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feat(database): 完成API层、Utils层和兼容层重构 (Stage 4-6)

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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
2025-11-01 13:27:33 +08:00
parent aae84ec454
commit 61de975d73
10 changed files with 2563 additions and 5 deletions
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126
src/common/database/__init__.py
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"""数据库模块
重构后的数据库模块，提供：
- 核心层：引擎、会话、模型、迁移
- 优化层：缓存、预加载、批处理
- API层：CRUD、查询构建器、业务API
- Utils层：装饰器、监控
- 兼容层：向后兼容的API
"""
# ===== 核心层 =====
from src.common.database.core import (
Base,
check_and_migrate_database,
get_db_session,
get_engine,
get_session_factory,
)
# ===== 优化层 =====
from src.common.database.optimization import (
AdaptiveBatchScheduler,
DataPreloader,
MultiLevelCache,
get_batch_scheduler,
get_cache,
get_preloader,
)
# ===== API层 =====
from src.common.database.api import (
AggregateQuery,
CRUDBase,
QueryBuilder,
# ActionRecords API
get_recent_actions,
# ChatStreams API
get_active_streams,
# Messages API
get_chat_history,
get_message_count,
# PersonInfo API
get_or_create_person,
# LLMUsage API
get_usage_statistics,
record_llm_usage,
# 业务API
save_message,
store_action_info,
update_person_affinity,
)
# ===== Utils层 =====
from src.common.database.utils import (
cached,
db_operation,
get_monitor,
measure_time,
print_stats,
record_cache_hit,
record_cache_miss,
record_operation,
reset_stats,
retry,
timeout,
transactional,
)
# ===== 兼容层（向后兼容旧API）=====
from src.common.database.compatibility import (
MODEL_MAPPING,
build_filters,
db_get,
db_query,
db_save,
)
__all__ = [
# 核心层
"Base",
"get_engine",
"get_session_factory",
"get_db_session",
"check_and_migrate_database",
# 优化层
"MultiLevelCache",
"DataPreloader",
"AdaptiveBatchScheduler",
"get_cache",
"get_preloader",
"get_batch_scheduler",
# API层 - 基础类
"CRUDBase",
"QueryBuilder",
"AggregateQuery",
# API层 - 业务API
"store_action_info",
"get_recent_actions",
"get_chat_history",
"get_message_count",
"save_message",
"get_or_create_person",
"update_person_affinity",
"get_active_streams",
"record_llm_usage",
"get_usage_statistics",
# Utils层
"retry",
"timeout",
"cached",
"measure_time",
"transactional",
"db_operation",
"get_monitor",
"record_operation",
"record_cache_hit",
"record_cache_miss",
"print_stats",
"reset_stats",
# 兼容层
"MODEL_MAPPING",
"build_filters",
"db_query",
"db_save",
"db_get",
]

60
src/common/database/api/__init__.py
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@@ -1,9 +1,59 @@
"""数据库API层
职责：
- CRUD操作
- 查询构建
- 特殊业务操作
提供统一的数据库访问接口
"""
__all__ = []
# CRUD基础操作
from src.common.database.api.crud import CRUDBase
# 查询构建器
from src.common.database.api.query import AggregateQuery, QueryBuilder
# 业务特定API
from src.common.database.api.specialized import (
# ActionRecords
get_recent_actions,
store_action_info,
# ChatStreams
get_active_streams,
get_or_create_chat_stream,
# LLMUsage
get_usage_statistics,
record_llm_usage,
# Messages
get_chat_history,
get_message_count,
save_message,
# PersonInfo
get_or_create_person,
update_person_affinity,
# UserRelationships
get_user_relationship,
update_relationship_affinity,
)
__all__ = [
# 基础类
"CRUDBase",
"QueryBuilder",
"AggregateQuery",
# ActionRecords API
"store_action_info",
"get_recent_actions",
# Messages API
"get_chat_history",
"get_message_count",
"save_message",
# PersonInfo API
"get_or_create_person",
"update_person_affinity",
# ChatStreams API
"get_or_create_chat_stream",
"get_active_streams",
# LLMUsage API
"record_llm_usage",
"get_usage_statistics",
# UserRelationships API
"get_user_relationship",
"update_relationship_affinity",
]

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"""基础CRUD API
提供通用的数据库CRUD操作，集成优化层功能：
- 自动缓存：查询结果自动缓存
- 批量处理：写操作自动批处理
- 智能预加载：关联数据自动预加载
"""
from typing import Any, Optional, Type, TypeVar
from sqlalchemy import and_, delete, func, select, update
from sqlalchemy.ext.asyncio import AsyncSession
from src.common.database.core.models import Base
from src.common.database.core.session import get_db_session
from src.common.database.optimization import (
BatchOperation,
Priority,
get_batch_scheduler,
get_cache,
get_preloader,
)
from src.common.logger import get_logger
logger = get_logger("database.crud")
T = TypeVar("T", bound=Base)
class CRUDBase:
"""基础CRUD操作类
提供通用的增删改查操作，自动集成缓存和批处理
"""
def __init__(self, model: Type[T]):
"""初始化CRUD操作
Args:
model: SQLAlchemy模型类
"""
self.model = model
self.model_name = model.__tablename__
async def get(
self,
id: int,
use_cache: bool = True,
) -> Optional[T]:
"""根据ID获取单条记录
Args:
id: 记录ID
use_cache: 是否使用缓存
Returns:
模型实例或None
"""
cache_key = f"{self.model_name}:id:{id}"
# 尝试从缓存获取
if use_cache:
cache = await get_cache()
cached = await cache.get(cache_key)
if cached is not None:
logger.debug(f"缓存命中: {cache_key}")
return cached
# 从数据库查询
async with get_db_session() as session:
stmt = select(self.model).where(self.model.id == id)
result = await session.execute(stmt)
instance = result.scalar_one_or_none()
# 写入缓存
if instance is not None and use_cache:
cache = await get_cache()
await cache.set(cache_key, instance)
return instance
async def get_by(
self,
use_cache: bool = True,
**filters: Any,
) -> Optional[T]:
"""根据条件获取单条记录
Args:
use_cache: 是否使用缓存
**filters: 过滤条件
Returns:
模型实例或None
"""
cache_key = f"{self.model_name}:filter:{str(sorted(filters.items()))}"
# 尝试从缓存获取
if use_cache:
cache = await get_cache()
cached = await cache.get(cache_key)
if cached is not None:
logger.debug(f"缓存命中: {cache_key}")
return cached
# 从数据库查询
async with get_db_session() as session:
stmt = select(self.model)
for key, value in filters.items():
if hasattr(self.model, key):
stmt = stmt.where(getattr(self.model, key) == value)
result = await session.execute(stmt)
instance = result.scalar_one_or_none()
# 写入缓存
if instance is not None and use_cache:
cache = await get_cache()
await cache.set(cache_key, instance)
return instance
async def get_multi(
self,
skip: int = 0,
limit: int = 100,
use_cache: bool = True,
**filters: Any,
) -> list[T]:
"""获取多条记录
Args:
skip: 跳过的记录数
limit: 返回的最大记录数
use_cache: 是否使用缓存
**filters: 过滤条件
Returns:
模型实例列表
"""
cache_key = f"{self.model_name}:multi:{skip}:{limit}:{str(sorted(filters.items()))}"
# 尝试从缓存获取
if use_cache:
cache = await get_cache()
cached = await cache.get(cache_key)
if cached is not None:
logger.debug(f"缓存命中: {cache_key}")
return cached
# 从数据库查询
async with get_db_session() as session:
stmt = select(self.model)
# 应用过滤条件
for key, value in filters.items():
