SQLAlchemy是一个基于Python实现的ORM框架。该框架建立在DBAPI之上，使用对象关系映射进行数据库操作，简言之便：将类和对象转换成SQL，然后使用数据API执行SQL并获取执行结果。
pip3 install sqlalchemy组成部分:
Engine,框架的引擎
Connection Pooling,数据库连接池
Dialect,选择连接数据库的DB API种类
Schema/Types，架构和类型
SQL Expression Language，SQL表达式语言SQLAlchemy本身无法操作数据库，其必须依赖pymql等第三方插件,Dialect用于和数据API进行交流，根据配置文件的不同调用不同的数据库API，从而实现对数据库的操作，如
MySQL-Python
mysql+mysqldb://<user>:<password>@<host>[:<port>]/<dbname>
pymysql
mysql+pymysql://<username>:<password>@<host>/<dbname>[?<options>]
MySQL-Connector
mysql+mysqlconnector://<user>:<password>@<host>[:<port>]/<dbname>
cx_Oracle
oracle+cx_oracle://user:pass@host:port/dbname[?key=value&key=value...]
更多：http://docs.sqlalchemy.org/en/latest/dialects/index.html修改表:在数据库添加字段，类对应上
1.执行原生sql(不常用)
import time
import threading
import sqlalchemy
from sqlalchemy import create_engine
from sqlalchemy.engine.base import Engine
engine = create_engine(
"mysql+pymysql://root:123456@127.0.0.1:3306/test?charset=utf8",
max_overflow=0, # 超过连接池大小外最多创建的连接
pool_size=5, # 连接池大小
pool_timeout=30, # 池中没有线程最多等待的时间，否则报错
pool_recycle=-1 # 多久之后对线程池中的线程进行一次连接的回收（重置）
)
def task(arg):
conn = engine.raw_connection()
cursor = conn.cursor()
cursor.execute(
"select * from app01_book"
)
result = cursor.fetchall()
print(result)
cursor.close()
conn.close()
for i in range(20):
t = threading.Thread(target=task, args=(i,))
t.start()
2.ORM
# models.py
import datetime
from sqlalchemy import create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, Integer, String, Text, ForeignKey, DateTime, UniqueConstraint, Index
Base = declarative_base()
class Users(Base):
__tablename__ = 'users' # 数据库表名称
id = Column(Integer, primary_key=True) # id 主键
name = Column(String(32), index=True, nullable=False) # name列，索引，不可为空
# email = Column(String(32), unique=True)
# datetime.datetime.now不能加括号，加了括号，以后永远是当前时间
# ctime = Column(DateTime, default=datetime.datetime.now)
# extra = Column(Text, nullable=True)
__table_args__ = (
# UniqueConstraint('id', 'name', name='uix_id_name'), #联合唯一
# Index('ix_id_name', 'name', 'email'), #索引
)
def init_db():
"""
根据类创建数据库表
:return:
"""
engine = create_engine(
"mysql+pymysql://root:123456@127.0.0.1:3306/test?charset=utf8",
max_overflow=0, # 超过连接池大小外最多创建的连接
pool_size=5, # 连接池大小
pool_timeout=30, # 池中没有线程最多等待的时间，否则报错
pool_recycle=-1 # 多久之后对线程池中的线程进行一次连接的回收（重置）
)
Base.metadata.create_all(engine)
def drop_db():
"""
根据类删除数据库表
:return:
"""
engine = create_engine(
"mysql+pymysql://root:123456@127.0.0.1:3306/test?charset=utf8",
max_overflow=0, # 超过连接池大小外最多创建的连接
pool_size=5, # 连接池大小
pool_timeout=30, # 池中没有线程最多等待的时间，否则报错
pool_recycle=-1 # 多久之后对线程池中的线程进行一次连接的回收（重置）
)
Base.metadata.drop_all(engine)
if __name__ == '__main__':
# drop_db()
init_db()
# app.py
# 插入一条
obj1 = Users(name="lqz")
session.add(obj1)
# 插入多条
session.add_all([Users(name='egon'), Users(name='xxx')])
# 删除数据(filter内写的是条件)
session.query(Users).filter(Users.id >= 2, Users.name == 'egon').delete()
# 修改
session.query(Users).filter(Users.id == 1).update({Users.name: Users.name + "099"}, synchronize_session=False)
session.query(Users).filter(Users.id == 1).update({'name': Users.name + "099"}, synchronize_session=False)
session.query(Users).filter(Users.id > 0).update({"age": Users.age + 1}, synchronize_session="evaluate")
# 查询操作
r1 = session.query(Users).all()
print(r1)
for u in r1:
print(u.name)
r2 = session.query(Users.name.label('xxx')).all()
print(r2)
# filter传的是表达式，filter_by传的是参数
re = session.query(Users).filter(Users.name == 'egon').all()
print(re)
re = session.query(Users).filter_by(name='egon').all()
print(re)
# 其他操作
ret = session.query(Users).filter(Users.id.between(1, 3), Users.name == 'eric')
ret = session.query(Users).filter(Users.id.in_([1, 3, 4])).all()
ret = session.query(Users).filter(Users.id.in_(session.query(Users.id).filter_by(name='lqz'))).all()
print(ret)
from sqlalchemy import and_, or_
ret = session.query(Users).filter(and_(Users.id > 3, Users.name == 'eric')).all()
