SqlAlchemy ORM

SqlAlchemy ORM　　

SQLAlchemy是Python编程语言下的一款ORM框架，该框架建立在数据库API之上，使用关系对象映射进行数据库操作，简言之便是：将对象转换成SQL，然后使用数据API执行SQL并获取执行结果

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: / / :@[:] /

pymysql
    mysql + pymysql: / / :@ / [?]

MySQL - Connector
    mysql + mysqlconnector: / / :@[:] /

cx_Oracle
    oracle + cx_oracle: / / user: pass @host:port / dbname[?key = value&key = value...]

更多详见：http: / / docs.sqlalchemy.org / en / latest / dialects / index.html

　　

步骤一：

使用 Engine/ConnectionPooling/Dialect 进行数据库操作，Engine使用ConnectionPooling连接数据库，然后再通过Dialect执行SQL语句。

#!/usr/bin/env python
# -*- coding:utf-8 -*-

from sqlalchemy import create_engine


engine = create_engine( "mysql+mysqldb://root:[email protected]:3306/s11" , max_overflow = 5 )

engine.execute(
    "INSERT INTO ts_test (a, b) VALUES ('2', 'v1')"
)

engine.execute(
     "INSERT INTO ts_test (a, b) VALUES (%s, %s)" ,
    (( 555 , "v1" ),( 666 , "v1" ),)
)
engine.execute(
    "INSERT INTO ts_test (a, b) VALUES (%(id)s, %(name)s)" ,
    id = 999 , name = "v1"
)

result = engine.execute( 'select * from ts_test' )
result.fetchall()

　　

步骤二：

使用 Schema Type/SQL Expression Language/Engine/ConnectionPooling/Dialect 进行数据库操作。Engine使用Schema Type创建一个特定的结构对象，之后通过SQL Expression Language将该对象转换成SQL语句，然后通过 ConnectionPooling 连接数据库，再然后通过 Dialect 执行SQL，并获取结果。

+ View Code

增删改查

+ View Code

 

一个简单的完整例子

from sqlalchemy import create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, Integer, String
from  sqlalchemy.orm import sessionmaker

Base = declarative_base() #生成一个SqlORM 基类


engine = create_engine( "mysql+mysqldb://root@localhost:3306/test" ,echo = False )


class Host(Base):
    __tablename__ = 'hosts'
    id = Column(Integer,primary_key = True ,autoincrement = True )
    hostname = Column(String( 64 ),unique = True ,nullable = False )
    ip_addr = Column(String( 128 ),unique = True ,nullable = False )
    port = Column(Integer,default = 22 )

Base.metadata.create_all(engine) #创建所有表结构

if __name__ = = '__main__' :
    SessionCls = sessionmaker(bind = engine) #创建与数据库的会话session class ,注意,这里返回给session的是个class,不是实例
    session = SessionCls()
    #h1 = Host(hostname='localhost',ip_addr='127.0.0.1')
    #h2 = Host(hostname='ubuntu',ip_addr='192.168.2.243',port=20000)
    #h3 = Host(hostname='ubuntu2',ip_addr='192.168.2.244',port=20000)
    #session.add(h3)
    #session.add_all( [h1,h2])
    #h2.hostname = 'ubuntu_test' #只要没提交,此时修改也没问题
    #session.rollback()
    #session.commit() #提交
    res = session.query(Host). filter (Host.hostname.in_([ 'ubuntu2' , 'localhost' ])). all ()
    print (res)

　　

 

更多内容详见：

    http://www.jianshu.com/p/e6bba189fcbd

    http://docs.sqlalchemy.org/en/latest/core/expression_api.html

注：SQLAlchemy无法修改表结构，如果需要可以使用SQLAlchemy开发者开源的另外一个软件Alembic来完成。

步骤三：

使用 ORM/Schema Type/SQL Expression Language/Engine/ConnectionPooling/Dialect 所有组件对数据进行操作。根据类创建对象，对象转换成SQL，执行SQL。

