| Name | tk-db-utils JSON |
| Version |
0.1.6
JSON |
| download |
| home_page | None |
| Summary | 一个基于 SQLAlchemy 2.0 的高可复用数据库工具库，提供完整的 CRUD 操作、批量数据处理和高级数据库功能。 |
| upload_time | 2025-08-02 04:31:29 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
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| keywords |
crud
database
mysql
orm
postgresql
sqlalchemy
sqlite
|
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# tk-db-utils
一个基于 SQLAlchemy 2.0 的高可复用数据库工具库，提供完整的 CRUD 操作、批量数据处理和高级数据库功能。
## 特性
### 🚀 核心功能
- **SQLAlchemy 2.0 风格**: 完全采用 SQLAlchemy 2.0 的现代化 API
- **高可复用性**: 模块化设计，易于集成到任何项目中
- **完整的 CRUD 操作**: 支持增删改查的所有基本操作
- **批量数据处理**: 支持大批量数据的高效处理
- **多数据库支持**: 支持 MySQL、PostgreSQL、SQLite 等主流数据库
### 📊 高级数据库功能
- **INSERT IGNORE**: 支持忽略重复数据的批量插入
- **REPLACE INTO**: 支持数据替换的批量操作
- **分块处理**: 可配置批量大小，避免内存溢出
- **事务管理**: 自动事务处理，确保数据一致性
- **连接池管理**: 高效的数据库连接池管理
- **模式验证**: 自动检查 ORM 模型与数据库表结构的一致性
- **冲突检测**: 智能检测和处理唯一约束冲突
### 🛠️ 开发友好
- **类型提示**: 完整的 TypeScript 风格类型提示
- **错误处理**: 详细的错误信息和异常处理
- **日志系统**: 可配置的日志记录系统
- **向后兼容**: 保持与旧版本的兼容性
## 安装
```bash
pip install tk-db-utils
```
## 快速开始
### 1. 安装
```bash
pip install tk-db-utils
```
### 2. 配置数据库连接
本项目采用分离式配置管理，将敏感信息和引擎参数分别存储：
#### 敏感信息配置 (`.env`)
创建 `.env` 文件并配置数据库连接的敏感信息：
```env
# 数据库敏感信息配置
DB_HOST=localhost
DB_PORT=3306
DB_USERNAME=your_username
DB_PASSWORD=your_password
```
#### 引擎参数配置 (`db_config.toml`)
创建 `db_config.toml` 文件并配置数据库引擎参数：
```toml
[database]
database = "your_database_name"
driver = "pymysql"
dialect = "mysql"
charset = "utf8mb4"
collation = "utf8mb4_general_ci"
[engine]
echo = false
pool_size = 5
max_overflow = 10
pool_timeout = 30
pool_recycle = 3600
pool_pre_ping = true
```
> 💡 **提示**: 可以复制 `.env.example` 和 `db_config.example.toml` 文件作为模板开始配置。
>
> 📖 **详细配置指南**: 查看 [CONFIG_GUIDE.md](CONFIG_GUIDE.md) 了解完整的配置说明。
### 3. 基础配置
```python
from tk_db_utils import (
configure_database,
init_db,
set_logger_level,
SqlAlChemyBase,
DbOrmBaseMixedIn,
BaseCurd
)
from sqlalchemy import Column, Integer, String, DateTime
from datetime import datetime
# 配置日志级别
set_logger_level('INFO')
# 配置数据库连接
configure_database(
host="localhost",
port=3306,
username="your_username",
password="your_password",
database="your_database",
driver="mysql",
dialect="mysql+pymysql",
# 引擎参数
echo=True,
pool_size=5,
max_overflow=10
)
# 初始化数据库
init_db()
```
### 2. 定义数据模型
```python
class User(SqlAlChemyBase, DbOrmBaseMixedIn):
"""用户表模型"""
__tablename__ = 'users'
id = Column(Integer, primary_key=True, autoincrement=True)
username = Column(String(50), unique=True, nullable=False)
email = Column(String(100), unique=True, nullable=False)
created_at = Column(DateTime, default=datetime.now)
```
### 3. 基础 CRUD 操作
```python
# 创建 CRUD 实例
crud = BaseCurd()
