Go-DDD: Build Domain-Driven Go Services Fast

go-ddd jump-starts production-grade Go backends that keep business rules, infrastructure, and delivery code cleanly separated. Out of the box you get opinionated DDD building blocks, CQRS command and query flows, idempotent write paths, and tooling to keep schema and code in lockstep.

Why This Template

• Model-first defaults – Onion architecture keeps the domain pure while application services orchestrate infrastructure concerns.
• Battle-tested patterns – Commands, queries, repositories, and soft deletes mirror patterns used in real-world enterprise applications.
• Idempotent pipelines – Retry-safe command handlers prevent duplicate writes and highlight resilient workflows.
• Migration discipline – SQL migrations, migrate.go, and sqlc make schema evolution explicit and reproducible.

What You Get

• Marketplace example that demonstrates aggregates (Seller, Product), cross-module interactions, and validation rules.
• Layered modules under internal/ for domain, application, infrastructure, interface, plus testhelpers for fixture reuse.
• Executable entrypoint at cmd/marketplace/main.go ready to wire adapters or frameworks of your choice.
• Database assets in migrations/ and sql/ plus generated data access via sqlc.

Tech Stack Essentials

• Go 1.24 with idiomatic patterns and testify-powered tests.
• Echo v4 HTTP stack for REST endpoints.
• pgx/v5 and sqlc for type-safe PostgreSQL access.
• golang-migrate handling SQL schema migrations
• Testcontainers integration to provision disposable Postgres instances during tests.
• google/uuid helpers for deterministic ID generation inside the domain.

Design Principles in Action

Domain-Driven Design connects implementation to an evolving model. go-ddd showcases this by modelling a simple marketplace where Sellers manage Products, exercising aggregates, value objects, and validation flows.

Documentation

📚 Comprehensive DDD & CQRS Principles Guide - Learn how to apply these patterns to any business domain.

Repository Structure

• domain: The heart of the software, representing business logic and rules.
  • entities: Fundamental objects within our system, like Product and Seller. Contains basic validation logic.
• application: Contains use-case specific operations that interact with the domain layer.
• infrastructure: Supports the higher layers with technical capabilities like database access.
  • db: Database access and models.
  • repositories: Concrete implementations of our storage needs.
• interface: The external layer which interacts with the outside world, like API endpoints.
  • api/rest: Handlers or controllers for managing HTTP requests and responses.

Further principles

• Domain
  • Must not depend on other layers.
  • Provides infrastructure with interfaces, but must not access infrastructure.
  • Implements business logic and rules.
  • Executes validations on entities. Validated entities are passed to the infrastructure layer.
  • Domain layer sets defaults of entities (e.g. uuid for ID or creation timestamp). Don't set defaults in the infrastructure layer or even database!
  • Do not leak domain objects to the outside world.
• Application
  • The glue code between the domain and infrastructure layer.
• Infrastructure
  • Repositories are responsible for translating a domain entity to a database model and retrieving it. No business logic is executed here.
  • Implements interfaces defined by the domain layer.
  • Implements persistence logic like accessing a postgres or mysql database.
  • When writing to storage, read written data before returning it. This ensures that the data is written correctly.

Best Practices

• Don't return validated entities from read methods in the repository. Instead, return the domain entity type directly.
  • Validations will change over time. You don't want to migrate all the data in your database. Instead, you should guarantee you can always load historical data, regardless of how your validation logic has evolved.
  • Otherwise, you won't be able to read data from the database that was written with a different validation logic. You will have to handle errors at runtime.
  • Push validation to the write side-creation (NewX) and update methods - where you must enforce invariants anyway.
• Don't put default values (e.g current timestamp or ID) in the database. Set them in the domain layer (factory!) for several reasons:
  • It's quite dangerous to have two sources of truth.
  • It's easier to test the domain layer.
  • Databases can get replaced, and you don't want to have to change all your default values.
• Always read the entity after write in the infrastructure layer.
  • This ensures that the data is written correctly, and we are never operating on stale data.
• find vs get:
  • find methods can return null or an empty list.
  • get methods must return a value. If the value is not found, throw an error.
• Deletion: Always use soft deletion. Create a deleted_at column in your database and set it to the current timestamp when deleting an entity. This way, you can always restore the entity if needed.

