This commit implements a robust, production-ready analytics system using an event-driven architecture with Redis and `asynq`.
Key changes:
- Event-Driven Architecture: Instead of synchronous database updates, analytics events (e.g., views, likes, comments) are now published to a Redis queue. This improves API response times and decouples the analytics system from the main application flow.
- Background Worker: A new worker process (`cmd/worker`) has been created to consume events from the queue and update the analytics counters in the database.
- View Counting: Implemented the missing view counting feature for both works and translations.
- New Analytics Query: Added a `popularTranslations` GraphQL query to demonstrate how to use the collected analytics data.
- Testing: Added unit tests for the new event publisher and integration tests for the analytics worker.
Known Issue:
The integration tests for the analytics worker (`AnalyticsWorkerSuite`) and the GraphQL API (`GraphQLIntegrationSuite`) are currently failing due to the lack of a Redis service in the test environment. The tests are written and are expected to pass in an environment where Redis is available on `localhost:6379`, as configured in the CI pipeline.
This commit introduces a `Makefile` to standardize the build, test, and linting process, as suggested in the `TODO.md` file.
The `Makefile` includes targets for `lint`, `test`, and `test-integration`.
The `.github/workflows/ci.yml` file has been updated to use the `make test-integration` target, simplifying the CI configuration.
The `.github/workflows/cd.yml` file has been updated to be ready for deployment to a staging environment. It now calls a `make deploy-staging` target, which serves as a placeholder for the actual deployment script.
This work addresses the 'Establish a CI/CD Pipeline' task from the `TODO.md`.
- Core Go application with GraphQL API using gqlgen
- Comprehensive data models for literary works, authors, translations
- Repository pattern with caching layer
- Authentication and authorization system
- Linguistics analysis capabilities with multiple adapters
- Vector search integration with Weaviate
- Docker containerization support
- Python data migration and analysis scripts
- Clean architecture with proper separation of concerns
- Production-ready configuration and middleware
- Proper .gitignore excluding vendor/, database files, and build artifacts
