Async ORM for MySQL and PostgreSQL, powered by Pydantic v2
Project description
vorm
Async ORM for Python 3.10+ — MySQL and PostgreSQL, 基于 Pydantic v2 开发, 以pydantic作为数据模型,同时增加额外字段属性作为数据库字段必要属性,从而可以操作数据库表结构.这个库以精简为主,不提供外键以及多对一,多对多等键功能
特性
- Async-first — 所有 I/O 均为
async/await,基于asyncmy(MySQL)和asyncpg(PostgreSQL) - Pydantic 原生 — 模型即
BaseModel,享受类型校验、序列化等全部 Pydantic 功能 - 表达式 API —
==!=><like()between()in_()json_path()生成参数化 SQL - DDL 自动同步 —
Model.sync_table()建表、新增列、修改列、创建索引,只增改不删 - JSON 原生支持 —
dict/list自动映射 JSON/JSONB,默认值使用数据库原生函数 - 时间戳行为 —
timestamp_behavior="create"|"update"|"both"控制自动时间戳 - 联合索引 —
Meta.indexes声明,Field(index=True)简写,只增不删 - 断连自动重试 — 连接断开时自动重试 3 次(指数退避),非连接错误直接抛出
- 多表 JOIN — 显式 ON 条件,
Row对象支持属性/字典双模式访问 - 原生 SQL —
execute_raw()/fetch_raw()/fetch_one_raw()绕过 ORM 直接执行
安装
pip install vorm
| 数据库 | 驱动 | 说明 |
|---|---|---|
| MySQL | asyncmy |
连接池 + 事务 |
| PostgreSQL | asyncpg |
连接池 + 事务 |
字段示例
import datetime
from vorm import Field, Model
from vorm.ddl import Index
from enum import Enum
class FruitEnum(str, Enum):
pear = 'pear'
banana = 'banana'
apple = 'apple'
class BaseField(Model):
id_base:int|None=None
class AllFieldTypes(BaseField):
"""覆盖所有 Python 类型 → SQL 类型的映射。"""
class Meta:
table = "all_field_types"
indexes = [Index(fields=("short_text",),unique=True),
Index(fields=("age","score"))
]
# -- 主键 ---------------------------------------------------------
id: int = Field(primary_key=True, auto_increment=True)
# MySQL → `id` INT AUTO_INCREMENT PRIMARY KEY
# PG → "id" SERIAL PRIMARY KEY
# -- 字符串 -------------------------------------------------------
short_text: str = Field(max_length=200, nullable=True,default="",comment="短字符")
# max_length 指定 → VARCHAR(100)
free_text: str | None = None
# 不指定 max_length → TEXT
# -- 数值 ---------------------------------------------------------
age: int | None = 0
# MySQL → INT, PG → INTEGER
score: float = Field(default=0)
# MySQL → DOUBLE, PG → DOUBLE PRECISION
price: decimal.Decimal | None = Field(default=0.0,db_type="decimal(15,5)")
# MySQL → DECIMAL(18,6), PG → DECIMAL(18,6)
# -- 布尔 ---------------------------------------------------------
is_active: bool = Field(default=True)
# MySQL → BOOL, PG → BOOLEAN
# -- 日期时间 -----------------------------------------------------
created_at: datetime.datetime = Field(
default_factory=datetime.datetime.now,
timestamp_behavior="create", # INSERT 时写入,UPDATE 时跳过
)
# MySQL → DATETIME, PG → TIMESTAMP
updated_at: datetime.datetime = Field(
default_factory=datetime.datetime.now,
timestamp_behavior="both", # INSERT / UPDATE 都会刷新
index=True
)
event_date: datetime.date | None = None
# MySQL → DATE, PG → DATE
# -- 二进制 -------------------------------------------------------
payload: bytes | None = None
# MySQL → BLOB, PG → BYTEA
# -- JSON ---------------------------------------------------------
config: dict = Field(default_factory=dict)
# MySQL → JSON, PG → JSONB
# DDL: MySQL DEFAULT (JSON_OBJECT()), PG DEFAULT jsonb_build_object()
tags: list = Field(default_factory=list)
# MySQL → JSON, PG → JSONB
# DDL: MySQL DEFAULT (JSON_ARRAY()), PG DEFAULT jsonb_build_array()
new_field:list = Field(default_factory=list)
enum_f:FruitEnum = Field(default=FruitEnum.banana)
快速开始
1. 定义模型
import datetime