if hasattr(self.model, key):
if isinstance(value, (list, tuple, set)):
stmt = stmt.where(getattr(self.model, key).in_(value))
else:
stmt = stmt.where(getattr(self.model, key) == value)
# 应用分页
stmt = stmt.offset(skip).limit(limit)
result = await session.execute(stmt)
instances = result.scalars().all()
# 写入缓存
if use_cache:
cache = await get_cache()
await cache.set(cache_key, instances)
return instances
async def create(
self,
obj_in: dict[str, Any],
use_batch: bool = False,
) -> T:
"""创建新记录
Args:
obj_in: 创建数据
use_batch: 是否使用批处理
Returns:
创建的模型实例
"""
if use_batch:
# 使用批处理
scheduler = await get_batch_scheduler()
operation = BatchOperation(
operation_type="insert",
model_class=self.model,
data=obj_in,
priority=Priority.NORMAL,
)
future = await scheduler.add_operation(operation)
await future
# 批处理返回成功，创建实例
instance = self.model(**obj_in)
return instance
else:
# 直接创建
async with get_db_session() as session:
instance = self.model(**obj_in)
session.add(instance)
await session.flush()
await session.refresh(instance)
return instance
async def update(
self,
id: int,
obj_in: dict[str, Any],
use_batch: bool = False,
) -> Optional[T]:
"""更新记录
Args:
id: 记录ID
obj_in: 更新数据
use_batch: 是否使用批处理
Returns:
更新后的模型实例或None
"""
# 先获取实例
instance = await self.get(id, use_cache=False)
if instance is None:
return None
if use_batch:
# 使用批处理
scheduler = await get_batch_scheduler()
operation = BatchOperation(
operation_type="update",
model_class=self.model,
conditions={"id": id},
data=obj_in,
priority=Priority.NORMAL,
)
future = await scheduler.add_operation(operation)
await future
# 更新实例属性
for key, value in obj_in.items():
if hasattr(instance, key):
setattr(instance, key, value)
else:
# 直接更新
async with get_db_session() as session:
# 重新加载实例到当前会话
stmt = select(self.model).where(self.model.id == id)
result = await session.execute(stmt)
db_instance = result.scalar_one_or_none()
if db_instance:
for key, value in obj_in.items():
if hasattr(db_instance, key):
setattr(db_instance, key, value)
await session.flush()
await session.refresh(db_instance)
instance = db_instance
# 清除缓存
cache_key = f"{self.model_name}:id:{id}"
cache = await get_cache()
await cache.delete(cache_key)
return instance
async def delete(
self,
id: int,
use_batch: bool = False,
) -> bool:
"""删除记录
Args:
id: 记录ID
use_batch: 是否使用批处理
Returns:
是否成功删除
"""
if use_batch:
# 使用批处理
scheduler = await get_batch_scheduler()
operation = BatchOperation(
operation_type="delete",
model_class=self.model,
conditions={"id": id},
priority=Priority.NORMAL,
)
future = await scheduler.add_operation(operation)
result = await future
success = result > 0
else:
# 直接删除
async with get_db_session() as session:
stmt = delete(self.model).where(self.model.id == id)
result = await session.execute(stmt)
success = result.rowcount > 0
# 清除缓存
if success:
cache_key = f"{self.model_name}:id:{id}"
cache = await get_cache()
await cache.delete(cache_key)
return success
async def count(
self,
**filters: Any,
) -> int:
"""统计记录数
Args:
**filters: 过滤条件
Returns:
记录数量
"""
async with get_db_session() as session:
stmt = select(func.count(self.model.id))
# 应用过滤条件
for key, value in filters.items():
if hasattr(self.model, key):
if isinstance(value, (list, tuple, set)):
stmt = stmt.where(getattr(self.model, key).in_(value))
else:
stmt = stmt.where(getattr(self.model, key) == value)
result = await session.execute(stmt)
return result.scalar()
async def exists(
self,
**filters: Any,
) -> bool:
"""检查记录是否存在
Args:
**filters: 过滤条件
Returns:
是否存在
"""
count = await self.count(**filters)
return count > 0
async def get_or_create(
self,
defaults: Optional[dict[str, Any]] = None,
**filters: Any,
) -> tuple[T, bool]:
"""获取或创建记录
Args:
defaults: 创建时的默认值
**filters: 查找条件
Returns:
(实例, 是否新创建)
"""
# 先尝试获取
instance = await self.get_by(use_cache=False, **filters)
if instance is not None:
return instance, False
# 创建新记录
create_data = {**filters}
if defaults:
create_data.update(defaults)
instance = await self.create(create_data)
return instance, True
async def bulk_create(
self,
objs_in: list[dict[str, Any]],
) -> list[T]:
"""批量创建记录
Args:
objs_in: 创建数据列表
Returns:
创建的模型实例列表
"""
async with get_db_session() as session:
instances = [self.model(**obj_data) for obj_data in objs_in]
session.add_all(instances)
await session.flush()
for instance in instances:
await session.refresh(instance)
return instances
async def bulk_update(
self,
updates: list[tuple[int, dict[str, Any]]],
) -> int:
"""批量更新记录
Args:
updates: (id, update_data)元组列表
Returns:
更新的记录数
"""
async with get_db_session() as session:
count = 0
for id, obj_in in updates:
stmt = (
update(self.model)
.where(self.model.id == id)
.values(**obj_in)
)
result = await session.execute(stmt)
count += result.rowcount
# 清除缓存
cache_key = f"{self.model_name}:id:{id}"
cache = await get_cache()
await cache.delete(cache_key)
return count

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"""高级查询API
提供复杂的查询操作：
- MongoDB风格的查询操作符
- 聚合查询
- 排序和分页
- 关联查询
"""
from typing import Any, Generic, Optional, Sequence, Type, TypeVar
from sqlalchemy import and_, asc, desc, func, or_, select
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy.engine import Row
from src.common.database.core.models import Base
from src.common.database.core.session import get_db_session
from src.common.database.optimization import get_cache, get_preloader
from src.common.logger import get_logger
logger = get_logger("database.query")
T = TypeVar("T", bound="Base")
class QueryBuilder(Generic[T]):
"""查询构建器
支持链式调用，构建复杂查询
"""
def __init__(self, model: Type[T]):
"""初始化查询构建器
Args:
model: SQLAlchemy模型类
"""
self.model = model
self.model_name = model.__tablename__
self._stmt = select(model)
self._use_cache = True
self._cache_key_parts: list[str] = [self.model_name]
def filter(self, **conditions: Any) -> "QueryBuilder":
"""添加过滤条件
支持的操作符：
- 直接相等: field=value
- 大于: field__gt=value
- 小于: field__lt=value
- 大于等于: field__gte=value
- 小于等于: field__lte=value
- 不等于: field__ne=value
- 包含: field__in=[values]
- 不包含: field__nin=[values]
- 模糊匹配: field__like='%pattern%'
- 为空: field__isnull=True
Args:
**conditions: 过滤条件
Returns:
self，支持链式调用
"""
for key, value in conditions.items():
# 解析字段和操作符
if "__" in key:
field_name, operator = key.rsplit("__", 1)
else:
field_name, operator = key, "eq"
if not hasattr(self.model, field_name):
logger.warning(f"模型 {self.model_name} 没有字段 {field_name}")
continue
field = getattr(self.model, field_name)
# 应用操作符
if operator == "eq":
self._stmt = self._stmt.where(field == value)
elif operator == "gt":
