What is Domain Driven Design?
Domain Driven Design (DDD) is an approach to software development that puts the core business—the domain—at the center of every decision. Instead of starting with database tables or REST endpoints, you start by understanding the language, rules, and workflows that experts use every day. The code becomes a living translation of that knowledge, making the system easier to change as the business evolves.
Why DDD Matters in 2025
Cloud, microservices, and serverless have made it cheap to split systems into small deployable units. Without a shared mental model, however, those units grow into silos that duplicate concepts and couple accidentally. DDD provides the linguistic and architectural guard rails that keep autonomous services aligned to real business capabilities. Teams that adopt DDD report fewer integration defects and faster onboarding of new developers because the codebase tells the story of the business.
The Two Sides of DDD: Strategic and Tactical
Strategic DDD helps you see the big picture: where to draw service boundaries, which groups of people share goals, and which legacy parts must be isolated. Tactical DDD gives you fine-grained patterns—Entities, Value Objects, Aggregates, Repositories—to write code that mirrors the model. Both sides are useless alone. You can write perfect Aggregates in a monolith that still slices the domain the wrong way, or split into microservices that duplicate business rules. Learn both sides and apply them together.
Step 1: Crunch Knowledge with Experts
Schedule short, focused建模 sessions—not endless PowerPoints. Ask domain experts to walk you through one real use case at a time. Record the exact words they use. When the same phrase pops up with different meanings, highlight it in red. These conflicts are gold; they reveal boundaries you will later turn into Bounded Contexts. End every session by reading your notes back to the experts until they nod. That oral consensus is the first version of the Ubiquitous Language.
Step 2: Distill the Ubiquitous Language
Create a living glossary in the repo README. Every entry contains the term, the definition in English, and one code example. Banish technical synonyms in code: if the business says “invoice,” do not name the class “Bill.” When a developer says “we can’t use that word because it confuses the ORM,” treat it as a design smell. Either the framework is wrong, or the model is. Refactor until code and conversation align.
Step 3: Find Bounded Contexts
A Bounded Context is an area where every word has exactly one meaning. Map each conflicting definition you discovered earlier to a separate context. Typical signs: different user roles, different legal rules, different lifecycles for what looks like the same object. Draw a simple context map: boxes are contexts, lines are relationships. Label each line with the integration pattern—partnership, shared kernel, customer-supplier, conformist, or anticorruption layer. This napkin diagram becomes the blueprint for service boundaries or module boundaries inside a monolith.
Step 4: Design the Aggregate
Inside a Bounded Context, group objects that must stay consistent at every transaction. This cluster is the Aggregate. Pick one Entity to be the Aggregate Root; all outside references point only to the root. Keep Aggregates small—many fit in memory—because they lock as a unit. A rule of thumb: if you need to update more than ten rows in one transaction, revisit the model; you may be missing a concept or hiding two Aggregates inside one.
Step 5: Protect Business Invariants
Place all invariant rules inside the Aggregate, not in service layer utilities. If an order may not exceed a customer’s credit limit, the Order Aggregate receives the Customer limit and enforces the rule before any state change. Raise descriptive domain events such as OrderLimitExceeded. These events become the API of your model; callers listen rather than query. Event naming must also follow the Ubiquitous Language: “Overdrawn” if that is what accountants say.
Tactical Patterns in Plain Code
Entity: object defined by identity, e.g., CustomerId. Value Object: object defined by attributes, e.g., Money. Domain Service: stateless operation that does not naturally belong to an Entity. Repository: collection-like interface that hides persistence. Domain Event: immutable record of something that happened. Write each pattern as a plain class or record; avoid framework annotations inside the domain layer. Infrastructure lives outside in its own package, depending on the domain, never the reverse.
Example: Modeling a Ride-Hailing Trip
In the Trip context, “trip” means the life cycle from rider request to driver drop-off. In the Billing context, “trip” means a line item with distance and fare. Use different classes—Trip versus TripBillingLine—linked only by a shared identifier. The Trip Aggregate enforces rules such as “a trip can be cancelled only before driver arrival.” The Billing Aggregate reacts to the TripCancelled event by issuing a credit note. Each context evolves independently; neither needs to know the internal state of the other.
