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
36 lines
862 B
Python
36 lines
862 B
Python
"""FastAPI应用入口"""
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import logging
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from contextlib import asynccontextmanager
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from fastapi import FastAPI
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from app.core.config import settings
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from app.core.logging import setup_logging
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from app.api import router
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from app.services import kafka_service
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setup_logging()
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logger = logging.getLogger(__name__)
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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"""应用生命周期管理"""
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logger.info("服务启动中...")
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yield
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logger.info("服务关闭中...")
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kafka_service.close()
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app = FastAPI(
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title="招聘数据采集服务",
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description="从八爪鱼API采集招聘数据，通过Kafka提供消费接口",
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version=settings.app.version,
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lifespan=lifespan
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)
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app.include_router(router)
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run("app.main:app", host="0.0.0.0", port=8000, reload=True)
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