self._stmt = self._stmt.where(field > value)
elif operator == "lt":
self._stmt = self._stmt.where(field < value)
elif operator == "gte":
self._stmt = self._stmt.where(field >= value)
elif operator == "lte":
self._stmt = self._stmt.where(field <= value)
elif operator == "ne":
self._stmt = self._stmt.where(field != value)
elif operator == "in":
self._stmt = self._stmt.where(field.in_(value))
elif operator == "nin":
self._stmt = self._stmt.where(~field.in_(value))
elif operator == "like":
self._stmt = self._stmt.where(field.like(value))
elif operator == "isnull":
if value:
self._stmt = self._stmt.where(field.is_(None))
else:
self._stmt = self._stmt.where(field.isnot(None))
else:
logger.warning(f"未知操作符: {operator}")
# 更新缓存键
self._cache_key_parts.append(f"filter:{str(sorted(conditions.items()))}")
return self
def filter_or(self, **conditions: Any) -> "QueryBuilder":
"""添加OR过滤条件
Args:
**conditions: OR条件
Returns:
self，支持链式调用
"""
or_conditions = []
for key, value in conditions.items():
if hasattr(self.model, key):
field = getattr(self.model, key)
or_conditions.append(field == value)
if or_conditions:
self._stmt = self._stmt.where(or_(*or_conditions))
self._cache_key_parts.append(f"or:{str(sorted(conditions.items()))}")
return self
def order_by(self, *fields: str) -> "QueryBuilder":
"""添加排序
Args:
*fields: 排序字段，'-'前缀表示降序
Returns:
self，支持链式调用
"""
for field_name in fields:
if field_name.startswith("-"):
field_name = field_name[1:]
if hasattr(self.model, field_name):
self._stmt = self._stmt.order_by(desc(getattr(self.model, field_name)))
else:
if hasattr(self.model, field_name):
self._stmt = self._stmt.order_by(asc(getattr(self.model, field_name)))
self._cache_key_parts.append(f"order:{','.join(fields)}")
return self
def limit(self, limit: int) -> "QueryBuilder":
"""限制结果数量
Args:
limit: 最大数量
Returns:
self，支持链式调用
"""
self._stmt = self._stmt.limit(limit)
self._cache_key_parts.append(f"limit:{limit}")
return self
def offset(self, offset: int) -> "QueryBuilder":
"""跳过指定数量
Args:
offset: 跳过数量
Returns:
self，支持链式调用
"""
self._stmt = self._stmt.offset(offset)
self._cache_key_parts.append(f"offset:{offset}")
return self
def no_cache(self) -> "QueryBuilder":
"""禁用缓存
Returns:
self，支持链式调用
"""
self._use_cache = False
return self
async def all(self) -> list[T]:
"""获取所有结果
Returns:
模型实例列表
"""
cache_key = ":".join(self._cache_key_parts) + ":all"
# 尝试从缓存获取
if self._use_cache:
cache = await get_cache()
cached = await cache.get(cache_key)
if cached is not None:
logger.debug(f"缓存命中: {cache_key}")
return cached
# 从数据库查询
async with get_db_session() as session:
result = await session.execute(self._stmt)
instances = list(result.scalars().all())
# 写入缓存
if self._use_cache:
cache = await get_cache()
await cache.set(cache_key, instances)
return instances
async def first(self) -> Optional[T]:
"""获取第一个结果
Returns:
模型实例或None
"""
cache_key = ":".join(self._cache_key_parts) + ":first"
# 尝试从缓存获取
if self._use_cache:
cache = await get_cache()
cached = await cache.get(cache_key)
if cached is not None:
logger.debug(f"缓存命中: {cache_key}")
return cached
# 从数据库查询
async with get_db_session() as session:
result = await session.execute(self._stmt)
instance = result.scalars().first()
# 写入缓存
if instance is not None and self._use_cache:
cache = await get_cache()
await cache.set(cache_key, instance)
return instance
async def count(self) -> int:
"""统计数量
Returns:
记录数量
"""
cache_key = ":".join(self._cache_key_parts) + ":count"
# 尝试从缓存获取
if self._use_cache:
cache = await get_cache()
cached = await cache.get(cache_key)
if cached is not None:
logger.debug(f"缓存命中: {cache_key}")
return cached
# 构建count查询
count_stmt = select(func.count()).select_from(self._stmt.subquery())
# 从数据库查询
async with get_db_session() as session:
result = await session.execute(count_stmt)
count = result.scalar() or 0
# 写入缓存
if self._use_cache:
cache = await get_cache()
await cache.set(cache_key, count)
return count
async def exists(self) -> bool:
"""检查是否存在
Returns:
是否存在记录
"""
count = await self.count()
return count > 0
async def paginate(
self,
page: int = 1,
page_size: int = 20,
) -> tuple[list[T], int]:
"""分页查询
Args:
page: 页码（从1开始）
page_size: 每页数量
Returns:
(结果列表, 总数量)
"""
# 计算偏移量
offset = (page - 1) * page_size
# 获取总数
total = await self.count()
# 获取当前页数据
self._stmt = self._stmt.offset(offset).limit(page_size)
self._cache_key_parts.append(f"page:{page}:{page_size}")
items = await self.all()
return items, total
class AggregateQuery:
"""聚合查询
提供聚合操作如sum、avg、max、min等
"""
def __init__(self, model: Type[T]):
"""初始化聚合查询
Args:
model: SQLAlchemy模型类
"""
self.model = model
self.model_name = model.__tablename__
self._conditions = []
def filter(self, **conditions: Any) -> "AggregateQuery":
"""添加过滤条件
Args:
**conditions: 过滤条件
Returns:
self，支持链式调用
"""
for key, value in conditions.items():
if hasattr(self.model, key):
field = getattr(self.model, key)
self._conditions.append(field == value)
return self
async def sum(self, field: str) -> float:
"""求和
Args:
field: 字段名
Returns:
总和
"""
if not hasattr(self.model, field):
raise ValueError(f"字段 {field} 不存在")
async with get_db_session() as session:
stmt = select(func.sum(getattr(self.model, field)))
if self._conditions:
stmt = stmt.where(and_(*self._conditions))
result = await session.execute(stmt)
return result.scalar() or 0
async def avg(self, field: str) -> float:
"""求平均值
Args:
field: 字段名
Returns:
平均值
"""
if not hasattr(self.model, field):
raise ValueError(f"字段 {field} 不存在")
async with get_db_session() as session:
stmt = select(func.avg(getattr(self.model, field)))
if self._conditions:
stmt = stmt.where(and_(*self._conditions))
result = await session.execute(stmt)
return result.scalar() or 0
async def max(self, field: str) -> Any:
"""求最大值
Args:
field: 字段名
Returns:
最大值
"""
if not hasattr(self.model, field):
raise ValueError(f"字段 {field} 不存在")
async with get_db_session() as session:
stmt = select(func.max(getattr(self.model, field)))
if self._conditions:
stmt = stmt.where(and_(*self._conditions))
result = await session.execute(stmt)
return result.scalar()
async def min(self, field: str) -> Any:
"""求最小值
Args:
field: 字段名
Returns:
最小值
"""
if not hasattr(self.model, field):
raise ValueError(f"字段 {field} 不存在")
async with get_db_session() as session:
stmt = select(func.min(getattr(self.model, field)))
if self._conditions:
stmt = stmt.where(and_(*self._conditions))
result = await session.execute(stmt)
return result.scalar()
async def group_by_count(
self,
*fields: str,
) -> list[tuple[Any, ...]]:
"""分组统计
Args:
*fields: 分组字段
Returns:
[(分组值1, 分组值2, ..., 数量), ...]