from vorm import Field, Model
from vorm.ddl import Index
class Article(Model):
class Meta:
table = "articles"
indexes = [
Index(fields=("title",)), # 单列索引
Index(fields=("title", "created_at")), # 联合索引
]
id: int = Field(primary_key=True, auto_increment=True)
title: str = Field(max_length=200)
body: str # → TEXT
tags: list = Field(default_factory=list) # → JSON/JSONB
view_count: int = Field(default=0)
created_at: datetime.datetime = Field(
default_factory=datetime.datetime.now,
timestamp_behavior="create", # INSERT 写入,UPDATE 保留原值
)
updated_at: datetime.datetime = Field(
default_factory=datetime.datetime.now,
timestamp_behavior="both", # INSERT + UPDATE 均刷新
)
2. 连接数据库
from vorm.engine import create_mysql_engine, create_postgresql_engine
# Mysql 连接池(推荐)
mysql_engine = await engine.create_mysql_engine(host="127.0.0.1",port=3306,user="username",password="password",database="test",minsize=10,maxsize=20)
session = mysql_engine.session()
# PostgreSQL 连接池(推荐)
postgresql_engine = await engine.create_postgresql_engine(host="127.0.0.1",port=3306,user="username",password="password",database="test",min_size=50,max_size=100)
session = postgresql_engine.session()
3. 建表 & 索引
await Article.sync_table(session.dialect)
首次 → CREATE TABLE IF NOT EXISTS + CREATE INDEX。后续新增列/修改类型/新增索引分别合并为单条 ALTER TABLE / CREATE INDEX。永不删除列、表或索引。
4. CRUD
# INSERT
article = Article(title="Hello", tags=["tech"])
await session.save(article) # 单条插入,id 自动回填
await session.bulk_insert([...]) # 批量插入
# SELECT
await session.query(Article).all() # 全表
await session.query(Article).filter(Article.view_count >= 10).all() # 条件
await session.query(Article).filter(Article.id == 1).one() # 精确一条
await session.query(Article).filter(Article.title.like("Post%")).first() # 有则返无则 None
await session.query(Article).count() # 计数
await session.query(Article).filter(Article.id == 1).exists() # 存在性
await session.query(Article).order_by(Article.id.desc()).limit(10).offset(20).all() # 分页
# UPDATE
article.view_count += 1
await session.save(article) # 主键不为 None → UPDATE
await session.query(Article).filter(Article.view_count == 0).update(view_count=1) # 批量
# DELETE
await session.delete(article)
await session.query(Article).filter(Article.view_count == 0).delete() # 批量
5. 高级查询
# LIKE / BETWEEN
await session.query(User).filter(User.name.like("张%")).all()
await session.query(Book).filter(Book.price.between(10, 50)).all()
# IN / IS NULL
await session.query(User).filter(User.role.in_(["admin", "editor"])).all()
await session.query(User).filter(User.email.is_null()).all()
# AND / OR
cond = (User.age >= 18) & (User.role == "member") # AND
cond = (User.role == "admin") | (User.role == "staff") # OR
# JSON 路径查询(MySQL: column->>'$.path' / PG: column->'key'->>'sub')
await session.query(Article).filter(Article.tags.json_path("$[0]") == "tech").all()
await session.query(Product).filter(Product.data.json_path("$.price") > 100).all()
6. 多表 JOIN
# 两表 INNER JOIN
rows = await session.query(Author).join(
Book, on=Author.id == Book.author_id
).filter(Book.price > 20).order_by(Author.name.asc()).all()
# LEFT JOIN
rows = await session.query(Author).join(
Book, on=Author.id == Book.author_id, type="LEFT"
).all()
# Row 访问
print(rows[0].authors.name) # 属性
