- Add comprehensive sequence diagrams documenting container startup, task initialization, and incremental crawling flow
- Implement reverse-order crawling logic (from latest to oldest) to optimize performance by processing new data first
- Add real-time Kafka message publishing after each batch filtering instead of waiting for task completion
- Update progress tracking to store last_start_offset for accurate incremental crawling across sessions
- Enhance crawler service with improved offset calculation and batch processing logic
- Update configuration files to support new crawling parameters and Kafka integration
- Add progress model enhancements to track crawling state and handle edge cases
- Improve main application initialization to properly handle lifespan events and task auto-start
This change enables efficient incremental data collection where new data is prioritized and published immediately, reducing latency and improving system responsiveness.
- Add technical documentation (技术方案.md) with system architecture and design details
- Create FastAPI application structure with modular organization (api, core, models, services, utils)
- Implement job data crawler service with incremental collection from third-party API
- Add Kafka service integration with Docker Compose configuration for message queue
- Create data models for job listings, progress tracking, and API responses
- Implement REST API endpoints for data consumption (/consume, /status) and task management
- Add progress persistence layer using SQLite for tracking collection offsets
- Implement date filtering logic to extract data published within 7 days
- Create API client service for third-party data source integration
- Add configuration management with environment-based settings
- Include Docker support with Dockerfile and docker-compose.yml for containerized deployment
- Add logging configuration and utility functions for date parsing
- Include requirements.txt with all Python dependencies and README documentation
