Python学习总结：Python之操作RabbitMQ、SQLAlchemy

RabbitMQ

　　RabbitMQ是一个在AMQP基础上完整的，可复用的企业消息系统。他遵循Mozilla Public License开源协议。
　　MQ全称为Message Queue, 消息队列（MQ）是一种应用程序对应用程序的通信方法。应用程序通过读写出入队列的消息（针对应用程序的数据）来通信，而无需专用连接来链接它们。消 息传递指的是程序之间通过在消息中发送数据进行通信，而不是通过直接调用彼此来通信，直接调用通常是用于诸如远程过程调用的技术。排队指的是应用程序通过 队列来通信。队列的使用除去了接收和发送应用程序同时执行的要求。

RabbitMQ部署

# RabbitMQ服务端
rpm -ivh http://dl.fedoraproject.org/pub/epel/6/i386/epel-release-6-8.noarch.rpm  # 安装配置epel源
yum -y install erlang                  　　# 安装erlang
yum -y install rabbitmq-server        # 安装RabbitMQ

# RabbitMQ的API
pip install pika
or
easy_install pika
or
源码

https://pypi.python.org/pypi/pika

使用API操作RabbitMQ

#!/usr/bin/env python
# -*- coding:utf-8 -*-
import queue
import threading
import time


message = queue.Queue(10)


def producer(i):
    while True:
        message.put(i)


def consumer(i):
    while True:
        msg = message.get()
        print(msg)
        time.sleep(2)

for i in range(12):
    t = threading.Thread(target=producer, args=(i,))
    t.start()

for i in range(10):
    t = threading.Thread(target=consumer, args=(i,))
    t.start()
基于Queue实现生产者消费者模型

　　对于RabbitMQ来说，生产和消费不再针对内存里的一个Queue对象，而是某台服务器上的RabbitMQ Server实现的消息队列。

#!/usr/bin/env python
import pika

# ######################### 生产者 #########################

connection = pika.BlockingConnection(pika.ConnectionParameters(host='192.168.21.104', port=4369))
channel = connection.channel()
channel.queue_declare(queue='hello')
channel.basic_publish(exchange='', routing_key='hello', body='Hello World!')
print(" [x] Sent 'Hello World!'")
connection.close()
生产者
#!/usr/bin/env python
import pika

# ########################## 消费者 ##########################

connection = pika.BlockingConnection(pika.ConnectionParameters(host='192.168.21.104', port=4369))
channel = connection.channel()
channel.queue_declare(queue='hello')


def callback(ch, method, properties, body):
    print(" [x] Received %r" % body)

channel.basic_consume(callback, queue='hello', no_ack=True)
print(' [*] Waiting for messages. To exit press CTRL+C')
channel.start_consuming()
消费者

出现问题：

　　生产者代码和消费者代码报错，报错信息为pika.exceptions.ConnectionClosed，看出rabbitMQ服务端日志报错   "AMQPLAIN login refused: user 'guest' can only connect via localhost"。

解决：

echo "[{rabbit, [{loopback_users, []}]}]." >/etc/rabbitmq/rabbitmq.config

然后重启rabbitMQ服务

1、acknowledgment 消息不丢失

no-ack ＝ False，如果消费者遇到情况(its channel is closed, connection is closed, or TCP connection is lost)挂掉了，那么，RabbitMQ会重新将该任务添加到队列中。

import pika

connection = pika.BlockingConnection(pika.ConnectionParameters(
        host='10.211.55.4'))
channel = connection.channel()

channel.queue_declare(queue='hello')

def callback(ch, method, properties, body):
    print(" [x] Received %r" % body)
    import time
    time.sleep(10)
    print 'ok'
    ch.basic_ack(delivery_tag = method.delivery_tag)  # 新增代码，代码功能是消费者正常消费信息后，回复给服务端一个确认消息

channel.basic_consume(callback,
                      queue='hello',
                      no_ack=False)   # 设置消息不丢失功能开关

print(' [*] Waiting for messages. To exit press CTRL+C')
channel.start_consuming()
消费者