CQRS and Idempotency

Command Query Responsibility Segregation (CQRS)

CQRS separates read operations (queries) from write operations (commands) in your application. In this codebase:

• Commands modify state (CreateSellerCommand, CreateProductCommand, UpdateSellerCommand)
• Queries retrieve data without side effects (FindAllSellers, FindSellerById)

This separation enables different optimization strategies:

• Write optimization: Commands can use normalized schemas, ACID transactions, and strong consistency
• Read optimization: Queries can use denormalized views, caching, read replicas, or even different databases (e.g., PostgreSQL for writes, Elasticsearch for reads)
• Scalability: Read and write workloads can be scaled independently based on actual usage patterns
• Performance: Complex queries don't impact write performance, and write locks don't block read operations

Idempotency Keys

Idempotency ensures that multiple identical requests have the same effect as a single request. This is crucial for handling network failures and retries in distributed systems. Implementation:

• Each command accepts an optional idempotency_key in the request
• The application layer checks if this key has been processed before
• If yes, it returns the cached response without re-executing business logic
• If no, it executes the command and stores the response for future requests

This prevents duplicate entities from being created when clients retry failed requests.

Database Migrations

This project uses golang-migrate for database schema management. Migrations are stored in the migrations/ directory with sequential version numbers.

Migration Files Structure

migrations/
├── 000001_initial_schema.up.sql    # Creates initial tables
└── 000001_initial_schema.down.sql  # Rollback for initial schema

Running Migrations

Using the built-in utility:

# Apply all pending migrations
go run migrate.go -database-url "postgres://user:pass@localhost/db?sslmode=disable" -command up

# Rollback last migration
go run migrate.go -database-url "postgres://user:pass@localhost/db?sslmode=disable" -command down -steps 1

# Check current version
go run migrate.go -database-url "postgres://user:pass@localhost/db?sslmode=disable" -command version

# Force to specific version (use with caution)
go run migrate.go -database-url "postgres://user:pass@localhost/db?sslmode=disable" -command force -version 1

Using the CLI tool directly:

# Install the CLI tool
go install -tags 'postgres' github.com/golang-migrate/migrate/v4/cmd/migrate@latest

# Apply all pending migrations
migrate -path migrations -database "postgres://user:pass@localhost/db?sslmode=disable" up

# Rollback last migration
migrate -path migrations -database "postgres://user:pass@localhost/db?sslmode=disable" down 1

Creating New Migrations

# Create a new migration
migrate create -ext sql -dir migrations -seq add_user_email_column

This will create two files:

• 000002_add_user_email_column.up.sql - Forward migration
• 000002_add_user_email_column.down.sql - Rollback migration

Migration Best Practices

• Always create both up and down migrations
• Test migrations on a copy of production data
• Keep migrations small and focused
• Never modify existing migration files once they've been applied in production
• Use descriptive names for migration files

Getting Started

1. Clone this repository:

git clone https://github.com/sklinkert/go-ddd.git
cd go-ddd
go mod download

2. Install sqlc (for development):

go install github.com/sqlc-dev/sqlc/cmd/sqlc@latest

3. Generate database code (if you modify SQL queries):

sqlc generate

4. Set up your PostgreSQL database and run migrations:

# Set your database connection URL
export DATABASE_URL="postgres://user:password@localhost/dbname?sslmode=disable"

# Run migrations using the built-in utility
go run migrate.go -command up

# Or use the CLI tool directly
migrate -path migrations -database $DATABASE_URL up

5. Run the application:

go run ./cmd/marketplace

Contributions

Contributions, issues, and feature requests are welcome! Feel free to check the issues page.

License

Distributed under the MIT License. See LICENSE for more information.