• Different bounded contexts may use the same word with different meanings -- and that is fine

Code applications:

See: references/ubiquitous-language.md when running modeling sessions or maintaining a glossary -- covers how the language evolves and feeds back into code.

2. Bounded Contexts and Context Mapping

Core concept: A bounded context is an explicit boundary within which a particular domain model applies. The same word ("Customer") can mean different things in different contexts; context maps define the relationships and translation strategies between them.

Why it works: Large systems that try to maintain a single unified model inevitably collapse into inconsistency. Bounded contexts accept that different parts of the business need different models; context maps manage the integration between them.

Key insights:

• A bounded context is not a microservice -- it is a linguistic and model boundary that may contain multiple services
• Context boundaries often align with team boundaries (Conway's Law)
• The nine context mapping patterns describe political and technical relationships between teams
• Anti-Corruption Layer is the most important defensive pattern -- never let a foreign model leak into your core domain
• Shared Kernel couples two teams; keep it small and explicitly governed
• Start by mapping what exists (Big Ball of Mud), then define target boundaries

Code applications:

See: references/bounded-contexts.md for the nine mapping patterns and integration strategies.

3. Entities, Value Objects, and Aggregates

Core concept: Entities have identity that persists across state changes. Value Objects are defined entirely by their attributes and are immutable. Aggregates are clusters of entities and value objects with a single root that enforces consistency boundaries.

Why it works: Without these distinctions, everything becomes a mutable, identity-bearing object -- tangled state, inconsistent updates, fragile concurrency. Aggregates draw the line: everything inside is guaranteed consistent; everything outside is eventually consistent.

Key insights:

• Entity test: "Am I the same thing even if all my attributes change?" (a person changes name and address -- still the same person)
• Value Object test: "Am I defined only by my attributes?" (any $10 bill is interchangeable with another)
• Most things should be Value Objects, not Entities -- prefer immutability
• Keep aggregates small (one root plus a minimal cluster); reference other aggregates by ID, not object reference
• Immediate consistency only within an aggregate; design for eventual consistency between aggregates

Code applications:

See: references/building-blocks.md for aggregate design rules and consistency boundaries.

4. Domain Events

Core concept: A domain event captures something that happened in the domain that experts care about, named in past tense (OrderPlaced, PaymentReceived) -- a fact that has already occurred.

Why it works: Domain events decouple cause from effect. When OrderPlaced is published, shipping, billing, and notifications each react independently without the ordering context knowing about them -- less coupling, eventual consistency, a natural audit trail.

Key insights:

• Events are immutable facts -- once published, they cannot be changed or retracted
• Domain events are internal to a bounded context; integration events cross boundaries
• Events enable temporal decoupling: the producer does not wait for the consumer
• Event sourcing stores the full event history as the source of truth, deriving current state by replay
• Not every state change deserves an event -- only publish what the domain cares about

Code applications:

See: references/domain-events.md for event naming, event sourcing, and integration events.