2、durable   消息不丢失

即：遇到RabbitMQ服务器宕机，如何保证消息不丢失

#!/usr/bin/env python
import pika

connection = pika.BlockingConnection(pika.ConnectionParameters(host='10.211.55.4'))
channel = connection.channel()

# make message persistent
channel.queue_declare(queue='hello', durable=True)  # 即告诉服务器端，要设置durable，尽可能确保我消息的可靠性

channel.basic_publish(exchange='',
                      routing_key='hello',
                      body='Hello World!',
                      properties=pika.BasicProperties(
                          delivery_mode=2, # make message persistent
                      ))
print(" [x] Sent 'Hello World!'")
connection.close()
生产者
#!/usr/bin/env python
# -*- coding:utf-8 -*-
import pika

connection = pika.BlockingConnection(pika.ConnectionParameters(host='10.211.55.4'))
channel = connection.channel()

# make message persistent
channel.queue_declare(queue='hello', durable=True)


def callback(ch, method, properties, body):
    print(" [x] Received %r" % body)
    import time
    time.sleep(10)
    print 'ok'
    ch.basic_ack(delivery_tag = method.delivery_tag)

channel.basic_consume(callback,
                      queue='hello',
                      no_ack=False)

print(' [*] Waiting for messages. To exit press CTRL+C')
channel.start_consuming()
消费者

3、消息获取顺序

默认消息队列里的数据是按照顺序被消费者拿走，例如：消费者1 去队列中获取 奇数 序列的任务，消费者1去队列中获取 偶数 序列的任务。

channel.basic_qos(prefetch_count=1) 表示谁来谁取，不再按照奇偶数排列

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

connection = pika.BlockingConnection(pika.ConnectionParameters(host='10.211.55.4'))
channel = connection.channel()

# make message persistent
channel.queue_declare(queue='hello')


def callback(ch, method, properties, body):
    print(" [x] Received %r" % body)
    import time
    time.sleep(10)
    print 'ok'
    ch.basic_ack(delivery_tag = method.delivery_tag)

channel.basic_qos(prefetch_count=1)

channel.basic_consume(callback,
                      queue='hello',
                      no_ack=False)

print(' [*] Waiting for messages. To exit press CTRL+C')
channel.start_consuming()
消费者

4、发布订阅

　　发布订阅和简单的消息队列区别在于，发布订阅会将消息发送给所有的订阅者，而消息队列中的数据被消费一次便消失。所以，RabbitMQ实现发布和订阅时，会为每一个订阅者创建一个队列，而发布者发布消息时，会将消息放置在所有相关队列中。

 exchange type = fanout

#!/usr/bin/env python
import pika
import sys

connection = pika.BlockingConnection(pika.ConnectionParameters(
        host='localhost'))
channel = connection.channel()

channel.exchange_declare(exchange='logs',    # 设置一个exchange，以后发送消息都直接发送给名为logs的exchange
                         type='fanout')

message = ' '.join(sys.argv[1:]) or "info: Hello World!"
channel.basic_publish(exchange='logs',
                      routing_key='',
                      body=message)
print(" [x] Sent %r" % message)
connection.close()
发布者
#!/usr/bin/env python
import pika

connection = pika.BlockingConnection(pika.ConnectionParameters(
        host='localhost'))
channel = connection.channel()  # 申请一个频道

channel.exchange_declare(exchange='logs',  # 设置一个exchange
                         type='fanout')

# 由RabbitMQ分配一个消息队列，并返回一个queue_name
result = channel.queue_declare(exclusive=True)
queue_name = result.method.queue

channel.queue_bind(exchange='logs',  # 将所有消息发送给所有与logs这个exchange做绑定的消息队列
                   queue=queue_name)

print(' [*] Waiting for logs. To exit press CTRL+C')

def callback(ch, method, properties, body):
    print(" [x] %r" % body)

channel.basic_consume(callback,
                      queue=queue_name,
                      no_ack=True)

channel.start_consuming()
订阅者

5、关键字发送

 exchange type = direct

之前事例，发送消息时明确指定某个队列并向其中发送消息，RabbitMQ还支持根据关键字发送，即：队列绑定关键字，发送者将数据根据关键字发送到消息exchange，exchange根据 关键字 判定应该将数据发送至指定队列。