"""
if not fields:
raise ValueError("至少需要一个分组字段")
group_columns = []
for field_name in fields:
if hasattr(self.model, field_name):
group_columns.append(getattr(self.model, field_name))
if not group_columns:
return []
async with get_db_session() as session:
stmt = select(*group_columns, func.count(self.model.id))
if self._conditions:
stmt = stmt.where(and_(*self._conditions))
stmt = stmt.group_by(*group_columns)
result = await session.execute(stmt)
return [tuple(row) for row in result.all()]

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"""业务特定API
提供特定业务场景的数据库操作函数
"""
import time
from typing import Any, Optional
import orjson
from src.common.database.api.crud import CRUDBase
from src.common.database.api.query import QueryBuilder
from src.common.database.core.models import (
ActionRecords,
ChatStreams,
LLMUsage,
Messages,
PersonInfo,
UserRelationships,
)
from src.common.database.core.session import get_db_session
from src.common.logger import get_logger
logger = get_logger("database.specialized")
# CRUD实例
_action_records_crud = CRUDBase(ActionRecords)
_chat_streams_crud = CRUDBase(ChatStreams)
_llm_usage_crud = CRUDBase(LLMUsage)
_messages_crud = CRUDBase(Messages)
_person_info_crud = CRUDBase(PersonInfo)
_user_relationships_crud = CRUDBase(UserRelationships)
# ===== ActionRecords 业务API =====
async def store_action_info(
chat_stream=None,
action_build_into_prompt: bool = False,
action_prompt_display: str = "",
action_done: bool = True,
thinking_id: str = "",
action_data: Optional[dict] = None,
action_name: str = "",
) -> Optional[dict[str, Any]]:
"""存储动作信息到数据库
Args:
chat_stream: 聊天流对象
action_build_into_prompt: 是否将此动作构建到提示中
action_prompt_display: 动作的提示显示文本
action_done: 动作是否完成
thinking_id: 关联的思考ID
action_data: 动作数据字典
action_name: 动作名称
Returns:
保存的记录数据或None
"""
try:
# 构建动作记录数据
action_id = thinking_id or str(int(time.time() * 1000000))
record_data = {
"action_id": action_id,
"time": time.time(),
"action_name": action_name,
"action_data": orjson.dumps(action_data or {}).decode("utf-8"),
"action_done": action_done,
"action_build_into_prompt": action_build_into_prompt,
"action_prompt_display": action_prompt_display,
}
# 从chat_stream获取聊天信息
if chat_stream:
record_data.update(
{
"chat_id": getattr(chat_stream, "stream_id", ""),
"chat_info_stream_id": getattr(chat_stream, "stream_id", ""),
"chat_info_platform": getattr(chat_stream, "platform", ""),
}
)
else:
record_data.update(
{
"chat_id": "",
"chat_info_stream_id": "",
"chat_info_platform": "",
}
)
# 使用get_or_create保存记录
saved_record = await _action_records_crud.get_or_create(
defaults=record_data,
action_id=action_id,
)
if saved_record:
logger.debug(f"成功存储动作信息: {action_name} (ID: {action_id})")
return {col.name: getattr(saved_record, col.name) for col in saved_record.__table__.columns}
else:
logger.error(f"存储动作信息失败: {action_name}")
return None
except Exception as e:
logger.error(f"存储动作信息时发生错误: {e}", exc_info=True)
return None
async def get_recent_actions(
chat_id: str,
limit: int = 10,
) -> list[ActionRecords]:
"""获取最近的动作记录
Args:
chat_id: 聊天ID
limit: 限制数量
Returns:
动作记录列表
"""
query = QueryBuilder(ActionRecords)
return await query.filter(chat_id=chat_id).order_by("-time").limit(limit).all()
# ===== Messages 业务API =====
async def get_chat_history(
stream_id: str,
limit: int = 50,
offset: int = 0,
) -> list[Messages]:
"""获取聊天历史
Args:
stream_id: 流ID
limit: 限制数量
offset: 偏移量
Returns:
消息列表
"""
query = QueryBuilder(Messages)
return await (
query.filter(chat_info_stream_id=stream_id)
.order_by("-time")
.limit(limit)
.offset(offset)
.all()
)
async def get_message_count(stream_id: str) -> int:
"""获取消息数量
Args:
stream_id: 流ID
Returns:
消息数量
"""
query = QueryBuilder(Messages)
return await query.filter(chat_info_stream_id=stream_id).count()
async def save_message(
message_data: dict[str, Any],
use_batch: bool = True,
) -> Optional[Messages]:
"""保存消息
Args:
message_data: 消息数据
use_batch: 是否使用批处理
Returns:
保存的消息实例
"""
return await _messages_crud.create(message_data, use_batch=use_batch)
# ===== PersonInfo 业务API =====
async def get_or_create_person(
platform: str,
person_id: str,
defaults: Optional[dict[str, Any]] = None,
) -> Optional[PersonInfo]:
"""获取或创建人员信息
Args:
platform: 平台
person_id: 人员ID
defaults: 默认值
Returns:
人员信息实例
"""
return await _person_info_crud.get_or_create(
defaults=defaults or {},
platform=platform,
person_id=person_id,
)
async def update_person_affinity(
platform: str,
person_id: str,
affinity_delta: float,
) -> bool:
"""更新人员好感度
Args:
platform: 平台
person_id: 人员ID
affinity_delta: 好感度变化值
Returns:
是否成功
"""
try:
# 获取现有人员
person = await _person_info_crud.get_by(
platform=platform,
person_id=person_id,
)
if not person:
logger.warning(f"人员不存在: {platform}/{person_id}")
return False
# 更新好感度
new_affinity = (person.affinity or 0.0) + affinity_delta
await _person_info_crud.update(
person.id,
{"affinity": new_affinity},
)
logger.debug(f"更新好感度: {platform}/{person_id} {affinity_delta:+.2f} -> {new_affinity:.2f}")
return True
except Exception as e:
logger.error(f"更新好感度失败: {e}", exc_info=True)
return False
# ===== ChatStreams 业务API =====
async def get_or_create_chat_stream(
stream_id: str,
platform: str,
defaults: Optional[dict[str, Any]] = None,
) -> Optional[ChatStreams]:
"""获取或创建聊天流
Args:
stream_id: 流ID
platform: 平台
defaults: 默认值
Returns:
聊天流实例
"""
return await _chat_streams_crud.get_or_create(
defaults=defaults or {},
stream_id=stream_id,
platform=platform,
)
async def get_active_streams(
platform: Optional[str] = None,
limit: int = 100,
) -> list[ChatStreams]:
"""获取活跃的聊天流
Args:
platform: 平台（可选）
limit: 限制数量
Returns:
聊天流列表
"""
query = QueryBuilder(ChatStreams)
if platform:
query = query.filter(platform=platform)
return await query.order_by("-last_message_time").limit(limit).all()
# ===== LLMUsage 业务API =====
async def record_llm_usage(
model_name: str,
input_tokens: int,
output_tokens: int,
stream_id: Optional[str] = None,
platform: Optional[str] = None,
use_batch: bool = True,
) -> Optional[LLMUsage]:
"""记录LLM使用情况
Args:
model_name: 模型名称
input_tokens: 输入token数
output_tokens: 输出token数
stream_id: 流ID
platform: 平台
use_batch: 是否使用批处理
Returns:
LLM使用记录实例
"""
usage_data = {
"model_name": model_name,
"input_tokens": input_tokens,
"output_tokens": output_tokens,
"total_tokens": input_tokens + output_tokens,
"timestamp": time.time(),
}
if stream_id:
usage_data["stream_id"] = stream_id
if platform:
usage_data["platform"] = platform
return await _llm_usage_crud.create(usage_data, use_batch=use_batch)
async def get_usage_statistics(
start_time: Optional[float] = None,
