SQLALCHEMY用法详解

 

一,SQLAlchemy的安装 
使用

$ easy_install sqlalchemy
或
$ pip install sqlalchemy

如果出现什么错,就进去root用户下进行安装试试,或者网上查查

>>> import sqlalchemy
>>> 
  • 这样说明成功了,切记是小写哦 
    二,使用 
    理论我也不懂,自己查查资料,现在用一个小的案例说一下使用步骤 
    1,在进行数据操作之前要先连上数据库。
>>> from sqlalchemy import create_engine
>>> from sqlalchemy.orm import sessionmaker  
>>> DB_CONNECT = 'mysql+mysqldb://root:102@localhost/mydb'
>>> engine = create_engine(DB_CONNECT, echo=True)
>>> DB_Session = sessionmaker(bind=engine)
>>> session = DB_Session()

from 是从sqlalchemy中插入必须的模板,DB_CONNECT 是构造数据库的路径 ,mysql+mysqldb是说明使用MySQL-Python 来连接,root是数据库用户名,102是密码,localhost表示是数据库在本机上,mydb是要连接的数据库名字,设置字符集的charset可以省了 
create_engine() 会返回一个数据库引擎,echo 参数为 True 时,会显示每条执行的 SQL 语句,生产环境下可关闭。 
sessionmaker(bind=engine)会生成一个数据库会话类。这个类的实例可以当成一个数据库连接,它同时还记录了一些查询的数据,并决定什么时候执行 SQL 语句。由于 SQLAlchemy 自己维护了一个数据库连接池(默认 5 个连接),也可以自己设置。 
得到session 后,就可以执行 SQL 了: 
2,在进行操作前先把表给建立了,由于SQLAlchemy 可以和变进行建立连接并且可以通过语言进行见表

mysql> show tables;
Empty set (0.00 sec)
mysql> 

此时是没有表的,现在我们建立一个学生便stu,一个课程表cla和一个成绩表grade

>>> from sqlalchemy import Column
>>> from sqlalchemy.types import CHAR, Integer, String
>>> from sqlalchemy.ext.declarative import declarative_base
>>> from random import randint
>>> from sqlalchemy import ForeignKey
>>> BaseModel = declarative_base()
>>> def init_db():
...     BaseModel.metadata.create_all(engine)
... 
>>> def drop_db():
...     BaseModel.metadata.drop_all()
... 
>>> class Stu(BaseModel):
...     __tablename__='stu'
...     id = Column(Integer,primary_key = True)
...     name = Column(CHAR(30))
... 
>>> class Cla(BaseModel):
...     __tablename__='cla'
...     id = Column(Integer,primary_key = True)设置主键
...     cname = Column(CHAR(30))
... 
>>> class Grade(BaseModel):
...     __tablename__ = 'grade'
...     uid = Column(Integer,ForeignKey('stu.id'))设置外键
...     cid = Column(Integer,ForeignKey('cla.id'))
...     id = Column(Integer,primary_key=True)
...     gre=Column(Integer)
... 

declarative_base() 创建了一个 BaseModel 类,这个类的子类可以自动与一个表关联。以 Stu 类为例,它的 tablename 属性就是数据库中该表的名称,它有 id 和 name 这两个字段,分别为整型和 30 个定长字符。Column 还有一些其他的参数,我就不解释了。 
最后,BaseModel.metadata.create_all(engine) 会找到 BaseModel 的所有子类,并在数据库中建立这些表;drop_all() 则是删除这些表。 
现在执行init_db()进行建立表,对应语句如下

>>> init_db()
CREATE TABLE stu (
    id INTEGER NOT NULL AUTO_INCREMENT, 
    name CHAR(30), 
    PRIMARY KEY (id)
)

CREATE TABLE cla (
    id INTEGER NOT NULL AUTO_INCREMENT, 
    cname CHAR(30), 
    PRIMARY KEY (id)
)
CREATE TABLE grade (
    id INTEGER NOT NULL AUTO_INCREMENT, 
    uid INTEGER, 
    cid INTEGER, 
    gre INTEGER, 
    PRIMARY KEY (id), 
    FOREIGN KEY(uid) REFERENCES stu (id), 
    FOREIGN KEY(cid) REFERENCES cla (id)
)
COMMIT
>>> 