ret = session.query(Users).filter(or_(Users.id < 2, Users.name == 'lqz')).all()
ret = session.query(Users).filter(Users.name.like('q%')).all()
ret = session.query(Users).filter(~Users.name.like('l%')).all()
# ret = session.query(Users)[1:2]
# 排序，根据name降序排列（从大到小）
ret = session.query(Users).order_by(Users.name.desc()).all()
# 第一个条件重复后，再按第二个条件升序排
ret = session.query(Users).order_by(Users.name.desc(), Users.id.asc()).all()
ret = session.query(Users).order_by(Users.name.asc()).all()
from sqlalchemy.sql import func
ret = session.query(
func.max(Users.id),
func.sum(Users.id),
func.min(Users.id)).filter(Users.id > 0).group_by(Users.name).having(func.min(Users.id) > 2).all()
print(ret)# modeles.py导包
import datetime
from sqlalchemy import create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, Integer, String, Text, ForeignKey, DateTime, UniqueConstraint, Index
from sqlalchemy.orm import relationship
Base = declarative_base()# models.py
class Hobby(Base):
__tablename__ = 'hobby'
id = Column(Integer, primary_key=True)
caption = Column(String(50), default='篮球')
def __repr__(self):
return self.caption
class Person(Base):
__tablename__ = 'person'
nid = Column(Integer, primary_key=True)
name = Column(String(32), index=True, nullable=True)
# hobby指的是tablename而不是类名
hobby_id = Column(Integer, ForeignKey("hobby.id"))
# 跟数据库无关，不会新增字段，只用于快速链表操作
# 类名，backref用于反向查询
hobby = relationship('Hobby', backref='pers')
def __repr__(self):
return self.name# app.py 导包
from sqlalchemy.orm import sessionmaker
from sqlalchemy import create_engine
from models import Girl, Boy, Boy2Girl, Users, Hobby, Person
# "mysql+pymysql://root@127.0.0.1:3306/aaa"
engine = create_engine("mysql+pymysql://root:123321@127.0.0.1:3306/aaa", max_overflow=0, pool_size=5)
# Connection = sessionmaker(bind=engine)
Session = sessionmaker(bind=engine)
# 每次执行数据库操作时，都需要创建一个Connection
# con= Connection()
session = Session()session.add_all([
Hobby(caption='乒乓球'),
Hobby(caption='羽毛球'),
Person(name='张三', hobby_id=1),
Person(name='李四', hobby_id=1),
])
person = Person(name='张九', hobby=Hobby(caption='姑娘'))
session.add(person)
hb = Hobby(caption='人妖')
hb.pers = [Person(name='文飞'), Person(name='博雅')]
session.add(hb)
ls = session.query(Person).filter_by(name='李四').first()
print(ls.hobby_id)
print(ls.hobby.caption)
print(ls)
ho = session.query(Hobby).filter_by(caption='妹子').first()
print(ho.pers[0].name)
# 连表查询
# 自己关联表 跟django中的双下划线查询一样
person_list = session.query(Person, Hobby).filter(Hobby.caption == '妹子').join(Hobby, isouter=True).all()
print(person_list)
person_list = session.query(Person).all()
for row in person_list:
print(row.name, row.hobby.caption)
obj = session.query(Hobby).filter(Hobby.id == 1).first()
persons = obj.pers
print(persons)class Hobby(Base):
__tablename__ = 'hobby'
id = Column(Integer, primary_key=True)
caption = Column(String(50), default='篮球')
def __repr__(self):
return self.caption
class Person(Base):
__tablename__ = 'person'
nid = Column(Integer, primary_key=True)
name = Column(String(32), index=True, nullable=True)
# hobby指的是tablename而不是类名
hobby_id = Column(Integer, ForeignKey("hobby.id"))
# 跟数据库无关，不会新增字段，只用于快速链表操作
# 类名，backref用于反向查询
hobby = relationship('Hobby', backref='pers')
def __repr__(self):
return self.namesession.add_all([
Boy(name='lqz'),
Boy(name='egon'),
Girl(name='lyf'),
Girl(name='dlrb'),
])
session.commit()
s2g = Boy2Girl(boy_id=1, girl_id=1)
session.add(s2g)
session.commit()
# 通过relationship
session.add_all([
Boy(name='boy_xxx', girls=[Girl(name='girl_xx')]),
])
girl = Girl(name='girl_yyy', boys=[Boy(name='boy_yyy')])
session.add(girl)
session.commit()
# 多对多查询
girl = session.query(Girl).filter(Girl.name == 'girl_yyy').first()
print(girl.boys)
boy = session.query(Boy).filter(Boy.name == 'boy_yyy').first()
print(boy.girls)
# 提交事务
session.commit()
# 关闭session，其实是将连接放回连接池
session.close()flask和SQLAchemy的管理者，通过他把他们做连接
db = SQLAlchemy() - 包含配置 - 包含ORM基类 - 包含create_all - engine - 创建连接sansa为flak项目的文件夹
原文：https://www.cnblogs.com/ShenJunHui6/p/11234201.html