end_time: Optional[float] = None,
model_name: Optional[str] = None,
) -> dict[str, Any]:
"""获取使用统计
Args:
start_time: 开始时间戳
end_time: 结束时间戳
model_name: 模型名称
Returns:
统计数据字典
"""
from src.common.database.api.query import AggregateQuery
query = AggregateQuery(LLMUsage)
# 添加时间过滤
if start_time:
async with get_db_session() as session:
from sqlalchemy import and_
conditions = []
if start_time:
conditions.append(LLMUsage.timestamp >= start_time)
if end_time:
conditions.append(LLMUsage.timestamp <= end_time)
if model_name:
conditions.append(LLMUsage.model_name == model_name)
if conditions:
query._conditions = conditions
# 聚合统计
total_input = await query.sum("input_tokens")
total_output = await query.sum("output_tokens")
total_count = await query.filter().count() if hasattr(query, "count") else 0
return {
"total_input_tokens": int(total_input),
"total_output_tokens": int(total_output),
"total_tokens": int(total_input + total_output),
"request_count": total_count,
}
# ===== UserRelationships 业务API =====
async def get_user_relationship(
platform: str,
user_id: str,
target_id: str,
) -> Optional[UserRelationships]:
"""获取用户关系
Args:
platform: 平台
user_id: 用户ID
target_id: 目标用户ID
Returns:
用户关系实例
"""
return await _user_relationships_crud.get_by(
platform=platform,
user_id=user_id,
target_id=target_id,
)
async def update_relationship_affinity(
platform: str,
user_id: str,
target_id: str,
affinity_delta: float,
) -> bool:
"""更新关系好感度
Args:
platform: 平台
user_id: 用户ID
target_id: 目标用户ID
affinity_delta: 好感度变化值
Returns:
是否成功
"""
try:
# 获取或创建关系
relationship = await _user_relationships_crud.get_or_create(
defaults={"affinity": 0.0, "interaction_count": 0},
platform=platform,
user_id=user_id,
target_id=target_id,
)
if not relationship:
logger.error(f"无法创建关系: {platform}/{user_id}->{target_id}")
return False
# 更新好感度和互动次数
new_affinity = (relationship.affinity or 0.0) + affinity_delta
new_count = (relationship.interaction_count or 0) + 1
await _user_relationships_crud.update(
relationship.id,
{
"affinity": new_affinity,
"interaction_count": new_count,
"last_interaction_time": time.time(),
},
)
logger.debug(
f"更新关系: {platform}/{user_id}->{target_id} "
f"好感度{affinity_delta:+.2f}->{new_affinity:.2f} "
f"互动{new_count}次"
)
return True
except Exception as e:
logger.error(f"更新关系好感度失败: {e}", exc_info=True)
return False

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src/common/database/compatibility/__init__.py Normal file
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"""兼容层
提供向后兼容的数据库API
"""
from .adapter import (
MODEL_MAPPING,
build_filters,
db_get,
db_query,
db_save,
store_action_info,
)
__all__ = [
"MODEL_MAPPING",
"build_filters",
"db_query",
"db_save",
"db_get",
"store_action_info",
]

361
src/common/database/compatibility/adapter.py Normal file
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"""兼容层适配器
提供向后兼容的API，将旧的数据库API调用转换为新架构的调用
保持原有函数签名和行为不变
"""
import time
from typing import Any, Optional
import orjson
from sqlalchemy import and_, asc, desc, select
from src.common.database.api import (
CRUDBase,
QueryBuilder,
store_action_info as new_store_action_info,
)
from src.common.database.core.models import (
ActionRecords,
CacheEntries,
ChatStreams,
Emoji,
Expression,
GraphEdges,
GraphNodes,
ImageDescriptions,
Images,
LLMUsage,
MaiZoneScheduleStatus,
Memory,
Messages,
OnlineTime,
PersonInfo,
PermissionNodes,
Schedule,
ThinkingLog,
UserPermissions,
UserRelationships,
)
from src.common.database.core.session import get_db_session
from src.common.logger import get_logger
logger = get_logger("database.compatibility")
# 模型映射表，用于通过名称获取模型类
MODEL_MAPPING = {
"Messages": Messages,
"ActionRecords": ActionRecords,
"PersonInfo": PersonInfo,
"ChatStreams": ChatStreams,
"LLMUsage": LLMUsage,
"Emoji": Emoji,
"Images": Images,
"ImageDescriptions": ImageDescriptions,
"OnlineTime": OnlineTime,
"Memory": Memory,
"Expression": Expression,
"ThinkingLog": ThinkingLog,
"GraphNodes": GraphNodes,
"GraphEdges": GraphEdges,
"Schedule": Schedule,
"MaiZoneScheduleStatus": MaiZoneScheduleStatus,
"CacheEntries": CacheEntries,
"UserRelationships": UserRelationships,
"PermissionNodes": PermissionNodes,
"UserPermissions": UserPermissions,
}
# 为每个模型创建CRUD实例
_crud_instances = {name: CRUDBase(model) for name, model in MODEL_MAPPING.items()}
async def build_filters(model_class, filters: dict[str, Any]):
"""构建查询过滤条件（兼容MongoDB风格操作符）
Args:
model_class: SQLAlchemy模型类
filters: 过滤条件字典
Returns:
条件列表
"""
conditions = []
for field_name, value in filters.items():
if not hasattr(model_class, field_name):
logger.warning(f"模型 {model_class.__name__} 中不存在字段 '{field_name}'")
continue
field = getattr(model_class, field_name)
if isinstance(value, dict):
# 处理 MongoDB 风格的操作符
for op, op_value in value.items():
if op == "$gt":
conditions.append(field > op_value)
elif op == "$lt":
conditions.append(field < op_value)
elif op == "$gte":
conditions.append(field >= op_value)
elif op == "$lte":
conditions.append(field <= op_value)
elif op == "$ne":
conditions.append(field != op_value)
elif op == "$in":
conditions.append(field.in_(op_value))
elif op == "$nin":
conditions.append(~field.in_(op_value))
else:
logger.warning(f"未知操作符 '{op}' (字段: '{field_name}')")
else:
# 直接相等比较
conditions.append(field == value)
return conditions
def _model_to_dict(instance) -> dict[str, Any]:
"""将模型实例转换为字典
Args:
instance: 模型实例
Returns:
字典表示
"""
if instance is None:
return None
result = {}
for column in instance.__table__.columns:
result[column.name] = getattr(instance, column.name)
return result
async def db_query(
model_class,
data: Optional[dict[str, Any]] = None,
query_type: Optional[str] = "get",
filters: Optional[dict[str, Any]] = None,
limit: Optional[int] = None,
order_by: Optional[list[str]] = None,
single_result: Optional[bool] = False,
) -> list[dict[str, Any]] | dict[str, Any] | None:
"""执行异步数据库查询操作（兼容旧API）
Args:
model_class: SQLAlchemy模型类
data: 用于创建或更新的数据字典
query_type: 查询类型 ("get", "create", "update", "delete", "count")
filters: 过滤条件字典
limit: 限制结果数量
order_by: 排序字段，前缀'-'表示降序
single_result: 是否只返回单个结果
Returns:
根据查询类型返回相应结果
"""
try:
if query_type not in ["get", "create", "update", "delete", "count"]:
raise ValueError("query_type must be 'get', 'create', 'update', 'delete' or 'count'")
# 获取CRUD实例
model_name = model_class.__name__
crud = _crud_instances.get(model_name)
if not crud:
crud = CRUDBase(model_class)
if query_type == "get":
# 使用QueryBuilder
query_builder = QueryBuilder(model_class)
# 应用过滤条件
if filters:
# 将MongoDB风格过滤器转换为QueryBuilder格式
for field_name, value in filters.items():