以上就是执行时对应的建表语句,现在去数据库看看表是否存在,并查看一个表结构

mysql> show tables;
+----------------+
| Tables_in_mydb |
+----------------+
| cla            |
| grade          |
| stu            |
+----------------+
3 rows in set (0.00 sec)

表已经建立成功了,现在看一下表结构

mysql> desc grade;
+-------+---------+------+-----+---------+----------------+
| Field | Type    | Null | Key | Default | Extra          |
+-------+---------+------+-----+---------+----------------+
| id    | int(11) | NO   | PRI | NULL    | auto_increment |
| uid   | int(11) | YES  | MUL | NULL    |                |
| cid   | int(11) | YES  | MUL | NULL    |                |
| gre   | int(11) | YES  |     | NULL    |                |
+-------+---------+------+-----+---------+----------------+
4 rows in set (0.00 sec)

可以看出 使用SQLAlchemy中的语句和使用SQL语句的结果一样。接下来就可以插入数据了

>>> stu = Stu(name='a')
>>> session.add(stu)
>>> stu = Stu(name='b')
>>> session.add(stu)
>>> stu = Stu(name='c')
>>> session.add(stu)
>>> stu = Stu(name='d')
>>> session.add(stu)
>>> stu = Stu(name='e')
>>> session.add(stu)
>>> 

手动插入了五条记录,但此时还没有提交,没有真正的写入数据库 
或者使用非ORM方式进行插入

>>>session.execute(Stu.__table__.insert(),[{'name':randint(1,100)} for i in xrange(10000)])
>>>session.commit()
#可以速度更快的插入更多的数据
  •  
>>> session.commit()
2016-05-09 18:22:16,839 INFO sqlalchemy.engine.base.Engine BEGIN (implicit)
2016-05-09 18:22:16,840 INFO sqlalchemy.engine.base.Engine INSERT INTO stu (name) VALUES (%s)
2016-05-09 18:22:16,840 INFO sqlalchemy.engine.base.Engine ('a',)
2016-05-09 18:22:16,841 INFO sqlalchemy.engine.base.Engine INSERT INTO stu (name) VALUES (%s)
2016-05-09 18:22:16,841 INFO sqlalchemy.engine.base.Engine ('b',)
2016-05-09 18:22:16,841 INFO sqlalchemy.engine.base.Engine INSERT INTO stu (name) VALUES (%s)
2016-05-09 18:22:16,841 INFO sqlalchemy.engine.base.Engine ('c',)
2016-05-09 18:22:16,842 INFO sqlalchemy.engine.base.Engine INSERT INTO stu (name) VALUES (%s)
2016-05-09 18:22:16,842 INFO sqlalchemy.engine.base.Engine ('d',)
2016-05-09 18:22:16,842 INFO sqlalchemy.engine.base.Engine INSERT INTO stu (name) VALUES (%s)
2016-05-09 18:22:16,842 INFO sqlalchemy.engine.base.Engine ('e',)
2016-05-09 18:22:16,843 INFO sqlalchemy.engine.base.Engine COMMIT
>>> 

此时真的写入数据库了哦。向课程表插入五条

>>> cla = Cla(cname='yuwen')
>>> session.add(cla)
>>> cla = Cla(cname='shuxue')
>>> session.add(cla)
>>> cla = Cla(cname='yingyu')
>>> session.add(cla)
>>> cla = Cla(cname='wuli')
>>> session.add(cla)
>>> cla = Cla(cname='huaxue')
>>> session.add(cla)
>>> session.commit()

