feat(database): 完成API层、Utils层和兼容层重构 (Stage 4-6)
Stage 4: API层重构
=================
新增文件:
- api/crud.py (430行): CRUDBase泛型类，提供12个CRUD方法
* get, get_by, get_multi, create, update, delete
* count, exists, get_or_create, bulk_create, bulk_update
* 集成缓存: 自动缓存读操作，写操作清除缓存
* 集成批处理: 可选use_batch参数透明使用AdaptiveBatchScheduler
- api/query.py (461行): 高级查询构建器
* QueryBuilder: 链式调用，MongoDB风格操作符
- 操作符: __gt, __lt, __gte, __lte, __ne, __in, __nin, __like, __isnull
- 方法: filter, filter_or, order_by, limit, offset, no_cache
- 执行: all, first, count, exists, paginate
* AggregateQuery: 聚合查询
- sum, avg, max, min, group_by_count
- api/specialized.py (461行): 业务特定API
* ActionRecords: store_action_info, get_recent_actions
* Messages: get_chat_history, get_message_count, save_message
* PersonInfo: get_or_create_person, update_person_affinity
* ChatStreams: get_or_create_chat_stream, get_active_streams
* LLMUsage: record_llm_usage, get_usage_statistics
* UserRelationships: get_user_relationship, update_relationship_affinity
- 更新api/__init__.py: 导出所有API接口
Stage 5: Utils层实现
===================
新增文件:
- utils/decorators.py (320行): 数据库操作装饰器
* @retry: 自动重试失败操作，指数退避
* @timeout: 超时控制
* @cached: 自动缓存函数结果
* @measure_time: 性能测量，慢查询日志
* @transactional: 事务管理，自动提交/回滚
* @db_operation: 组合装饰器
- utils/monitoring.py (330行): 性能监控系统
* DatabaseMonitor: 单例监控器
* OperationMetrics: 操作指标 (次数、时间、错误)
* DatabaseMetrics: 全局指标
- 连接池统计
- 缓存命中率
- 批处理统计
- 预加载统计
* 便捷函数: get_monitor, record_operation, print_stats
- 更新utils/__init__.py: 导出装饰器和监控函数
Stage 6: 兼容层实现
==================
新增目录: compatibility/
- adapter.py (370行): 向后兼容适配器
* 完全兼容旧API签名: db_query, db_save, db_get, store_action_info
* 支持MongoDB风格操作符 (\, \, \)
* 内部使用新架构 (QueryBuilder + CRUDBase)
* 保持返回dict格式不变
* MODEL_MAPPING: 25个模型映射
- __init__.py: 导出兼容API
更新database/__init__.py:
- 导出核心层 (engine, session, models, migration)
- 导出优化层 (cache, preloader, batch_scheduler)
- 导出API层 (CRUD, Query, 业务API)
- 导出Utils层 (装饰器, 监控)
- 导出兼容层 (db_query, db_save等)
核心特性
========
类型安全: Generic[T]提供完整类型推断
缓存透明: 自动缓存，用户无需关心
批处理透明: 可选批处理，自动优化高频写入
链式查询: 流畅的API设计
业务封装: 常用操作封装成便捷函数
向后兼容: 兼容层保证现有代码无缝迁移
性能监控: 完整的指标收集和报告
统计数据
========
- 新增文件: 7个
- 代码行数: ~2050行
- API函数: 14个业务API + 6个装饰器
- 兼容函数: 5个 (db_query, db_save, db_get等)
下一步
======
- 更新28个文件的import语句 (从sqlalchemy_database_api迁移)
- 移动旧文件到old/目录
- 编写Stage 4-6的测试
- 集成测试验证兼容性
This commit is contained in:
Windpicker-owo
434
src/common/database/api/crud.py
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434
src/common/database/api/crud.py
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"""基础CRUD API
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提供通用的数据库CRUD操作，集成优化层功能：
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- 自动缓存：查询结果自动缓存
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- 批量处理：写操作自动批处理
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- 智能预加载：关联数据自动预加载
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"""
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from typing import Any, Optional, Type, TypeVar
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from sqlalchemy import and_, delete, func, select, update
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from sqlalchemy.ext.asyncio import AsyncSession
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from src.common.database.core.models import Base
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from src.common.database.core.session import get_db_session
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from src.common.database.optimization import (
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BatchOperation,
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Priority,
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get_batch_scheduler,
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get_cache,
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get_preloader,
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)
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from src.common.logger import get_logger
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logger = get_logger("database.crud")
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T = TypeVar("T", bound=Base)
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class CRUDBase:
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"""基础CRUD操作类
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提供通用的增删改查操作，自动集成缓存和批处理
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"""
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def __init__(self, model: Type[T]):
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"""初始化CRUD操作
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Args:
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model: SQLAlchemy模型类
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"""
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self.model = model
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self.model_name = model.__tablename__
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async def get(
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self,
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id: int,
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use_cache: bool = True,
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) -> Optional[T]:
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"""根据ID获取单条记录
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Args:
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id: 记录ID
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use_cache: 是否使用缓存
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Returns:
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模型实例或None
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"""
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cache_key = f"{self.model_name}:id:{id}"
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# 尝试从缓存获取
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if use_cache:
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cache = await get_cache()
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cached = await cache.get(cache_key)
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if cached is not None:
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logger.debug(f"缓存命中: {cache_key}")
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return cached
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# 从数据库查询
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async with get_db_session() as session:
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stmt = select(self.model).where(self.model.id == id)
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result = await session.execute(stmt)
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instance = result.scalar_one_or_none()
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# 写入缓存
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if instance is not None and use_cache:
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cache = await get_cache()
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await cache.set(cache_key, instance)
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return instance
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async def get_by(
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self,
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use_cache: bool = True,
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**filters: Any,
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) -> Optional[T]:
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"""根据条件获取单条记录
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Args:
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use_cache: 是否使用缓存
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**filters: 过滤条件
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Returns:
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模型实例或None
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"""
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cache_key = f"{self.model_name}:filter:{str(sorted(filters.items()))}"
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# 尝试从缓存获取
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if use_cache:
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cache = await get_cache()
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cached = await cache.get(cache_key)
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if cached is not None:
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logger.debug(f"缓存命中: {cache_key}")
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return cached
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# 从数据库查询
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async with get_db_session() as session:
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stmt = select(self.model)
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for key, value in filters.items():
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if hasattr(self.model, key):
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stmt = stmt.where(getattr(self.model, key) == value)
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result = await session.execute(stmt)
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instance = result.scalar_one_or_none()
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# 写入缓存
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if instance is not None and use_cache:
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cache = await get_cache()
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await cache.set(cache_key, instance)
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return instance