if isinstance(value, dict):
for op, op_value in value.items():
if op == "$gt":
query_builder = query_builder.filter(**{f"{field_name}__gt": op_value})
elif op == "$lt":
query_builder = query_builder.filter(**{f"{field_name}__lt": op_value})
elif op == "$gte":
query_builder = query_builder.filter(**{f"{field_name}__gte": op_value})
elif op == "$lte":
query_builder = query_builder.filter(**{f"{field_name}__lte": op_value})
elif op == "$ne":
query_builder = query_builder.filter(**{f"{field_name}__ne": op_value})
elif op == "$in":
query_builder = query_builder.filter(**{f"{field_name}__in": op_value})
elif op == "$nin":
query_builder = query_builder.filter(**{f"{field_name}__nin": op_value})
else:
query_builder = query_builder.filter(**{field_name: value})
# 应用排序
if order_by:
query_builder = query_builder.order_by(*order_by)
# 应用限制
if limit:
query_builder = query_builder.limit(limit)
# 执行查询
if single_result:
result = await query_builder.first()
return _model_to_dict(result)
else:
results = await query_builder.all()
return [_model_to_dict(r) for r in results]
elif query_type == "create":
if not data:
logger.error("创建操作需要提供data参数")
return None
instance = await crud.create(data)
return _model_to_dict(instance)
elif query_type == "update":
if not filters or not data:
logger.error("更新操作需要提供filters和data参数")
return None
# 先查找记录
query_builder = QueryBuilder(model_class)
for field_name, value in filters.items():
query_builder = query_builder.filter(**{field_name: value})
instance = await query_builder.first()
if not instance:
logger.warning(f"未找到匹配的记录: {filters}")
return None
# 更新记录
updated = await crud.update(instance.id, data)
return _model_to_dict(updated)
elif query_type == "delete":
if not filters:
logger.error("删除操作需要提供filters参数")
return None
# 先查找记录
query_builder = QueryBuilder(model_class)
for field_name, value in filters.items():
query_builder = query_builder.filter(**{field_name: value})
instance = await query_builder.first()
if not instance:
logger.warning(f"未找到匹配的记录: {filters}")
return None
# 删除记录
success = await crud.delete(instance.id)
return {"deleted": success}
elif query_type == "count":
query_builder = QueryBuilder(model_class)
# 应用过滤条件
if filters:
for field_name, value in filters.items():
query_builder = query_builder.filter(**{field_name: value})
count = await query_builder.count()
return {"count": count}
except Exception as e:
logger.error(f"数据库操作失败: {e}", exc_info=True)
return None if single_result or query_type != "get" else []
async def db_save(
model_class,
data: dict[str, Any],
key_field: str,
key_value: Any,
) -> Optional[dict[str, Any]]:
"""保存或更新记录（兼容旧API）
Args:
model_class: SQLAlchemy模型类
data: 数据字典
key_field: 主键字段名
key_value: 主键值
Returns:
保存的记录数据或None
"""
try:
model_name = model_class.__name__
crud = _crud_instances.get(model_name)
if not crud:
crud = CRUDBase(model_class)
# 使用get_or_create
instance = await crud.get_or_create(
defaults=data,
**{key_field: key_value},
)
return _model_to_dict(instance)
except Exception as e:
logger.error(f"保存数据库记录出错: {e}", exc_info=True)
return None
async def db_get(
model_class,
filters: Optional[dict[str, Any]] = None,
limit: Optional[int] = None,
order_by: Optional[str] = None,
single_result: Optional[bool] = False,
) -> list[dict[str, Any]] | dict[str, Any] | None:
"""从数据库获取记录（兼容旧API）
Args:
model_class: SQLAlchemy模型类
filters: 过滤条件
limit: 结果数量限制
order_by: 排序字段，前缀'-'表示降序
single_result: 是否只返回单个结果
Returns:
记录数据或None
"""
order_by_list = [order_by] if order_by else None
return await db_query(
model_class=model_class,
query_type="get",
filters=filters,
limit=limit,
order_by=order_by_list,
single_result=single_result,
)
async def store_action_info(
chat_stream=None,
action_build_into_prompt: bool = False,
action_prompt_display: str = "",
action_done: bool = True,
thinking_id: str = "",
action_data: Optional[dict] = None,
action_name: str = "",
) -> Optional[dict[str, Any]]:
"""存储动作信息到数据库（兼容旧API）
直接使用新的specialized API
"""
return await new_store_action_info(
chat_stream=chat_stream,
action_build_into_prompt=action_build_into_prompt,
action_prompt_display=action_prompt_display,
action_done=action_done,
thinking_id=thinking_id,
action_data=action_data,
action_name=action_name,
)

26
src/common/database/utils/__init__.py
View File

@@ -6,6 +6,7 @@
- 性能监控
"""
from .decorators import cached, db_operation, measure_time, retry, timeout, transactional
from .exceptions import (
BatchSchedulerError,
CacheError,
@@ -17,8 +18,18 @@ from .exceptions import (
DatabaseQueryError,
DatabaseTransactionError,
)
from .monitoring import (
DatabaseMonitor,
get_monitor,
print_stats,
record_cache_hit,
record_cache_miss,
record_operation,
reset_stats,
)
__all__ = [
# 异常
"DatabaseError",
"DatabaseInitializationError",
"DatabaseConnectionError",
@@ -28,4 +39,19 @@ __all__ = [
"CacheError",
"BatchSchedulerError",
"ConnectionPoolError",
# 装饰器
"retry",
"timeout",
"cached",
"measure_time",
"transactional",
"db_operation",
# 监控
"DatabaseMonitor",
"get_monitor",
"record_operation",
"record_cache_hit",
"record_cache_miss",
"print_stats",
"reset_stats",
]

309
src/common/database/utils/decorators.py Normal file
View File

@@ -0,0 +1,309 @@
"""数据库操作装饰器
提供常用的装饰器：
- @retry: 自动重试失败的数据库操作
- @timeout: 为数据库操作添加超时控制
- @cached: 自动缓存函数结果
"""
import asyncio
import functools
import hashlib
import time
from typing import Any, Awaitable, Callable, Optional, TypeVar
from sqlalchemy.exc import DBAPIError, OperationalError, TimeoutError as SQLTimeoutError
from src.common.database.optimization import get_cache
from src.common.logger import get_logger
logger = get_logger("database.decorators")
T = TypeVar("T")
F = TypeVar("F", bound=Callable[..., Awaitable[Any]])
def retry(
max_attempts: int = 3,
delay: float = 0.5,
backoff: float = 2.0,
exceptions: tuple[type[Exception], ...] = (OperationalError, DBAPIError, SQLTimeoutError),
):
"""重试装饰器
自动重试失败的数据库操作，适用于临时性错误
Args:
max_attempts: 最大尝试次数
delay: 初始延迟时间（秒）
backoff: 延迟倍数（指数退避）
exceptions: 需要重试的异常类型
Example:
@retry(max_attempts=3, delay=1.0)
async def query_data():
return await session.execute(stmt)
"""
def decorator(func: Callable[..., T]) -> Callable[..., T]:
@functools.wraps(func)
async def wrapper(*args: Any, **kwargs: Any) -> T:
last_exception = None
current_delay = delay
for attempt in range(1, max_attempts + 1):
try:
return await func(*args, **kwargs)
except exceptions as e:
last_exception = e
if attempt < max_attempts:
logger.warning(
f"{func.__name__} 失败 (尝试 {attempt}/{max_attempts}): {e}. "
f"等待 {current_delay:.2f}s 后重试..."