3,现在开始操作数据

>>> query = session.query(Stu)
>>> for st in query:
...     print st.name
... 
对应的SQL语句
SELECT stu.id AS stu_id, stu.name AS stu_name 
FROM stu
2016-05-09 18:56:07,084 INFO sqlalchemy.engine.base.Engine ()
a
b
c
d
e
>>> print query.all()# # 返回的是一个类似列表的对象
SELECT stu.id AS stu_id, stu.name AS stu_name 
FROM stu
2016-05-09 18:58:16,085 INFO sqlalchemy.engine.base.Engine ()
[<__main__.Stu object at 0xb66b3f4c>, <__main__.Stu object at 0xb5e4202c>, <__main__.Stu object at 0xb66b3f8c>, <__main__.Stu object at 0xb5e4206c>, <__main__.Stu object at 0xb6688c0c>]

>>> print query.first().name# 有数据时返回第一条记录,没有数据时会返回 None
SELECT stu.id AS stu_id, stu.name AS stu_name 
FROM stu 
 LIMIT %s
2016-05-09 18:59:43,149 INFO sqlalchemy.engine.base.Engine (1,)
a
# print query.one().name# 不存在,或有多行记录时会抛出异常

>>> print query.filter(Stu.id == 2).first().name
SELECT stu.id AS stu_id, stu.name AS stu_name 
FROM stu 
WHERE stu.id = %s 
 LIMIT %s
2016-05-09 19:04:54,363 INFO sqlalchemy.engine.base.Engine (2, 1)
b
>>> print query.filter('id = 2').first().name # 支持字符串
SELECT stu.id AS stu_id, stu.name AS stu_name 
FROM stu 
WHERE id = 2 
 LIMIT %s
2016-05-09 19:07:02,016 INFO sqlalchemy.engine.base.Engine (1,)
b
>>> print query.get(2).name # 以主键获取,等效于上句
2016-05-09 19:07:40,007 INFO sqlalchemy.engine.base.Engine SELECT stu.id AS stu_id, stu.name AS stu_name 
FROM stu 
WHERE stu.id = %s
2016-05-09 19:07:40,007 INFO sqlalchemy.engine.base.Engine (2,)
b
>>> print query.get(2).id
SELECT stu.id AS stu_id, stu.name AS stu_name 
FROM stu 
WHERE stu.id = %s
2016-05-09 19:08:46,009 INFO sqlalchemy.engine.base.Engine (2,)
2
  •  
>>> print quer2.limit(1).all() #只返回一条
2016-05-09 19:11:23,383 INFO sqlalchemy.engine.base.Engine SELECT stu.name AS stu_name 
FROM stu 
 LIMIT %s
2016-05-09 19:11:23,383 INFO sqlalchemy.engine.base.Engine (1,)
[('a',)]

>>> print quer2.limit(2).all()#只返回两条
SELECT stu.name AS stu_name 
FROM stu 
 LIMIT %s
2016-05-09 19:11:29,480 INFO sqlalchemy.engine.base.Engine (2,)
[('a',), ('b',)]
>>> print quer2.offset(1).all() #跳过一条,从第二条数据开始查询
SELECT stu.name AS stu_name 
FROM stu 
 LIMIT %s, 18446744073709551615
2016-05-09 19:13:25,734 INFO sqlalchemy.engine.base.Engine (1,)
[('b',), ('c',), ('d',), ('e',)]
>>> print quer2.offset(3).all() #从第四条数据开始
SELECT stu.name AS stu_name 
FROM stu 
 LIMIT %s, 18446744073709551615
2016-05-09 19:13:39,629 INFO sqlalchemy.engine.base.Engine (3,)
[('d',), ('e',)]
#按name降序排序
>>> print quer2.order_by(Stu.name.desc()).all()
SELECT stu.name AS stu_name 
FROM stu ORDER BY stu.name DESC
2016-05-09 19:16:56,022 INFO sqlalchemy.engine.base.Engine ()
[('e',), ('d',), ('c',), ('b',), ('a',)]

