Python学习路程day11

时间：2023-03-09 18:44:26
Python学习路程day11

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

Python学习路程day11

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:123@127.0.0.1:3306/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。

 #!/usr/bin/env python

 # -*- coding:utf-8 -*-

 from sqlalchemy import create_engine, Table, Column, Integer, String, MetaData, ForeignKey

 metadata = MetaData()

 user = Table('user', metadata,

     Column('id', Integer, primary_key=True),

     Column('name', String(20)),

 )

 color = Table('color', metadata,

     Column('id', Integer, primary_key=True),

     Column('name', String(20)),

 )

 engine = create_engine("mysql+mysqldb://root@localhost:3306/test", max_overflow=5)

 metadata.create_all(engine)

增删改查

 #!/usr/bin/env python

 # -*- coding:utf-8 -*-

 from sqlalchemy import create_engine, Table, Column, Integer, String, MetaData, ForeignKey

 metadata = MetaData()

 user = Table('user', metadata,

     Column('id', Integer, primary_key=True),

     Column('name', String(20)),

 )

 color = Table('color', metadata,

     Column('id', Integer, primary_key=True),

     Column('name', String(20)),

 )

 engine = create_engine("mysql+mysqldb://root:123@127.0.0.1:3306/s11", max_overflow=5)

 conn = engine.connect()

 # 创建SQL语句，INSERT INTO "user" (id, name) VALUES (:id, :name)

 conn.execute(user.insert(),{'id':7,'name':'seven'})

 conn.close()

 # sql = user.insert().values(id=123, name='wu')

 # conn.execute(sql)

 # conn.close()

 # sql = user.delete().where(user.c.id > 1)

 # sql = user.update().values(fullname=user.c.name)

 # sql = user.update().where(user.c.name == 'jack').values(name='ed')

 # sql = select([user, ])

 # sql = select([user.c.id, ])

 # sql = select([user.c.name, color.c.name]).where(user.c.id==color.c.id)

 # sql = select([user.c.name]).order_by(user.c.name)

 # sql = select([user]).group_by(user.c.name)

 # result = conn.execute(sql)

 # print result.fetchall()

 # conn.close()

一个简单的完整例子

 #!/usr/bin/env python

 #-*- coding:utf-8 -*-

 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.rollback()

     #session.commit()     #提交

     #update   #更新

     #res = session.query(Host).filter(Host.hostname.in_(['ubuntu2','localhost'])).all()

     #res = session.query(Host).filter(Host,hostname=='localhost').all()

     #res.hostname = "test server"

     #delete    删除

     #session.delete(obj)

     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。

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#!/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:123@127.0.0.1:3306/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()
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<br>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:

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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:

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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://*.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
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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

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

　　

group by 查询

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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

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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

更多具体内容：

http://files.cnblogs.com/files/wupeiqi/sqlalchemy.pdf.zip

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