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async def get_multi(
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self,
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skip: int = 0,
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limit: int = 100,
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use_cache: bool = True,
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**filters: Any,
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) -> list[T]:
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"""获取多条记录
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Args:
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skip: 跳过的记录数
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limit: 返回的最大记录数
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use_cache: 是否使用缓存
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**filters: 过滤条件
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Returns:
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模型实例列表
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"""
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cache_key = f"{self.model_name}:multi:{skip}:{limit}:{str(sorted(filters.items()))}"
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# 尝试从缓存获取
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if use_cache:
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cache = await get_cache()
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cached = await cache.get(cache_key)
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if cached is not None:
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logger.debug(f"缓存命中: {cache_key}")
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return cached
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# 从数据库查询
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async with get_db_session() as session:
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stmt = select(self.model)
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# 应用过滤条件
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for key, value in filters.items():
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if hasattr(self.model, key):
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if isinstance(value, (list, tuple, set)):
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stmt = stmt.where(getattr(self.model, key).in_(value))
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else:
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stmt = stmt.where(getattr(self.model, key) == value)
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# 应用分页
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stmt = stmt.offset(skip).limit(limit)
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result = await session.execute(stmt)
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instances = result.scalars().all()
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# 写入缓存
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if use_cache:
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cache = await get_cache()
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await cache.set(cache_key, instances)
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return instances
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async def create(
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self,
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obj_in: dict[str, Any],
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use_batch: bool = False,
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) -> T:
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"""创建新记录
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Args:
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obj_in: 创建数据
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use_batch: 是否使用批处理
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Returns:
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创建的模型实例
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"""
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if use_batch:
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# 使用批处理
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scheduler = await get_batch_scheduler()
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operation = BatchOperation(
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operation_type="insert",
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model_class=self.model,
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data=obj_in,
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priority=Priority.NORMAL,
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)
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future = await scheduler.add_operation(operation)
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await future
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# 批处理返回成功，创建实例
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instance = self.model(**obj_in)
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return instance
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else:
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# 直接创建
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async with get_db_session() as session:
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instance = self.model(**obj_in)
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session.add(instance)
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await session.flush()
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await session.refresh(instance)
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return instance
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async def update(
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self,
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id: int,
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obj_in: dict[str, Any],
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use_batch: bool = False,
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) -> Optional[T]:
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"""更新记录
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Args:
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id: 记录ID
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obj_in: 更新数据
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use_batch: 是否使用批处理
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Returns:
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更新后的模型实例或None
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"""
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# 先获取实例
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instance = await self.get(id, use_cache=False)
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if instance is None:
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return None
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if use_batch:
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# 使用批处理
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scheduler = await get_batch_scheduler()
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operation = BatchOperation(
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operation_type="update",
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model_class=self.model,
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conditions={"id": id},
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data=obj_in,
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priority=Priority.NORMAL,
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)
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future = await scheduler.add_operation(operation)
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await future
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# 更新实例属性
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for key, value in obj_in.items():
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if hasattr(instance, key):
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setattr(instance, key, value)
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else:
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# 直接更新
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async with get_db_session() as session:
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# 重新加载实例到当前会话
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stmt = select(self.model).where(self.model.id == id)
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result = await session.execute(stmt)
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db_instance = result.scalar_one_or_none()
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if db_instance:
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for key, value in obj_in.items():
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if hasattr(db_instance, key):
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setattr(db_instance, key, value)
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await session.flush()
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await session.refresh(db_instance)
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instance = db_instance
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# 清除缓存
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cache_key = f"{self.model_name}:id:{id}"
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cache = await get_cache()
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await cache.delete(cache_key)
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return instance
