ThreeDotsLabs/wild-workouts-go-ddd-example

Features

• Go Design Pattern Examples - Provides a comprehensive set of practical code samples demonstrating DDD, Clean Architecture, and CQRS patterns in Go.
• Clean Architecture Implementations - Implements a concrete Go-based distributed system using Clean Architecture to isolate business logic from infrastructure.
• CQRS Patterns - Separates read and write operations to optimize data retrieval and state updates independently.
• Data Abstraction Layers - Defines data store interactions through interfaces to isolate domain logic from implementation details.
• Data Access Patterns - Employs the repository pattern to separate business logic from persistence details and simplify testing.
• Atomic Event Publishing - Ensures that database state changes and corresponding event notifications are committed as a single atomic unit.
• Persistence Abstractions - Decouples domain logic from specific storage technologies to facilitate database swaps and infrastructure independence.
• gRPC Microservices Integration - Uses gRPC as the primary high-performance communication layer between microservices.
• Architectural Patterns - Implements a distributed system design using gRPC for type-safe communication and service contracts between decoupled components.
• gRPC Service Implementations - Utilizes gRPC for high-performance, type-safe communication between distributed services using buffer contracts.
• Repository Pattern Abstractions - Decouples core business logic from specific storage technologies using the repository pattern.
• Resource-Level Access Controls - Evaluates domain business rules to validate if a user has permission to view or modify specific resource instances.
• gRPC and HTTP TLS Securings - Secures gRPC service communication using token authentication and TLS encryption for data in transit.
• Request-Level Role Authorization - Enforces runtime role-based access control by validating user roles on every incoming request.
• Asynchronous Command Processing - Subscribe to message topics and map payloads to application logic to execute tasks asynchronously.
• Command Query Responsibility Segregation - Decouples modification logic into commands and retrieval logic into queries to scale performance independently.
• Command Query Responsibility Segregation Patterns - Implements the CQRS pattern to separate data modification and retrieval for optimized performance.
• Core Business Logic - Isolates core business rules from infrastructure and delivery mechanisms to ensure domain logic remains independent.
• Event-Driven Architectures - Coordinates updates across microservices using an event-driven architecture with asynchronous processing.
• Dependency Injection - Injects specific adapter implementations at runtime to allow flexible swapping of infrastructure components.
• Business Entities - Encapsulates business rules within domain entities to decouple core logic from orchestration and infrastructure.
• Dependency Inversion Patterns - Employs dependency inversion by defining interfaces in the domain layer to decouple high-level logic from low-level details.
• Logic And Infrastructure Decoupling - Isolates core business rules from transport protocols and database models using intermediate application types.
• Domain-Driven Designs - Uses Domain-Driven Design to encapsulate business rules and state validity within domain entities.
• Domain-Persistence Decoupling - Isolates business logic from storage implementations so domain types follow business rules instead of database constraints.
• Transactional Outbox Patterns - Implements the transactional outbox pattern to ensure reliable event delivery by recording messages in a local database.
• Unit Testing - Implements table-driven unit tests and mocks to validate application services in isolation.
• Table-Driven Tests - Uses table-driven tests and interface mocks to verify pure application orchestration and domain logic.
• Domain Workflow Orchestration - Coordinates data retrieval and domain logic execution via command handlers to complete user requests.
• Domain - Prevents invalid object states by restricting updates to a public API of behavior-oriented methods.
• Containerized Database Integration - Ensures data persistence layers function correctly by running tests against real database engines in containers.
• Atomic Transaction Coordinators - Implements atomic transaction coordination to wrap multiple repository calls into a single indivisible operation.
• Document Stores - Persists application data in NoSQL document stores while maintaining atomic updates across multiple records.
• SQL-Based - Transmits messages via SQL databases to implement the Outbox pattern across different database drivers.
• Polyglot Persistence - Implements polyglot persistence by duplicating domain data into read-optimized databases for faster retrieval.
• Polyglot Data Modeling - Implements a polyglot persistence strategy by duplicating domain data into specialized read-optimized databases via events.
• Read-Optimized Workflows - Creates specialized, read-optimized data structures to increase API response speed and meet UI requirements.
• Read-Write Splitting - Implements separate read and write models to optimize data retrieval and state updates independently.
• Declarative Infrastructure Provisioning - Automates the provisioning of cloud resources and server configurations using declarative infrastructure-as-code modules.
• Continuous Delivery Pipelines - Automates code linting, image packaging, and deployment through structured continuous delivery pipelines.
• Infrastructure as Code Toolkits - Ships collections of scripts and configurations to automate the provisioning of serverless cloud resources and deployment pipelines.
• Asynchronous Message Publishing - Publishes asynchronous events to message brokers to enable reactive system changes without blocking requests.
• Serverless Provisioning - Provisions serverless compute, database, and authentication services in the cloud using standardized templates.
• Web Service Deployments - Deploys Docker containers as network-accessible services that scale automatically based on gRPC and HTTP traffic.
• Eventual Consistency Orchestrators - Coordinates data synchronization across distributed services to achieve eventual consistency via event publishing.
• Authorized Collection Filtering - Filters database queries at the repository level to ensure users only see authorized records.
• OAuth Provider Integrations - Enables user authentication through integrations with external OAuth providers like Google and GitHub.
• Optimistic Locking - Prevents data conflicts using optimistic locking with version numbers to ensure consistent record modifications.
• Third-Party Identity Integrations - Integrates with third-party authentication services to manage user sign-in and identity verification.
• JWT Authentications - Validates API requests using signed JSON Web Tokens issued by an external identity provider.
• Asynchronous Command Execution - Processes heavy computations asynchronously using a message queue to prevent blocking the user interface.
• Behavior-Oriented API Design - Designs APIs with specific methods that trigger distinct domain actions rather than generic endpoints.
• Data Transformation Layers - Implements explicit conversion functions to transform data between different architectural layer models.
• Event Sourcing - Stores a sequence of events rather than just the current state to provide a complete audit trail.
• Distributed - Prevents duplicate state changes by acknowledging messages only after success and employing unique database constraints.
• Data Model Decoupling - Uses separate data structures for APIs and databases to isolate layers and prevent breaking changes.
• Service Testing - Validates internal service layers by calling port handlers and using infrastructure containers with mocked dependencies.
• End-to-End Testing - Verifies complete system flows across multiple services with a comprehensive end-to-end test suite.
• Containerized Integration Tests - Runs integration tests against real infrastructure using containers within the CI pipeline.
• Containerized Test Runners - Executes tests within container images using configuration overrides to validate production-ready artifacts.
• Local Infrastructure Environments - Tests adapters against external infrastructure using containers to mirror production environments.
• Systems and Infrastructure - Practical example of domain-driven design and clean architecture patterns.
• Architecture Patterns - Example project demonstrating DDD and CQRS.
• Domain Driven Design Samples - Serverless application showcasing Clean Architecture and CQRS.
• Implementation Patterns - Production-ready Go example using Clean Architecture and gRPC.
• Language Fundamentals - Practical example of domain-driven design and clean architecture patterns.
• Web Development - Practical example of domain-driven design and clean architecture patterns.
• Security and Testing - Practical example of domain-driven design and clean architecture patterns.

Istoric stele