print(rows[0]["authors.id"]) # 字典 key
print(dict(rows[0])) # 扁平字典
# select() 可用于查询指定字段,alias()可用于别名
d = await mysql_session.query(AllFieldTypes)\
.join(JoinTable,on=AllFieldTypes.id==JoinTable.all_id,type="left")\
.join(JoinTableSecond,on=JoinTable.id==JoinTableSecond.join_all_id,type="left")\
.select(AllFieldTypes.id,AllFieldTypes.short_text,JoinTable.short_text.alias("j_s_t"),JoinTableSecond.short_text.alias("j_ss_t")).all()
class ReturnJoinTableSecond(Model):
id: int|None
short_text: str|None
j_s_t: str|None
j_ss_t:str|None
# 后续可以使用pydantic提供的功能反序列化到pydantic模型中
adapter = TypeAdapter(list[ReturnJoinTableSecond])
e: list[ReturnJoinTableSecond] = adapter.validate_python(d)
7. 事务 & 健康检查
# Session 事务
async with session.transaction():
await session.save(alice)
await session.save(bob)
# 异常自动回滚,正常自动提交
# 健康检查
ok = await session.dialect.ping() # True → 连接正常
8. 原生 SQL
await session.execute_raw("DELETE FROM users WHERE status = %s", ["inactive"])
rows = await session.fetch_raw("SELECT id, name FROM users WHERE age > %s", [18])
row = await session.fetch_one_raw("SELECT * FROM users WHERE id = %s", [1])
查看日志
import logging
# 看所有 SQL 语句
logging.getLogger("vorm").setLevel(logging.DEBUG)
# 只看 DDL 和重连信息
logging.getLogger("vorm").setLevel(logging.INFO)
# 配置输出格式
logging.basicConfig(
level=logging.DEBUG,
format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
)
模型定义参考
Field 参数
| 参数 | 类型 | 默认值 | 说明 |
|---|---|---|---|
primary_key |
bool |
False |
主键 |
auto_increment |
bool |
False |
自增(仅 int 主键) |
column_name |
str | None |
None |
数据库列名,默认取 Python 属性名 |
nullable |
bool |
True |
是否允许 NULL |
unique |
bool |
False |
唯一约束 |
index |
bool |
False |
单列索引(自动转为 Index) |
max_length |
int | None |
None |
字符串长度 → VARCHAR(n);无 → TEXT |
db_type |
str | None |
None |
直接指定数据库类型 |
timestamp_behavior |
"create" | "update" | "both" | None |
None |
自动时间戳行为 |
default / default_factory |
— | — | Python 默认值(同时生成 DDL DEFAULT) |
timestamp_behavior
| 值 | INSERT | UPDATE |
|---|---|---|
"create" |
写入值 | 跳过,保留原值 |
"update" |
写入值 | 重新计算当前时间 |
"both" |
写入值 | 重新计算当前时间 |
Meta 配置
class Meta:
table = "table_name" # 表名(必填)
indexes = [ # 联合索引(可选)
Index(fields=("col1", "col2")), # 普通索引
Index(fields=("col1",), unique=True), # 唯一索引
Index(fields=("col1", "col2"), name="idx_custom"), # 自定义名称
]
索引名自动生成规则:ix_{table}_{fields}(普通),unq_{table}_{fields}(唯一)。
Python → SQL 类型映射
| Python 类型 | MySQL | PostgreSQL |
|---|---|---|
int |
INT |
INTEGER |
int + auto_increment PK |
INT AUTO_INCREMENT |
SERIAL |
str + max_length |
VARCHAR(n) |
VARCHAR(n) |
str 无 max_length |
TEXT |
TEXT |
bool |
BOOL |
BOOLEAN |
float |
DOUBLE |
DOUBLE PRECISION |
bytes |
BLOB |
BYTEA |
datetime.datetime |
DATETIME |
TIMESTAMP |
datetime.date |
DATE |
DATE |
decimal.Decimal |
DECIMAL(18,6) |
DECIMAL(18,6) |
dict |
JSON |
JSONB |
list |
JSON |
JSONB |
开发
uv sync # 安装依赖
uv run pytest # 运行测试(112 个)
uv run mypy vorm/ # 类型检查
uv run ruff check vorm/ # 代码风格
设计原则
- 参数化查询 — 所有 SQL 参数绑定,永不拼接用户输入
- 方言隔离 — MySQL / PG 差异封装在 Dialect 子类,业务层无感
- 安全 DDL — 只增不改不删(ADD COLUMN + ALTER COLUMN + CREATE INDEX,不 DROP)
- Pydantic 优先 — 模型定义、校验、序列化完全复用 Pydantic 生态
- 数据库原生优先 — JSON 默认值使用
JSON_OBJECT()/jsonb_build_object()等原生函数,不依赖代码序列化
License
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