李顺东
ae681575b9
- 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
23 lines
601 B
Python
23 lines
601 B
Python
"""日志配置"""
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import logging
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import sys
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from .config import settings
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def setup_logging():
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"""配置日志"""
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level = logging.DEBUG if settings.app.debug else logging.INFO
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logging.basicConfig(
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level=level,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
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handlers=[
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logging.StreamHandler(sys.stdout)
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]
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)
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# 降低第三方库日志级别
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logging.getLogger("httpx").setLevel(logging.WARNING)
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logging.getLogger("kafka").setLevel(logging.WARNING)
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logging.getLogger("uvicorn").setLevel(logging.INFO)
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