>>> print quer2.order_by('name desc').all()
SELECT stu.name AS stu_name 
FROM stu ORDER BY name desc
2016-05-09 19:17:09,851 INFO sqlalchemy.engine.base.Engine ()
[('e',), ('d',), ('c',), ('b',), ('a',)]
#按name降序,有重复的按id升序排序
>>> print session.query(Stu.id).order_by('name desc','id').all()
SELECT stu.id AS stu_id 
FROM stu ORDER BY name desc, stu.id
2016-05-09 19:20:34,818 INFO sqlalchemy.engine.base.Engine ()
[(5L,), (4L,), (3L,), (2L,), (1L,)]
#scalar()在有多条数据时使用报出异常,all()可以使用多条也可以使用一条
#>>> print quer2.filter(Stu.id>2).scalar()
>>> print quer2.filter(Stu.id>2).all()
SELECT stu.name AS stu_name 
FROM stu 
WHERE stu.id > %s
2016-05-09 19:56:47,760 INFO sqlalchemy.engine.base.Engine (2,)
[('c',), ('d',), ('e',)]

>>> print quer2.filter(Stu.id==2).all()
SELECT stu.name AS stu_name 
FROM stu 
WHERE stu.id = %s
2016-05-09 19:57:47,901 INFO sqlalchemy.engine.base.Engine (2,)
[('b',)]


>>> print quer2.filter(Stu.id==2).scalar()
SELECT stu.name AS stu_name 
FROM stu 
WHERE stu.id = %s
2016-05-09 19:23:38,761 INFO sqlalchemy.engine.base.Engine (2,)
b

>>> print quer2.filter('id=2').scalar()
SELECT stu.name AS stu_name 
FROM stu 
WHERE id=2
2016-05-09 19:43:47,797 INFO sqlalchemy.engine.base.Engine ()
b

#在此中‘,’等价于and
>>> print query2.filter(Stu.id>1,Stu.name !='a').first()
SELECT stu.name AS stu_name 
FROM stu 
WHERE stu.id > %s AND stu.name != %s 
 LIMIT %s
2016-05-09 19:51:14,571 INFO sqlalchemy.engine.base.Engine (1, 'a', 1)
('b',)
>>> 
#此种迭代也类似与and
>>> query3 = query2.filter(Stu.id>1)
>>> query3 = query3.filter(Stu.name != 'a')
>>> query3.first()
2016-05-09 19:53:50,150 INFO sqlalchemy.engine.base.Engine SELECT stu.name AS stu_name 
FROM stu 
WHERE stu.id > %s AND stu.name != %s 
 LIMIT %s
2016-05-09 19:53:50,151 INFO sqlalchemy.engine.base.Engine (1, 'a', 1)
('b',)
  •  
#or_就是类似or
>>> print query2.filter(or_(Stu.id == 1,Stu.id==2)).all()
2016-05-09 19:55:59,383 INFO sqlalchemy.engine.base.Engine SELECT stu.name AS stu_name 
FROM stu 
WHERE stu.id = %s OR stu.id = %s
2016-05-09 19:55:59,383 INFO sqlalchemy.engine.base.Engine (1, 2)
[('a',), ('b',)]
  •  
# in的用法
>>> print query2.filter(Stu.id.in_((1,2,3))).all()
SELECT stu.name AS stu_name 
FROM stu 
WHERE stu.id IN (%s, %s, %s)
2016-05-09 20:01:01,729 INFO sqlalchemy.engine.base.Engine (1, 2, 3)
[('a',), ('b',), ('c',)]
>>> 
  •  
#修改数据
>>> query.filter(Stu.id==1).update({Stu.name:'li'})
UPDATE stu SET name=%s WHERE stu.id = %s
2016-05-09 20:12:57,027 INFO sqlalchemy.engine.base.Engine ('li', 1)
1L