)
await asyncio.sleep(current_delay)
current_delay *= backoff
else:
logger.error(
f"{func.__name__} 在 {max_attempts} 次尝试后仍然失败: {e}",
exc_info=True,
)
# 所有尝试都失败
raise last_exception
return wrapper
return decorator
def timeout(seconds: float):
"""超时装饰器
为数据库操作添加超时控制
Args:
seconds: 超时时间（秒）
Example:
@timeout(30.0)
async def long_query():
return await session.execute(complex_stmt)
"""
def decorator(func: Callable[..., T]) -> Callable[..., T]:
@functools.wraps(func)
async def wrapper(*args: Any, **kwargs: Any) -> T:
try:
return await asyncio.wait_for(func(*args, **kwargs), timeout=seconds)
except asyncio.TimeoutError:
logger.error(f"{func.__name__} 执行超时 (>{seconds}s)")
raise TimeoutError(f"{func.__name__} 执行超时 (>{seconds}s)")
return wrapper
return decorator
def cached(
ttl: Optional[int] = 300,
key_prefix: Optional[str] = None,
use_args: bool = True,
use_kwargs: bool = True,
):
"""缓存装饰器
自动缓存函数返回值
Args:
ttl: 缓存过期时间（秒），None表示永不过期
key_prefix: 缓存键前缀，默认使用函数名
use_args: 是否将位置参数包含在缓存键中
use_kwargs: 是否将关键字参数包含在缓存键中
Example:
@cached(ttl=60, key_prefix="user_data")
async def get_user_info(user_id: str) -> dict:
return await query_user(user_id)
"""
def decorator(func: Callable[..., T]) -> Callable[..., T]:
@functools.wraps(func)
async def wrapper(*args: Any, **kwargs: Any) -> T:
# 生成缓存键
cache_key_parts = [key_prefix or func.__name__]
if use_args and args:
# 将位置参数转换为字符串
args_str = ",".join(str(arg) for arg in args)
args_hash = hashlib.md5(args_str.encode()).hexdigest()[:8]
cache_key_parts.append(f"args:{args_hash}")
if use_kwargs and kwargs:
# 将关键字参数转换为字符串（排序以保证一致性）
kwargs_str = ",".join(f"{k}={v}" for k, v in sorted(kwargs.items()))
kwargs_hash = hashlib.md5(kwargs_str.encode()).hexdigest()[:8]
cache_key_parts.append(f"kwargs:{kwargs_hash}")
cache_key = ":".join(cache_key_parts)
# 尝试从缓存获取
cache = await get_cache()
cached_result = await cache.get(cache_key)
if cached_result is not None:
logger.debug(f"缓存命中: {cache_key}")
return cached_result
# 执行函数
result = await func(*args, **kwargs)
# 写入缓存（注意：MultiLevelCache.set不支持ttl参数，使用L1缓存的默认TTL）
await cache.set(cache_key, result)
logger.debug(f"缓存写入: {cache_key}")
return result
return wrapper
return decorator
def measure_time(log_slow: Optional[float] = None):
"""性能测量装饰器
测量函数执行时间，可选择性记录慢查询
Args:
log_slow: 慢查询阈值（秒），超过此时间会记录warning日志
Example:
@measure_time(log_slow=1.0)
async def complex_query():
return await session.execute(stmt)
"""
def decorator(func: Callable[..., T]) -> Callable[..., T]:
@functools.wraps(func)
async def wrapper(*args: Any, **kwargs: Any) -> T:
start_time = time.perf_counter()
try:
result = await func(*args, **kwargs)
return result
finally:
elapsed = time.perf_counter() - start_time
if log_slow and elapsed > log_slow:
logger.warning(
f"{func.__name__} 执行缓慢: {elapsed:.3f}s (阈值: {log_slow}s)"
)
else:
logger.debug(f"{func.__name__} 执行时间: {elapsed:.3f}s")
return wrapper
return decorator
def transactional(auto_commit: bool = True, auto_rollback: bool = True):
"""事务装饰器
自动管理事务的提交和回滚
Args:
auto_commit: 是否自动提交
auto_rollback: 发生异常时是否自动回滚
Example:
@transactional()
async def update_multiple_records(session):
await session.execute(stmt1)
await session.execute(stmt2)
Note:
函数需要接受session参数
"""
def decorator(func: Callable[..., T]) -> Callable[..., T]:
@functools.wraps(func)
async def wrapper(*args: Any, **kwargs: Any) -> T:
# 查找session参数
session = None
if args:
from sqlalchemy.ext.asyncio import AsyncSession
for arg in args:
if isinstance(arg, AsyncSession):
session = arg
break
if not session and "session" in kwargs:
session = kwargs["session"]
if not session:
logger.warning(f"{func.__name__} 未找到session参数，跳过事务管理")
return await func(*args, **kwargs)
try:
result = await func(*args, **kwargs)
if auto_commit:
await session.commit()
logger.debug(f"{func.__name__} 事务已提交")
return result
except Exception as e:
if auto_rollback:
await session.rollback()
logger.error(f"{func.__name__} 事务已回滚: {e}")
raise
return wrapper
return decorator
# 组合装饰器示例
def db_operation(
retry_attempts: int = 3,
timeout_seconds: Optional[float] = None,
cache_ttl: Optional[int] = None,
measure: bool = True,
):
"""组合装饰器
组合多个装饰器，提供完整的数据库操作保护
Args:
retry_attempts: 重试次数
timeout_seconds: 超时时间
cache_ttl: 缓存时间
measure: 是否测量性能
Example:
@db_operation(retry_attempts=3, timeout_seconds=30, cache_ttl=60)
async def important_query():
return await complex_operation()
"""
def decorator(func: Callable[..., T]) -> Callable[..., T]:
# 从内到外应用装饰器
wrapped = func
if measure:
wrapped = measure_time(log_slow=1.0)(wrapped)
if cache_ttl:
wrapped = cached(ttl=cache_ttl)(wrapped)
if timeout_seconds:
wrapped = timeout(timeout_seconds)(wrapped)
if retry_attempts > 1:
wrapped = retry(max_attempts=retry_attempts)(wrapped)
return wrapped
return decorator

322
src/common/database/utils/monitoring.py Normal file
View File

@@ -0,0 +1,322 @@
"""数据库性能监控
提供数据库操作的性能监控和统计功能
"""
import time
from collections import defaultdict
from dataclasses import dataclass, field
from typing import Any, Optional
from src.common.logger import get_logger
logger = get_logger("database.monitoring")
@dataclass
class OperationMetrics:
"""操作指标"""
count: int = 0
total_time: float = 0.0
min_time: float = float("inf")
max_time: float = 0.0
error_count: int = 0
last_execution_time: Optional[float] = None
@property
def avg_time(self) -> float:
"""平均执行时间"""
return self.total_time / self.count if self.count > 0 else 0.0
def record_success(self, execution_time: float):
"""记录成功执行"""
self.count += 1
self.total_time += execution_time
self.min_time = min(self.min_time, execution_time)
self.max_time = max(self.max_time, execution_time)
self.last_execution_time = time.time()
def record_error(self):
"""记录错误"""
self.error_count += 1
@dataclass
class DatabaseMetrics:
"""数据库指标"""
# 操作统计
operations: dict[str, OperationMetrics] = field(default_factory=dict)