#!/usr/bin/env python
import pika
import sys

connection = pika.BlockingConnection(pika.ConnectionParameters(
        host='localhost'))
channel = connection.channel()

channel.exchange_declare(exchange='direct_logs',
                         type='direct')

result = channel.queue_declare(exclusive=True)
queue_name = result.method.queue

severities = sys.argv[1:]
if not severities:
    sys.stderr.write("Usage: %s [info] [warning] [error]\n" % sys.argv[0])
    sys.exit(1)

for severity in severities:
    channel.queue_bind(exchange='direct_logs',
                       queue=queue_name,
                       routing_key=severity)

print(' [*] Waiting for logs. To exit press CTRL+C')

def callback(ch, method, properties, body):
    print(" [x] %r:%r" % (method.routing_key, body))

channel.basic_consume(callback,
                      queue=queue_name,
                      no_ack=True)

channel.start_consuming()
消费者
#!/usr/bin/env python
import pika
import sys

connection = pika.BlockingConnection(pika.ConnectionParameters(
        host='localhost'))
channel = connection.channel()

channel.exchange_declare(exchange='direct_logs',
                         type='direct')

severity = sys.argv[1] if len(sys.argv) > 1 else 'info'
message = ' '.join(sys.argv[2:]) or 'Hello World!'
channel.basic_publish(exchange='direct_logs',
                      routing_key=severity,
                      body=message)
print(" [x] Sent %r:%r" % (severity, message))
connection.close()
生产者

6、模糊匹配

 exchange type = topic

在topic类型下，可以让队列绑定几个模糊的关键字，之后发送者将数据发送到exchange，exchange将传入”路由值“和 ”关键字“进行匹配，匹配成功，则将数据发送到指定队列。

  • # 表示可以匹配 0 个 或 多个 单词
  • *  表示只能匹配 一个 单词
发送者路由值              队列中
old.boy.python          old.*  -- 不匹配
old.boy.python          old.#  -- 匹配
#!/usr/bin/env python
import pika
import sys

connection = pika.BlockingConnection(pika.ConnectionParameters(
        host='localhost'))
channel = connection.channel()

channel.exchange_declare(exchange='topic_logs',
                         type='topic')

result = channel.queue_declare(exclusive=True)
queue_name = result.method.queue

binding_keys = sys.argv[1:]
if not binding_keys:
    sys.stderr.write("Usage: %s [binding_key]...\n" % sys.argv[0])
    sys.exit(1)

for binding_key in binding_keys:
    channel.queue_bind(exchange='topic_logs',
                       queue=queue_name,
                       routing_key=binding_key)

print(' [*] Waiting for logs. To exit press CTRL+C')

def callback(ch, method, properties, body):
    print(" [x] %r:%r" % (method.routing_key, body))

channel.basic_consume(callback,
                      queue=queue_name,
                      no_ack=True)

channel.start_consuming()
消费者
#!/usr/bin/env python
import pika
import sys

connection = pika.BlockingConnection(pika.ConnectionParameters(
        host='localhost'))
channel = connection.channel()

channel.exchange_declare(exchange='topic_logs',
                         type='topic')

routing_key = sys.argv[1] if len(sys.argv) > 1 else 'anonymous.info'
message = ' '.join(sys.argv[2:]) or 'Hello World!'
channel.basic_publish(exchange='topic_logs',
                      routing_key=routing_key,
                      body=message)
print(" [x] Sent %r:%r" % (routing_key, message))
connection.close()
生产者

