228 lines
4.3 KiB
Markdown
228 lines
4.3 KiB
Markdown
# 数据库设置指南
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## 概述
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本项目使用PostgreSQL作为主数据库，SQLAlchemy作为ORM，Alembic作为数据库迁移工具。
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## 数据库架构
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### 表结构
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1. **reports** - 报告主表
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- 存储股票分析报告的基本信息
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- 包含证券代码、市场、状态等字段
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2. **analysis_modules** - 分析模块表
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- 存储报告中各个分析模块的内容
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- 与reports表一对多关系
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3. **progress_tracking** - 进度追踪表
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- 记录报告生成过程中各步骤的执行状态
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- 与reports表一对多关系
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4. **system_config** - 系统配置表
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- 存储系统配置信息
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- 使用JSONB格式存储配置值
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## 环境配置
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### 1. 安装PostgreSQL
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```bash
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# macOS (使用Homebrew)
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brew install postgresql
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brew services start postgresql
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# Ubuntu/Debian
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sudo apt-get install postgresql postgresql-contrib
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# CentOS/RHEL
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sudo yum install postgresql-server postgresql-contrib
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```
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### 2. 创建数据库
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```sql
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-- 连接到PostgreSQL
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psql -U postgres
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-- 创建数据库
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CREATE DATABASE stock_analysis;
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-- 创建用户（可选）
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CREATE USER stock_user WITH PASSWORD 'your_password';
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GRANT ALL PRIVILEGES ON DATABASE stock_analysis TO stock_user;
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```
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### 3. 配置环境变量
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创建 `.env` 文件：
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```bash
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# 数据库配置
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DATABASE_URL=postgresql+asyncpg://username:password@localhost:5432/stock_analysis
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DATABASE_ECHO=false
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# API配置
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GEMINI_API_KEY=your_gemini_api_key
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TUSHARE_TOKEN=your_tushare_token
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```
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## 数据库管理
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### 使用管理脚本
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```bash
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# 检查数据库连接
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python manage_db.py check
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# 初始化数据库表
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python manage_db.py init
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# 查看数据库状态
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python manage_db.py status
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```
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### 使用Alembic迁移
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```bash
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# 初始化Alembic（已完成）
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alembic init alembic
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# 创建迁移文件
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alembic revision --autogenerate -m "描述信息"
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# 应用迁移
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alembic upgrade head
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# 查看迁移历史
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alembic history
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# 回滚迁移
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alembic downgrade -1
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```
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## 开发工具
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### 1. 数据库连接检查
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```bash
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python check_db.py
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```
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### 2. 数据库初始化
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```bash
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python init_db.py
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```
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### 3. 综合管理工具
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```bash
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python manage_db.py [check|init|status]
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```
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## 模型使用示例
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### 创建报告
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```python
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from app.models import Report, AnalysisModule
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from app.core.database import get_db
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async def create_report():
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async for db in get_db():
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# 创建报告
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report = Report(
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symbol="000001",
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market="中国",
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status="generating"
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)
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db.add(report)
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await db.commit()
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await db.refresh(report)
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# 创建分析模块
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module = AnalysisModule(
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report_id=report.id,
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module_type="financial_data",
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module_order=1,
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title="财务数据分析",
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status="pending"
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)
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db.add(module)
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await db.commit()
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```
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### 查询报告
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```python
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from sqlalchemy import select
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from app.models import Report
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async def get_report(symbol: str, market: str):
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async for db in get_db():
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stmt = select(Report).where(
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Report.symbol == symbol,
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Report.market == market
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)
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result = await db.execute(stmt)
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return result.scalar_one_or_none()
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```
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## 性能优化
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### 索引
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所有表都已配置适当的索引：
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- reports: symbol+market, status, created_at
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- analysis_modules: report_id, module_type, status, module_order
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- progress_tracking: report_id, status, step_order
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- system_config: config_key, updated_at
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### 连接池
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数据库连接使用异步连接池，配置参数：
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- pool_size: 10
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- max_overflow: 20
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- pool_timeout: 30秒
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- pool_recycle: 1小时
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## 故障排除
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### 常见问题
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1. **连接失败**
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- 检查PostgreSQL服务是否运行
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- 验证数据库URL配置
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- 确认防火墙设置
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2. **迁移失败**
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- 检查数据库权限
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- 验证表结构冲突
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- 查看Alembic日志
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3. **性能问题**
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- 检查索引使用情况
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- 分析慢查询日志
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- 优化查询语句
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### 日志配置
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在 `config.py` 中设置 `DATABASE_ECHO=True` 可以查看SQL执行日志。
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## 备份与恢复
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### 备份
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```bash
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pg_dump -U username -h localhost stock_analysis > backup.sql
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```
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### 恢复
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```bash
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psql -U username -h localhost stock_analysis < backup.sql
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``` |