#!/usr/bin/env python
# -*- coding:utf-8 -*-

from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, Integer, String
from sqlalchemy.orm import sessionmaker
from sqlalchemy import create_engine

engine = create_engine( "mysql+mysqldb://root:[email protected]:3306/s11" , max_overflow = 5 )

Base = declarative_base()


class User(Base):
    __tablename__ = 'users'
    id = Column(Integer, primary_key = True )
    name = Column(String( 50 ))

# 寻找Base的所有子类，按照子类的结构在数据库中生成对应的数据表信息
# Base.metadata.create_all(engine)

Session = sessionmaker(bind = engine)
session = Session()


# ########## 增 ##########
# u = User(id=2, name='sb')
# session.add(u)
# session.add_all([
#     User(id=3, name='sb'),
#     User(id=4, name='sb')
# ])
# session.commit()

# ########## 删除 ##########
# session.query(User).filter(User.id > 2).delete()
# session.commit()

# ########## 修改 ##########
# session.query(User).filter(User.id > 2).update({'cluster_id' : 0})
# session.commit()
# ########## 查 ##########
# ret = session.query(User).filter_by(name='sb').first()

# ret = session.query(User).filter_by(name='sb').all()
# print ret

# ret = session.query(User).filter(User.name.in_(['sb','bb'])).all()
# print ret

# ret = session.query(User.name.label('name_label')).all()
# print ret,type(ret)

# ret = session.query(User).order_by(User.id).all()
# print ret

# ret = session.query(User).order_by(User.id)[1:3]
# print ret
# session.commit()

外键关联

A one to many relationship places a foreign key on the child table referencing the parent.relationship() is then specified on the parent, as referencing a collection of items represented by the child

from sqlalchemy import Table, Column, Integer, ForeignKey from sqlalchemy.orm import relationship from sqlalchemy.ext.declarative import declarative_base Base = declarative_base()

class Parent(Base):
    __tablename__ = 'parent'
    id = Column(Integer, primary_key = True )
    children = relationship( "Child" )

class Child(Base):
    __tablename__ = 'child'
    id = Column(Integer, primary_key = True )
    parent_id = Column(Integer, ForeignKey( 'parent.id' ))

To establish a bidirectional relationship in one-to-many, where the “reverse” side is a many to one, specify an additional relationship() and connect the two using therelationship.back_populates parameter:

class Parent(Base):
    __tablename__ = 'parent'
    id = Column(Integer, primary_key = True )
    children = relationship( "Child" , back_populates = "parent" )

class Child(Base):
    __tablename__ = 'child'
    id = Column(Integer, primary_key = True )
    parent_id = Column(Integer, ForeignKey( 'parent.id' ))
    parent = relationship( "Parent" , back_populates = "children" )

Child will get a parent attribute with many-to-one semantics.

Alternatively, the backref option may be used on a single relationship() instead of usingback_populates:

class Parent(Base):
    __tablename__ = 'parent'
    id = Column(Integer, primary_key = True )
    children = relationship( "Child" , backref = "parent" )

　　

　　

附，原生sql join查询

几个Join的区别 http://stackoverflow.com/questions/38549/difference-between-inner-and-outer-joins 

• INNER JOIN: Returns all rows when there is at least one match in BOTH tables
• LEFT JOIN: Return all rows from the left table, and the matched rows from the right table
• RIGHT JOIN: Return all rows from the right table, and the matched rows from the left table

select host.id,hostname,ip_addr,port,host_group. name from host right join host_group on host.id = host_group.host_id

in SQLAchemy

session.query(Host). join (Host.host_groups).filter(HostGroup. name == 't1' ).group_by( "Host" ). all ()

　　

group by 查询

select name , count (host.id) as NumberOfHosts from host right join host_group on host.id= host_group.host_id group by name ;

in SQLAchemy

from sqlalchemy import func
session.query(HostGroup, func. count (HostGroup. name )).group_by(HostGroup. name ). all ()

#another example
session.query(func. count ( User . name ), User . name ).group_by( User . name ). all () SELECT count (users. name ) AS count_1, users. name AS users_name
FROM users GROUP BY users. name

 

转载于:https://www.cnblogs.com/xiajie/p/5343302.html