# 插入单条记录
user_data = {"username": "john", "email": "john@example.com"}
user_id = crud.insert_one(User, user_data)
# 批量插入
users_data = [
{"username": "alice", "email": "alice@example.com"},
{"username": "bob", "email": "bob@example.com"},
]
inserted_count = crud.bulk_insert(User, users_data, chunk_size=1000)
# 查询所有记录
all_users = crud.select_all(User, limit=10, offset=0)
# 根据ID查询
user = crud.select_by_id(User, 1)
# 根据条件查询
users = crud.select_by_conditions(User, {"username": "alice"})
# 更新记录
updated_count = crud.update_by_id(User, 1, {"email": "new@example.com"})
# 删除记录
deleted_count = crud.delete_by_id(User, 1)
# 统计记录数
total_count = crud.count(User)
```
## 高级功能
### 模式验证功能
```python
from tk_db_utils import (
SchemaValidator,
validate_schema_consistency,
SchemaValidationError
)
# 方法1: 使用便捷函数进行验证
with get_session() as session:
try:
is_valid = validate_schema_consistency(
model=User,
engine=get_engine(),
session=session,
strict_mode=False, # 非严格模式
halt_on_error=True # 发现错误时暂停等待用户确认
)
if is_valid:
print("✅ 模式验证通过")
else:
print("❌ 模式验证失败")
except SchemaValidationError as e:
print(f"模式验证错误: {e}")
# 方法2: 使用 SchemaValidator 类进行详细验证
with get_session() as session:
validator = SchemaValidator(get_engine(), session)
result = validator.validate_model_schema(
model=User,
strict_mode=False
)
if not result['valid']:
print("发现的问题:")
for error in result['errors']:
print(f" - {error}")
```
### INSERT IGNORE 批量操作
```python
# 批量插入，忽略重复数据
duplicate_users = [
{"username": "alice", "email": "alice@example.com"}, # 可能重复
{"username": "david", "email": "david@example.com"}, # 新数据
]
# 使用 INSERT IGNORE，重复数据会被忽略
inserted_count = crud.bulk_insert_ignore(User, duplicate_users, chunk_size=1000)
print(f"实际插入了 {inserted_count} 条记录")
```
### REPLACE INTO 批量操作
```python
# 批量替换数据
replace_users = [
{"id": 1, "username": "john_updated", "email": "john.new@example.com"},
{"id": 100, "username": "new_user", "email": "new@example.com"}, # 新记录
]
# 使用 REPLACE INTO，存在则更新，不存在则插入
processed_count = crud.bulk_replace_into(User, replace_users, chunk_size=1000)
print(f"处理了 {processed_count} 条记录")
```
### 会话管理
```python
from tk_db_utils import get_session
# 使用上下文管理器
with get_session() as session:
# 在会话中进行复杂查询
users = session.query(User).filter(User.username.like('%admin%')).all()
# 创建新记录
new_user = User(username="admin", email="admin@example.com")
session.add(new_user)
# 会话结束时自动提交
```
### 原生 SQL 执行
```python
# 执行原生 SQL
result = crud.execute_raw_sql(
"SELECT COUNT(*) as total FROM users WHERE created_at > :date",
params={"date": "2024-01-01"}
)
```
## 数据库支持
### MySQL
```python
configure_database(
host="localhost",
port=3306,
username="root",
password="password",
database="mydb",
driver="mysql",
dialect="mysql+pymysql"
)
```