#删除数据
>>> query = session.query(Grade)
>>> query.filter(Grade.id == 1).delete()
DELETE FROM grade WHERE grade.id = %s
2016-05-09 20:28:18,638 INFO sqlalchemy.engine.base.Engine (1,)
1L
>>> 
此时没有提交,在数据库中环视存在的
mysql> select * from grade;
+----+------+------+------+
| id | uid  | cid  | gre  |
+----+------+------+------+
|  1 |    1 |    1 |   60 |
|  2 |    2 |    1 |   66 |
|  3 |    5 |    1 |   66 |
|  4 |    5 |    5 |   96 |
|  5 |    5 |    3 |   96 |
|  6 |    3 |    2 |   96 |
|  7 |    3 |    4 |   76 |
|  8 |    4 |    4 |   76 |
|  9 |    4 |    3 |   76 |
| 10 |    4 |    5 |   76 |
| 11 |    1 |    4 |   76 |
| 12 |    1 |    5 |   76 |
| 13 |    2 |    5 |   76 |
| 14 |    3 |    3 |   60 |
| 15 |    2 |    3 |   50 |
+----+------+------+------+
15 rows in set (0.00 sec)
#开始提交
>>> session.commit()
2016-05-09 20:31:02,461 INFO sqlalchemy.engine.base.Engine COMMIT
>>> 
mysql> select * from grade;
+----+------+------+------+
| id | uid  | cid  | gre  |
+----+------+------+------+
|  2 |    2 |    1 |   66 |
|  3 |    5 |    1 |   66 |
|  4 |    5 |    5 |   96 |
|  5 |    5 |    3 |   96 |
|  6 |    3 |    2 |   96 |
|  7 |    3 |    4 |   76 |
|  8 |    4 |    4 |   76 |
|  9 |    4 |    3 |   76 |
| 10 |    4 |    5 |   76 |
| 11 |    1 |    4 |   76 |
| 12 |    1 |    5 |   76 |
| 13 |    2 |    5 |   76 |
| 14 |    3 |    3 |   60 |
| 15 |    2 |    3 |   50 |
+----+------+------+------+
14 rows in set (0.00 sec)

也获取不到对象了
>>> print query.get(1)
SELECT grade.id AS grade_id, grade.uid AS grade_uid, grade.cid AS grade_cid, grade.gre AS grade_gre 
FROM grade 
WHERE grade.id = %s
2016-05-09 20:32:20,742 INFO sqlalchemy.engine.base.Engine (1,)
None
>>> 
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SQLAlchemy是一个Python编程语言下的SQL工具包和对象-关系映射器(ORM)。它提供了一种与数据库进行交互的高级抽象,使得开发人员可以使用Python语言来执行数据库操作,而不需要直接编写SQL语句。 下面是SQLAlchemy的使用详解: 1. 安装SQLAlchemy:可以使用pip命令来安装SQLAlchemy,如下所示: ``` pip install sqlalchemy ``` 2. 导入SQLAlchemy模块:在Python脚本中,首先需要导入SQLAlchemy模块,如下所示: ```python from sqlalchemy import create_engine, Column, Integer, String, ForeignKey from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import sessionmaker, relationship ``` 3. 创建连接引擎:使用`create_engine()`函数创建一个数据库连接引擎,该引擎将负责与数据库进行通信。引擎的参数通常包括数据库的URL、用户名、密码等信息,如下所示: ```python engine = create_engine('数据库URL') ``` 4. 创建映射类:使用`declarative_base()`函数创建一个基类,该基类将作为所有映射类的父类。然后,使用`Column()`函数定义表的列,如下所示: ```python Base = declarative_base() class User(Base): __tablename__ = 'users' id = Column(Integer, primary_key=True) name = Column(String) email = Column(String) ``` 5. 创建表:使用`Base.metadata.create_all()`方法创建数据库中的表,如下所示: ```python Base.metadata.create_all(engine) ``` 6. 创建会话:使用`sessionmaker()`函数创建一个会话工厂,然后使用工厂创建会话对象,如下所示: ```python Session = sessionmaker(bind=engine) session = Session() ``` 7. 执行数据库操作:通过会话对象,可以执行各种数据库操作,例如插入、查询、更新和删除数据,如下所示: ```python # 插入数据 user = User(name='John', email='[email protected]') session.add(user) session.commit() # 查询数据 users = session.query(User).all() for user in users: print(user.name, user.email) # 更新数据 user = session.query(User).filter_by(name='John').first() user.email = '[email protected]' session.commit() # 删除数据 user = session.query(User).filter_by(name='John').first() session.delete(user) session.commit() ``` 这是SQLAlchemy的基本使用方法。通过这些步骤,你可以使用Python来执行各种数据库操作,并且无需直接编写SQL语句。你还可以进一步学习SQLAlchemy的高级特性,如事务处理、关联关系等。

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