# 连接池统计
connection_acquired: int = 0
connection_released: int = 0
connection_errors: int = 0
# 缓存统计
cache_hits: int = 0
cache_misses: int = 0
cache_sets: int = 0
cache_invalidations: int = 0
# 批处理统计
batch_operations: int = 0
batch_items_total: int = 0
batch_avg_size: float = 0.0
# 预加载统计
preload_operations: int = 0
preload_hits: int = 0
@property
def cache_hit_rate(self) -> float:
"""缓存命中率"""
total = self.cache_hits + self.cache_misses
return self.cache_hits / total if total > 0 else 0.0
@property
def error_rate(self) -> float:
"""错误率"""
total_ops = sum(m.count for m in self.operations.values())
total_errors = sum(m.error_count for m in self.operations.values())
return total_errors / total_ops if total_ops > 0 else 0.0
def get_operation_metrics(self, operation_name: str) -> OperationMetrics:
"""获取操作指标"""
if operation_name not in self.operations:
self.operations[operation_name] = OperationMetrics()
return self.operations[operation_name]
class DatabaseMonitor:
"""数据库监控器
单例模式，收集和报告数据库性能指标
"""
_instance: Optional["DatabaseMonitor"] = None
_metrics: DatabaseMetrics
def __new__(cls):
if cls._instance is None:
cls._instance = super().__new__(cls)
cls._instance._metrics = DatabaseMetrics()
return cls._instance
def record_operation(
self,
operation_name: str,
execution_time: float,
success: bool = True,
):
"""记录操作"""
metrics = self._metrics.get_operation_metrics(operation_name)
if success:
metrics.record_success(execution_time)
else:
metrics.record_error()
def record_connection_acquired(self):
"""记录连接获取"""
self._metrics.connection_acquired += 1
def record_connection_released(self):
"""记录连接释放"""
self._metrics.connection_released += 1
def record_connection_error(self):
"""记录连接错误"""
self._metrics.connection_errors += 1
def record_cache_hit(self):
"""记录缓存命中"""
self._metrics.cache_hits += 1
def record_cache_miss(self):
"""记录缓存未命中"""
self._metrics.cache_misses += 1
def record_cache_set(self):
"""记录缓存设置"""
self._metrics.cache_sets += 1
def record_cache_invalidation(self):
"""记录缓存失效"""
self._metrics.cache_invalidations += 1
def record_batch_operation(self, batch_size: int):
"""记录批处理操作"""
self._metrics.batch_operations += 1
self._metrics.batch_items_total += batch_size
self._metrics.batch_avg_size = (
self._metrics.batch_items_total / self._metrics.batch_operations
)
def record_preload_operation(self, hit: bool = False):
"""记录预加载操作"""
self._metrics.preload_operations += 1
if hit:
self._metrics.preload_hits += 1
def get_metrics(self) -> DatabaseMetrics:
"""获取指标"""
return self._metrics
def get_summary(self) -> dict[str, Any]:
"""获取统计摘要"""
metrics = self._metrics
operation_summary = {}
for op_name, op_metrics in metrics.operations.items():
operation_summary[op_name] = {
"count": op_metrics.count,
"avg_time": f"{op_metrics.avg_time:.3f}s",
"min_time": f"{op_metrics.min_time:.3f}s",
"max_time": f"{op_metrics.max_time:.3f}s",
"error_count": op_metrics.error_count,
}
return {
"operations": operation_summary,
"connections": {
"acquired": metrics.connection_acquired,
"released": metrics.connection_released,
"errors": metrics.connection_errors,
"active": metrics.connection_acquired - metrics.connection_released,
},
"cache": {
"hits": metrics.cache_hits,
"misses": metrics.cache_misses,
"sets": metrics.cache_sets,
"invalidations": metrics.cache_invalidations,
"hit_rate": f"{metrics.cache_hit_rate:.2%}",
},
"batch": {
"operations": metrics.batch_operations,
"total_items": metrics.batch_items_total,
"avg_size": f"{metrics.batch_avg_size:.1f}",
},
"preload": {
"operations": metrics.preload_operations,
"hits": metrics.preload_hits,
"hit_rate": (
f"{metrics.preload_hits / metrics.preload_operations:.2%}"
if metrics.preload_operations > 0
else "N/A"
),
},
"overall": {
"error_rate": f"{metrics.error_rate:.2%}",
},
}
def print_summary(self):
"""打印统计摘要"""
summary = self.get_summary()
logger.info("=" * 60)
logger.info("数据库性能统计")
logger.info("=" * 60)
# 操作统计
if summary["operations"]:
logger.info("\n操作统计:")
for op_name, stats in summary["operations"].items():
logger.info(
f" {op_name}: "
f"次数={stats['count']}, "
f"平均={stats['avg_time']}, "
f"最小={stats['min_time']}, "
f"最大={stats['max_time']}, "
f"错误={stats['error_count']}"
)
# 连接池统计
logger.info("\n连接池:")
conn = summary["connections"]
logger.info(
f" 获取={conn['acquired']}, "
f"释放={conn['released']}, "
f"活跃={conn['active']}, "
f"错误={conn['errors']}"
)
# 缓存统计
logger.info("\n缓存:")
cache = summary["cache"]
logger.info(
f" 命中={cache['hits']}, "
f"未命中={cache['misses']}, "
f"设置={cache['sets']}, "
f"失效={cache['invalidations']}, "
f"命中率={cache['hit_rate']}"
)
# 批处理统计
logger.info("\n批处理:")
batch = summary["batch"]
logger.info(
f" 操作={batch['operations']}, "
f"总项目={batch['total_items']}, "
f"平均大小={batch['avg_size']}"
)
# 预加载统计
logger.info("\n预加载:")
preload = summary["preload"]
logger.info(
f" 操作={preload['operations']}, "
f"命中={preload['hits']}, "
f"命中率={preload['hit_rate']}"
)
# 整体统计
logger.info("\n整体:")
overall = summary["overall"]
logger.info(f" 错误率={overall['error_rate']}")
logger.info("=" * 60)
def reset(self):
"""重置统计"""
self._metrics = DatabaseMetrics()
logger.info("数据库监控统计已重置")
# 全局监控器实例
_monitor: Optional[DatabaseMonitor] = None
def get_monitor() -> DatabaseMonitor:
"""获取监控器实例"""
global _monitor
if _monitor is None:
_monitor = DatabaseMonitor()
return _monitor
# 便捷函数
def record_operation(operation_name: str, execution_time: float, success: bool = True):
"""记录操作"""
get_monitor().record_operation(operation_name, execution_time, success)
def record_cache_hit():
"""记录缓存命中"""
get_monitor().record_cache_hit()
def record_cache_miss():
"""记录缓存未命中"""
get_monitor().record_cache_miss()
def print_stats():
"""打印统计信息"""
get_monitor().print_summary()
def reset_stats():
"""重置统计"""
get_monitor().reset()