### PostgreSQL
```python
configure_database(
host="localhost",
port=5432,
username="postgres",
password="password",
database="mydb",
driver="postgresql",
dialect="postgresql+psycopg2"
)
```
### SQLite
```python
configure_database(
host="",
port=0,
username="",
password="",
database="mydb.db",
driver="sqlite",
dialect="sqlite"
)
```
## 环境变量配置
你也可以通过环境变量来配置数据库连接：
```bash
# 数据库连接配置
export DB_HOST=localhost
export DB_PORT=3306
export DB_USERNAME=root
export DB_PASSWORD=password
export DB_DATABASE=mydb
export DB_DRIVER=mysql
export DB_DIALECT=mysql+pymysql
# 引擎参数配置
export DB_ECHO=true
export DB_POOL_SIZE=5
export DB_MAX_OVERFLOW=10
export DB_POOL_TIMEOUT=30
export DB_POOL_RECYCLE=3600
```
## 日志配置
```python
from tk_db_tool import set_logger_level, set_message_handler, set_message_config
import logging
# 设置日志级别
set_logger_level('DEBUG')
# 设置自定义日志处理器
handler = logging.FileHandler('app.log')
set_message_handler(handler)
# 设置日志配置
logger = logging.getLogger('my_app')
set_message_config(logger)
```
## 性能优化
### 批量操作建议
```python
# 对于大量数据，建议使用适当的批量大小
# 一般建议 1000-5000 条记录为一批
# 小数据量
crud.bulk_insert(User, small_data, chunk_size=1000)
# 大数据量
crud.bulk_insert(User, large_data, chunk_size=3000)
# 超大数据量
crud.bulk_insert(User, huge_data, chunk_size=5000)
```
### 连接池配置
```python
configure_database(
# ... 其他配置
pool_size=10, # 连接池大小
max_overflow=20, # 最大溢出连接数
pool_timeout=30, # 获取连接超时时间
pool_recycle=3600, # 连接回收时间
)
```
## 错误处理
```python
try:
crud.bulk_insert(User, invalid_data)
except ValueError as e:
print(f"数据验证错误: {e}")
except RuntimeError as e:
print(f"数据库操作错误: {e}")
except Exception as e:
print(f"未知错误: {e}")
```
## 迁移指南
### 从旧版本迁移
如果你正在使用旧版本的 `bulk_insert_ignore_in_chunks` 方法：
```python
# 旧方法（仍然支持，但会显示警告）
crud.bulk_insert_ignore_in_chunks(User, data, chunk_size=1000)
# 新方法（推荐）
crud.bulk_insert_ignore(User, data, chunk_size=1000)
```
## 完整示例
查看 `example.py` 文件获取完整的使用示例，包括：
- 数据库配置
- 模型定义
- CRUD 操作
- INSERT IGNORE 和 REPLACE INTO
- 会话管理
- 错误处理
## 依赖要求
- Python >= 3.11
- SQLAlchemy >= 2.0
- PyMySQL (用于 MySQL)
- psycopg2 (用于 PostgreSQL)
- python-dotenv (用于环境变量)
- pydantic (用于数据验证)
## 许可证
MIT License
## 贡献
欢迎提交 Issue 和 Pull Request！
## 更新日志
### v0.1.2
- ✨ 重构为 SQLAlchemy 2.0 风格
- ✨ 新增 INSERT IGNORE 和 REPLACE INTO 支持
- ✨ 新增完整的 CRUD 操作方法
- ✨ 新增灵活的数据库配置系统
- ✨ 修复 message 模块在 pip 安装后的使用问题
- ✨ 新增批量操作的分块处理
- ✨ 新增多数据库支持
- ✨ 新增完整的类型提示
- ✨ 新增详细的错误处理和日志记录