Integration Without Spaghetti
Choose explicit integration patterns from your context map. Shared Kernel: publish a tiny versioned jar with value objects such as TripId. Customer-Supplier: the Billing team defines the event schema they need; the Trip team guarantees backward compatibility. Anticorruption Layer: when you must consume a legacy SOAP service, wrap it in a façade that translates messy data into your Ubiquitous Language. Never let foreign concepts leak into your model; the layer absorbs the friction.
Event Storming: A Rapid Recipe
Gather eight people max: domain experts, testers, developers. Roll out ten meters of paper. Write sticky notes in four colors: orange for events, blue for commands, green for aggregates, purple for policies. Start by placing orange notes in timeline order: RiderRequestedTrip, DriverArrived, TripCompleted. Ask “what triggered this?” and add blue notes above. Ask “what must we guarantee?” and add green below. After one hour you will have a candidate context map and a backlog of aggregate candidates.
From Model to Microservices
Each Bounded Context becomes an independently deployable service when organizational and performance constraints allow. If two contexts share a database today, start by giving each its own schema inside the same server. Move to separate servers only when you need different scaling profiles or teams. Keep the code in one mono-repo at first; the expensive part is the conceptual split, not the git layout. Evolve to fully isolated services once the message contracts stabilize.
Testing the Domain Layer
Write behavior-focused unit tests in the language of the model. Given a Rider with default credit limit 100, When she requests a Trip costing 120, Then expect OrderLimitExceeded. Use in-memory repositories to keep tests fast. Test infrastructure—REST adapters, database mappings—separately so that domain tests remain pure. When a business rule changes, update the test name and the Ubiquitous Language glossary at the same commit; this keeps documentation honest.
Common Pitfalls and Fixes
Anemic Domain Model: classes with getters and setters but no behavior. Cure: move logic from services into Aggregates. Shared Big Ball of Mud: everyone edits the same Customer class. Cure: split into CustomerRegistration, CustomerLoyalty, CustomerSupport across contexts. CRUD Worship: exposing create-read-update-delete for every table. Cure: expose intention-revealing commands such as UpgradeToVIP instead of generic Update. Premature Microservices: splitting before the model is understood. Cure: modulith first, services later.
DDD and Legacy Systems
Do not rewrite everything. Strangle the legacy inside an Anticorruption Layer. Expose a small aggregate-oriented API on top of the old mess. Migrate slice by slice—one Bounded Context at a time—while the old system continues to earn money. Celebrate when you finally turn off a stored procedure because the new aggregate now owns its rule. Keep a visible “legacy kill board” in the team room; morale matters.
Tooling That Respects the Model
Use lightweight frameworks: plain Java or Kotlin with Spring Boot stripped to minimum annotations, or Node with NestJS modules per context. Prefer event buses that speak your language—Kafka topics named after domain events. Generate OpenAPI from the commands and queries inside each aggregate instead of the other way around. Adopt Liquibase or Flyway migrations scoped per context so that database changes never collide.
Team Organization Tips
Align one cross-functional team per Bounded Context. Give the team direct access to domain experts with no proxy layers. Measure success by business outcomes—fewer rejected orders, faster settlement—rather than story points. Rotate developers across contexts occasionally to spread the Ubiquitous Language, but keep a core guardian in each context to maintain coherence. Hold weekly modeling open hours where anyone can ask “what does this word mean?”
Cheatsheet: Ten-Minute DDD Health Check
1. Open a random source file. Can a domain expert read the class name and understand its purpose? 2. List the public methods of your largest aggregate. Are any named Update or Process? Rename them to match a business verb. 3. Search for double meanings of a core term across services. If found, draw a context map and move classes apart. 4. Count database joins inside a transaction. If more than three, you may have two aggregates. 5. Read the last three bug reports. Could the aggregate have enforced the missing rule? If yes, refactor.
Next Steps: Learn by Modeling
Pick a tiny subdomain you know well—maybe the expense approval process in your company. Run a one-hour Event Storming session. Extract one aggregate, code it in your favorite language, and write three behavior-focused tests. Publish the repo and invite feedback. The muscle you build on a small problem scales to the large ones. Remember: Domain Driven Design is not a ceremony; it is continuous curiosity about the language of the business.
Disclaimer
This article was generated by an AI language model for instructional purposes. It is not financial, legal, or engineering advice. Apply techniques at your own risk and consult a qualified professional for mission-critical systems.