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async def delete(
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self,
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id: int,
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use_batch: bool = False,
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) -> bool:
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"""删除记录
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Args:
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id: 记录ID
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use_batch: 是否使用批处理
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Returns:
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是否成功删除
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"""
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if use_batch:
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# 使用批处理
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scheduler = await get_batch_scheduler()
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operation = BatchOperation(
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operation_type="delete",
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model_class=self.model,
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conditions={"id": id},
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priority=Priority.NORMAL,
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)
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future = await scheduler.add_operation(operation)
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result = await future
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success = result > 0
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else:
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# 直接删除
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async with get_db_session() as session:
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stmt = delete(self.model).where(self.model.id == id)
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result = await session.execute(stmt)
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success = result.rowcount > 0
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# 清除缓存
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if success:
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cache_key = f"{self.model_name}:id:{id}"
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cache = await get_cache()
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await cache.delete(cache_key)
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return success
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async def count(
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self,
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**filters: Any,
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) -> int:
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"""统计记录数
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Args:
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**filters: 过滤条件
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Returns:
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记录数量
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"""
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async with get_db_session() as session:
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stmt = select(func.count(self.model.id))
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# 应用过滤条件
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for key, value in filters.items():
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if hasattr(self.model, key):
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if isinstance(value, (list, tuple, set)):
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stmt = stmt.where(getattr(self.model, key).in_(value))
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else:
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stmt = stmt.where(getattr(self.model, key) == value)
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result = await session.execute(stmt)
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return result.scalar()
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async def exists(
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self,
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**filters: Any,
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) -> bool:
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"""检查记录是否存在
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Args:
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**filters: 过滤条件
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Returns:
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是否存在
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"""
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count = await self.count(**filters)
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return count > 0
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async def get_or_create(
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self,
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defaults: Optional[dict[str, Any]] = None,
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**filters: Any,
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) -> tuple[T, bool]:
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"""获取或创建记录
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Args:
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defaults: 创建时的默认值
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**filters: 查找条件
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Returns:
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(实例, 是否新创建)
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"""
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# 先尝试获取
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instance = await self.get_by(use_cache=False, **filters)
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if instance is not None:
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return instance, False
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# 创建新记录
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create_data = {**filters}
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if defaults:
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create_data.update(defaults)
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instance = await self.create(create_data)
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return instance, True
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async def bulk_create(
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self,
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objs_in: list[dict[str, Any]],
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) -> list[T]:
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"""批量创建记录
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Args:
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objs_in: 创建数据列表
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Returns:
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创建的模型实例列表
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"""
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async with get_db_session() as session:
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instances = [self.model(**obj_data) for obj_data in objs_in]
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session.add_all(instances)
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await session.flush()
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for instance in instances:
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await session.refresh(instance)
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return instances
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async def bulk_update(
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self,
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updates: list[tuple[int, dict[str, Any]]],
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) -> int:
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"""批量更新记录
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Args:
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updates: (id, update_data)元组列表
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Returns:
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更新的记录数
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"""
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async with get_db_session() as session:
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count = 0
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for id, obj_in in updates:
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stmt = (
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update(self.model)
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.where(self.model.id == id)
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.values(**obj_in)
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)
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result = await session.execute(stmt)
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count += result.rowcount
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# 清除缓存
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cache_key = f"{self.model_name}:id:{id}"
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cache = await get_cache()
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await cache.delete(cache_key)
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return count
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Block a user