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"description": "# tk-db-utils\n\n\u4e00\u4e2a\u57fa\u4e8e SQLAlchemy 2.0 \u7684\u9ad8\u53ef\u590d\u7528\u6570\u636e\u5e93\u5de5\u5177\u5e93\uff0c\u63d0\u4f9b\u5b8c\u6574\u7684 CRUD \u64cd\u4f5c\u3001\u6279\u91cf\u6570\u636e\u5904\u7406\u548c\u9ad8\u7ea7\u6570\u636e\u5e93\u529f\u80fd\u3002\n\n## \u7279\u6027\n\n### \ud83d\ude80 \u6838\u5fc3\u529f\u80fd\n- **SQLAlchemy 2.0 \u98ce\u683c**: \u5b8c\u5168\u91c7\u7528 SQLAlchemy 2.0 \u7684\u73b0\u4ee3\u5316 API\n- **\u9ad8\u53ef\u590d\u7528\u6027**: \u6a21\u5757\u5316\u8bbe\u8ba1\uff0c\u6613\u4e8e\u96c6\u6210\u5230\u4efb\u4f55\u9879\u76ee\u4e2d\n- **\u5b8c\u6574\u7684 CRUD \u64cd\u4f5c**: \u652f\u6301\u589e\u5220\u6539\u67e5\u7684\u6240\u6709\u57fa\u672c\u64cd\u4f5c\n- **\u6279\u91cf\u6570\u636e\u5904\u7406**: \u652f\u6301\u5927\u6279\u91cf\u6570\u636e\u7684\u9ad8\u6548\u5904\u7406\n- **\u591a\u6570\u636e\u5e93\u652f\u6301**: \u652f\u6301 MySQL\u3001PostgreSQL\u3001SQLite \u7b49\u4e3b\u6d41\u6570\u636e\u5e93\n\n### \ud83d\udcca \u9ad8\u7ea7\u6570\u636e\u5e93\u529f\u80fd\n- **INSERT IGNORE**: \u652f\u6301\u5ffd\u7565\u91cd\u590d\u6570\u636e\u7684\u6279\u91cf\u63d2\u5165\n- **REPLACE INTO**: \u652f\u6301\u6570\u636e\u66ff\u6362\u7684\u6279\u91cf\u64cd\u4f5c\n- **\u5206\u5757\u5904\u7406**: \u53ef\u914d\u7f6e\u6279\u91cf\u5927\u5c0f\uff0c\u907f\u514d\u5185\u5b58\u6ea2\u51fa\n- **\u4e8b\u52a1\u7ba1\u7406**: \u81ea\u52a8\u4e8b\u52a1\u5904\u7406\uff0c\u786e\u4fdd\u6570\u636e\u4e00\u81f4\u6027\n- **\u8fde\u63a5\u6c60\u7ba1\u7406**: \u9ad8\u6548\u7684\u6570\u636e\u5e93\u8fde\u63a5\u6c60\u7ba1\u7406\n- **\u6a21\u5f0f\u9a8c\u8bc1**: \u81ea\u52a8\u68c0\u67e5 ORM \u6a21\u578b\u4e0e\u6570\u636e\u5e93\u8868\u7ed3\u6784\u7684\u4e00\u81f4\u6027\n- **\u51b2\u7a81\u68c0\u6d4b**: \u667a\u80fd\u68c0\u6d4b\u548c\u5904\u7406\u552f\u4e00\u7ea6\u675f\u51b2\u7a81\n\n### \ud83d\udee0\ufe0f \u5f00\u53d1\u53cb\u597d\n- **\u7c7b\u578b\u63d0\u793a**: \u5b8c\u6574\u7684 TypeScript \u98ce\u683c\u7c7b\u578b\u63d0\u793a\n- **\u9519\u8bef\u5904\u7406**: \u8be6\u7ec6\u7684\u9519\u8bef\u4fe1\u606f\u548c\u5f02\u5e38\u5904\u7406\n- **\u65e5\u5fd7\u7cfb\u7edf**: \u53ef\u914d\u7f6e\u7684\u65e5\u5fd7\u8bb0\u5f55\u7cfb\u7edf\n- **\u5411\u540e\u517c\u5bb9**: \u4fdd\u6301\u4e0e\u65e7\u7248\u672c\u7684\u517c\u5bb9\u6027\n\n## \u5b89\u88c5\n\n```bash\npip install tk-db-utils\n```\n\n## \u5feb\u901f\u5f00\u59cb\n\n### 1. \u5b89\u88c5\n\n```bash\npip install tk-db-utils\n```\n\n### 2. \u914d\u7f6e\u6570\u636e\u5e93\u8fde\u63a5\n\n\u672c\u9879\u76ee\u91c7\u7528\u5206\u79bb\u5f0f\u914d\u7f6e\u7ba1\u7406\uff0c\u5c06\u654f\u611f\u4fe1\u606f\u548c\u5f15\u64ce\u53c2\u6570\u5206\u522b\u5b58\u50a8\uff1a\n\n#### \u654f\u611f\u4fe1\u606f\u914d\u7f6e (`.env`)\n\u521b\u5efa `.env` \u6587\u4ef6\u5e76\u914d\u7f6e\u6570\u636e\u5e93\u8fde\u63a5\u7684\u654f\u611f\u4fe1\u606f\uff1a\n\n```env\n# \u6570\u636e\u5e93\u654f\u611f\u4fe1\u606f\u914d\u7f6e\nDB_HOST=localhost\nDB_PORT=3306\nDB_USERNAME=your_username\nDB_PASSWORD=your_password\n```\n\n#### \u5f15\u64ce\u53c2\u6570\u914d\u7f6e (`db_config.toml`)\n\u521b\u5efa `db_config.toml` \u6587\u4ef6\u5e76\u914d\u7f6e\u6570\u636e\u5e93\u5f15\u64ce\u53c2\u6570\uff1a\n\n```toml\n[database]\ndatabase = \"your_database_name\"\ndriver = \"pymysql\"\ndialect = \"mysql\"\ncharset = \"utf8mb4\"\ncollation = \"utf8mb4_general_ci\"\n\n[engine]\necho = false\npool_size = 5\nmax_overflow = 10\npool_timeout = 30\npool_recycle = 3600\npool_pre_ping = true\n```\n\n> \ud83d\udca1 **\u63d0\u793a**: \u53ef\u4ee5\u590d\u5236 `.env.example` \u548c `db_config.example.toml` \u6587\u4ef6\u4f5c\u4e3a\u6a21\u677f\u5f00\u59cb\u914d\u7f6e\u3002\n> \n> \ud83d\udcd6 **\u8be6\u7ec6\u914d\u7f6e\u6307\u5357**: \u67e5\u770b [CONFIG_GUIDE.md](CONFIG_GUIDE.md) \u4e86\u89e3\u5b8c\u6574\u7684\u914d\u7f6e\u8bf4\u660e\u3002\n\n### 3. \u57fa\u7840\u914d\u7f6e\n\n```python\nfrom tk_db_utils import (\n configure_database,\n init_db,\n set_logger_level,\n SqlAlChemyBase,\n DbOrmBaseMixedIn,\n BaseCurd\n)\nfrom sqlalchemy import Column, Integer, String, DateTime\nfrom datetime import datetime\n\n# \u914d\u7f6e\u65e5\u5fd7\u7ea7\u522b\nset_logger_level('INFO')\n\n# \u914d\u7f6e\u6570\u636e\u5e93\u8fde\u63a5\nconfigure_database(\n host=\"localhost\",\n port=3306,\n username=\"your_username\",\n password=\"your_password\",\n database=\"your_database\",\n driver=\"mysql\",\n dialect=\"mysql+pymysql\",\n # \u5f15\u64ce\u53c2\u6570\n echo=True,\n pool_size=5,\n max_overflow=10\n)\n\n# \u521d\u59cb\u5316\u6570\u636e\u5e93\ninit_db()\n```\n\n### 2. \u5b9a\u4e49\u6570\u636e\u6a21\u578b\n\n```python\nclass User(SqlAlChemyBase, DbOrmBaseMixedIn):\n \"\"\"\u7528\u6237\u8868\u6a21\u578b\"\"\"\n __tablename__ = 'users'\n \n id = Column(Integer, primary_key=True, autoincrement=True)\n username = Column(String(50), unique=True, nullable=False)\n email = Column(String(100), unique=True, nullable=False)\n created_at = Column(DateTime, default=datetime.now)\n```\n\n### 3. \u57fa\u7840 CRUD \u64cd\u4f5c\n\n```python\n# \u521b\u5efa CRUD \u5b9e\u4f8b\ncrud = BaseCurd()\n\n# \u63d2\u5165\u5355\u6761\u8bb0\u5f55\nuser_data = {\"username\": \"john\", \"email\": \"john@example.com\"}\nuser_id = crud.insert_one(User, user_data)\n\n# \u6279\u91cf\u63d2\u5165\nusers_data = [\n {\"username\": \"alice\", \"email\": \"alice@example.com\"},\n {\"username\": \"bob\", \"email\": \"bob@example.com\"},\n]\ninserted_count = crud.bulk_insert(User, users_data, chunk_size=1000)\n\n# \u67e5\u8be2\u6240\u6709\u8bb0\u5f55\nall_users = crud.select_all(User, limit=10, offset=0)\n\n# \u6839\u636eID\u67e5\u8be2\nuser = crud.select_by_id(User, 1)\n\n# \u6839\u636e\u6761\u4ef6\u67e5\u8be2\nusers = crud.select_by_conditions(User, {\"username\": \"alice\"})\n\n# \u66f4\u65b0\u8bb0\u5f55\nupdated_count = crud.update_by_id(User, 1, {\"email\": \"new@example.com\"})\n\n# \u5220\u9664\u8bb0\u5f55\ndeleted_count = crud.delete_by_id(User, 1)\n\n# \u7edf\u8ba1\u8bb0\u5f55\u6570\ntotal_count = crud.count(User)\n```\n\n## \u9ad8\u7ea7\u529f\u80fd\n\n### \u6a21\u5f0f\u9a8c\u8bc1\u529f\u80fd\n\n```python\nfrom tk_db_utils import (\n SchemaValidator,\n validate_schema_consistency,\n SchemaValidationError\n)\n\n# \u65b9\u6cd51: \u4f7f\u7528\u4fbf\u6377\u51fd\u6570\u8fdb\u884c\u9a8c\u8bc1\nwith get_session() as session:\n try:\n is_valid = validate_schema_consistency(\n model=User,\n engine=get_engine(),\n session=session,\n strict_mode=False, # \u975e\u4e25\u683c\u6a21\u5f0f\n halt_on_error=True # \u53d1\u73b0\u9519\u8bef\u65f6\u6682\u505c\u7b49\u5f85\u7528\u6237\u786e\u8ba4\n )\n \n if is_valid:\n print(\"\u2705 \u6a21\u5f0f\u9a8c\u8bc1\u901a\u8fc7\")\n else:\n print(\"\u274c \u6a21\u5f0f\u9a8c\u8bc1\u5931\u8d25\")\n \n except SchemaValidationError as e:\n print(f\"\u6a21\u5f0f\u9a8c\u8bc1\u9519\u8bef: {e}\")\n\n# \u65b9\u6cd52: \u4f7f\u7528 SchemaValidator \u7c7b\u8fdb\u884c\u8be6\u7ec6\u9a8c\u8bc1\nwith get_session() as session:\n validator = SchemaValidator(get_engine(), session)\n \n result = validator.validate_model_schema(\n model=User,\n strict_mode=False\n )\n \n if not result['valid']:\n print(\"\u53d1\u73b0\u7684\u95ee\u9898:\")\n for error in result['errors']:\n print(f\" - {error}\")\n```\n\n### INSERT IGNORE \u6279\u91cf\u64cd\u4f5c\n\n```python\n# \u6279\u91cf\u63d2\u5165\uff0c\u5ffd\u7565\u91cd\u590d\u6570\u636e\nduplicate_users = [\n {\"username\": \"alice\", \"email\": \"alice@example.com\"}, # \u53ef\u80fd\u91cd\u590d\n {\"username\": \"david\", \"email\": \"david@example.com\"}, # \u65b0\u6570\u636e\n]\n\n# \u4f7f\u7528 INSERT IGNORE\uff0c\u91cd\u590d\u6570\u636e\u4f1a\u88ab\u5ffd\u7565\ninserted_count = crud.bulk_insert_ignore(User, duplicate_users, chunk_size=1000)\nprint(f\"\u5b9e\u9645\u63d2\u5165\u4e86 {inserted_count} \u6761\u8bb0\u5f55\")\n```\n\n### REPLACE INTO \u6279\u91cf\u64cd\u4f5c\n\n```python\n# \u6279\u91cf\u66ff\u6362\u6570\u636e\nreplace_users = [\n {\"id\": 1, \"username\": \"john_updated\", \"email\": \"john.new@example.com\"},\n {\"id\": 100, \"username\": \"new_user\", \"email\": \"new@example.com\"}, # \u65b0\u8bb0\u5f55\n]\n\n# \u4f7f\u7528 REPLACE INTO\uff0c\u5b58\u5728\u5219\u66f4\u65b0\uff0c\u4e0d\u5b58\u5728\u5219\u63d2\u5165\nprocessed_count = crud.bulk_replace_into(User, replace_users, chunk_size=1000)\nprint(f\"\u5904\u7406\u4e86 {processed_count} \u6761\u8bb0\u5f55\")\n```\n\n### \u4f1a\u8bdd\u7ba1\u7406\n\n```python\nfrom tk_db_utils import get_session\n\n# \u4f7f\u7528\u4e0a\u4e0b\u6587\u7ba1\u7406\u5668\nwith get_session() as session:\n # \u5728\u4f1a\u8bdd\u4e2d\u8fdb\u884c\u590d\u6742\u67e5\u8be2\n users = session.query(User).filter(User.username.like('%admin%')).all()\n \n # \u521b\u5efa\u65b0\u8bb0\u5f55\n new_user = User(username=\"admin\", email=\"admin@example.com\")\n session.add(new_user)\n # \u4f1a\u8bdd\u7ed3\u675f\u65f6\u81ea\u52a8\u63d0\u4ea4\n```\n\n### \u539f\u751f SQL \u6267\u884c\n\n```python\n# \u6267\u884c\u539f\u751f SQL\nresult = crud.execute_raw_sql(\n \"SELECT COUNT(*) as total FROM users WHERE created_at > :date\",\n params={\"date\": \"2024-01-01\"}\n)\n```\n\n## \u6570\u636e\u5e93\u652f\u6301\n\n### MySQL\n```python\nconfigure_database(\n host=\"localhost\",\n port=3306,\n username=\"root\",\n password=\"password\",\n database=\"mydb\",\n driver=\"mysql\",\n dialect=\"mysql+pymysql\"\n)\n```\n\n### PostgreSQL\n```python\nconfigure_database(\n host=\"localhost\",\n port=5432,\n username=\"postgres\",\n password=\"password\",\n database=\"mydb\",\n driver=\"postgresql\",\n dialect=\"postgresql+psycopg2\"\n)\n```\n\n### SQLite\n```python\nconfigure_database(\n host=\"\",\n port=0,\n username=\"\",\n password=\"\",\n database=\"mydb.db\",\n driver=\"sqlite\",\n dialect=\"sqlite\"\n)\n```\n\n## \u73af\u5883\u53d8\u91cf\u914d\u7f6e\n\n\u4f60\u4e5f\u53ef\u4ee5\u901a\u8fc7\u73af\u5883\u53d8\u91cf\u6765\u914d\u7f6e\u6570\u636e\u5e93\u8fde\u63a5\uff1a\n\n```bash\n# \u6570\u636e\u5e93\u8fde\u63a5\u914d\u7f6e\nexport DB_HOST=localhost\nexport DB_PORT=3306\nexport DB_USERNAME=root\nexport DB_PASSWORD=password\nexport DB_DATABASE=mydb\nexport DB_DRIVER=mysql\nexport DB_DIALECT=mysql+pymysql\n\n# \u5f15\u64ce\u53c2\u6570\u914d\u7f6e\nexport DB_ECHO=true\nexport DB_POOL_SIZE=5\nexport DB_MAX_OVERFLOW=10\nexport DB_POOL_TIMEOUT=30\nexport DB_POOL_RECYCLE=3600\n```\n\n## \u65e5\u5fd7\u914d\u7f6e\n\n```python\nfrom tk_db_tool import set_logger_level, set_message_handler, set_message_config\nimport logging\n\n# \u8bbe\u7f6e\u65e5\u5fd7\u7ea7\u522b\nset_logger_level('DEBUG')\n\n# \u8bbe\u7f6e\u81ea\u5b9a\u4e49\u65e5\u5fd7\u5904\u7406\u5668\nhandler = logging.FileHandler('app.log')\nset_message_handler(handler)\n\n# \u8bbe\u7f6e\u65e5\u5fd7\u914d\u7f6e\nlogger = logging.getLogger('my_app')\nset_message_config(logger)\n```\n\n## \u6027\u80fd\u4f18\u5316\n\n### \u6279\u91cf\u64cd\u4f5c\u5efa\u8bae\n\n```python\n# \u5bf9\u4e8e\u5927\u91cf\u6570\u636e\uff0c\u5efa\u8bae\u4f7f\u7528\u9002\u5f53\u7684\u6279\u91cf\u5927\u5c0f\n# \u4e00\u822c\u5efa\u8bae 1000-5000 \u6761\u8bb0\u5f55\u4e3a\u4e00\u6279\n\n# \u5c0f\u6570\u636e\u91cf\ncrud.bulk_insert(User, small_data, chunk_size=1000)\n\n# \u5927\u6570\u636e\u91cf\ncrud.bulk_insert(User, large_data, chunk_size=3000)\n\n# \u8d85\u5927\u6570\u636e\u91cf\ncrud.bulk_insert(User, huge_data, chunk_size=5000)\n```\n\n### \u8fde\u63a5\u6c60\u914d\u7f6e\n\n```python\nconfigure_database(\n # ... \u5176\u4ed6\u914d\u7f6e\n pool_size=10, # \u8fde\u63a5\u6c60\u5927\u5c0f\n max_overflow=20, # \u6700\u5927\u6ea2\u51fa\u8fde\u63a5\u6570\n pool_timeout=30, # \u83b7\u53d6\u8fde\u63a5\u8d85\u65f6\u65f6\u95f4\n pool_recycle=3600, # \u8fde\u63a5\u56de\u6536\u65f6\u95f4\n)\n```\n\n## \u9519\u8bef\u5904\u7406\n\n```python\ntry:\n crud.bulk_insert(User, invalid_data)\nexcept ValueError as e:\n print(f\"\u6570\u636e\u9a8c\u8bc1\u9519\u8bef: {e}\")\nexcept RuntimeError as e:\n print(f\"\u6570\u636e\u5e93\u64cd\u4f5c\u9519\u8bef: {e}\")\nexcept Exception as e:\n print(f\"\u672a\u77e5\u9519\u8bef: {e}\")\n```\n\n## \u8fc1\u79fb\u6307\u5357\n\n### \u4ece\u65e7\u7248\u672c\u8fc1\u79fb\n\n\u5982\u679c\u4f60\u6b63\u5728\u4f7f\u7528\u65e7\u7248\u672c\u7684 `bulk_insert_ignore_in_chunks` \u65b9\u6cd5\uff1a\n\n```python\n# \u65e7\u65b9\u6cd5\uff08\u4ecd\u7136\u652f\u6301\uff0c\u4f46\u4f1a\u663e\u793a\u8b66\u544a\uff09\ncrud.bulk_insert_ignore_in_chunks(User, data, chunk_size=1000)\n\n# \u65b0\u65b9\u6cd5\uff08\u63a8\u8350\uff09\ncrud.bulk_insert_ignore(User, data, chunk_size=1000)\n```\n\n## \u5b8c\u6574\u793a\u4f8b\n\n\u67e5\u770b `example.py` \u6587\u4ef6\u83b7\u53d6\u5b8c\u6574\u7684\u4f7f\u7528\u793a\u4f8b\uff0c\u5305\u62ec\uff1a\n- \u6570\u636e\u5e93\u914d\u7f6e\n- \u6a21\u578b\u5b9a\u4e49\n- CRUD \u64cd\u4f5c\n- INSERT IGNORE \u548c REPLACE INTO\n- \u4f1a\u8bdd\u7ba1\u7406\n- \u9519\u8bef\u5904\u7406\n\n## \u4f9d\u8d56\u8981\u6c42\n\n- Python >= 3.11\n- SQLAlchemy >= 2.0\n- PyMySQL (\u7528\u4e8e MySQL)\n- psycopg2 (\u7528\u4e8e PostgreSQL)\n- python-dotenv (\u7528\u4e8e\u73af\u5883\u53d8\u91cf)\n- pydantic (\u7528\u4e8e\u6570\u636e\u9a8c\u8bc1)\n\n## \u8bb8\u53ef\u8bc1\n\nMIT License\n\n## \u8d21\u732e\n\n\u6b22\u8fce\u63d0\u4ea4 Issue \u548c Pull Request\uff01\n\n## \u66f4\u65b0\u65e5\u5fd7\n\n### v0.1.2\n- \u2728 \u91cd\u6784\u4e3a SQLAlchemy 2.0 \u98ce\u683c\n- \u2728 \u65b0\u589e INSERT IGNORE \u548c REPLACE INTO \u652f\u6301\n- \u2728 \u65b0\u589e\u5b8c\u6574\u7684 CRUD \u64cd\u4f5c\u65b9\u6cd5\n- \u2728 \u65b0\u589e\u7075\u6d3b\u7684\u6570\u636e\u5e93\u914d\u7f6e\u7cfb\u7edf\n- \u2728 \u4fee\u590d message \u6a21\u5757\u5728 pip \u5b89\u88c5\u540e\u7684\u4f7f\u7528\u95ee\u9898\n- \u2728 \u65b0\u589e\u6279\u91cf\u64cd\u4f5c\u7684\u5206\u5757\u5904\u7406\n- \u2728 \u65b0\u589e\u591a\u6570\u636e\u5e93\u652f\u6301\n- \u2728 \u65b0\u589e\u5b8c\u6574\u7684\u7c7b\u578b\u63d0\u793a\n- \u2728 \u65b0\u589e\u8be6\u7ec6\u7684\u9519\u8bef\u5904\u7406\u548c\u65e5\u5fd7\u8bb0\u5f55\n",
"bugtrack_url": null,
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