7、RabbitMQ实现RPC

资料：http://www.rabbitmq.com/tutorials/tutorial-six-python.html

 8、RabbitMQ常见应用场景

  • 最简单的hello world
  • 工作队列（工人分配任务）
  • 发布/订阅
  • 路由(有选择的接收消息）
  • 主题（基于模式接收消息）
  • RPC（远程过程调用实现）

RabbitMQ相关参考资料（官网资料为主，其他为辅）：

http://rabbitmq.mr-ping.com/tutorials_with_python/[1]Hello_World.html

http://www.cnblogs.com/duanv/p/5006952.html

SQLAlchemy

　　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://<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

一、底层处理

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()
#!/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)


# 事务操作
with engine.begin() as conn:
    conn.execute("insert into table (x, y, z) values (1, 2, 3)")
    conn.execute("my_special_procedure(5)")
    
    
conn = engine.connect()
# 事务操作 
with conn.begin():
       conn.execute("some statement", {'x':5, 'y':10})
事务操作

注：查看数据库连接：show status like 'Threads%';

二、ORM功能使用

　　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。

1、创建表

#!/usr/bin/env python
# -*- coding:utf-8 -*-
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, Integer, String, ForeignKey, UniqueConstraint, Index
from sqlalchemy.orm import sessionmaker, relationship
from sqlalchemy import create_engine

engine = create_engine("mysql+pymysql://root:123@127.0.0.1:3306/t1", max_overflow=5)

Base = declarative_base()

# 创建单表
class Users(Base):
    __tablename__ = 'users'
    id = Column(Integer, primary_key=True)
    name = Column(String(32))
    extra = Column(String(16))

    __table_args__ = (
    UniqueConstraint('id', 'name', name='uix_id_name'),
        Index('ix_id_name', 'name', 'extra'),
    )


# 一对多
class Favor(Base):
    __tablename__ = 'favor'
    nid = Column(Integer, primary_key=True)
    caption = Column(String(50), default='red', unique=True)


class Person(Base):
    __tablename__ = 'person'
    nid = Column(Integer, primary_key=True)
    name = Column(String(32), index=True, nullable=True)
    favor_id = Column(Integer, ForeignKey("favor.nid"))


# 多对多
class Group(Base):
    __tablename__ = 'group'
    id = Column(Integer, primary_key=True)
    name = Column(String(64), unique=True, nullable=False)
    port = Column(Integer, default=22)


class Server(Base):
    __tablename__ = 'server'

    id = Column(Integer, primary_key=True, autoincrement=True)
    hostname = Column(String(64), unique=True, nullable=False)


class ServerToGroup(Base):
    __tablename__ = 'servertogroup'
    nid = Column(Integer, primary_key=True, autoincrement=True)
    server_id = Column(Integer, ForeignKey('server.id'))
    group_id = Column(Integer, ForeignKey('group.id'))


def init_db():
    Base.metadata.create_all(engine)


def drop_db():
    Base.metadata.drop_all(engine)
创建表

 

2、操作表

#!/usr/bin/env python
# -*- coding:utf-8 -*-
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, Integer, String, ForeignKey, UniqueConstraint, Index
from sqlalchemy.orm import sessionmaker, relationship
from sqlalchemy import create_engine

engine = create_engine("mysql+pymysql://root:123@127.0.0.1:3306/t1", max_overflow=5)

Base = declarative_base()

# 创建单表
class Users(Base):
    __tablename__ = 'users'
    id = Column(Integer, primary_key=True)
    name = Column(String(32))
    extra = Column(String(16))

    __table_args__ = (
    UniqueConstraint('id', 'name', name='uix_id_name'),
        Index('ix_id_name', 'name', 'extra'),
    )

    def __repr__(self):
        return "%s-%s" %(self.id, self.name)

# 一对多
class Favor(Base):
    __tablename__ = 'favor'
    nid = Column(Integer, primary_key=True)
    caption = Column(String(50), default='red', unique=True)

    def __repr__(self):
        return "%s-%s" %(self.nid, self.caption)

class Person(Base):
    __tablename__ = 'person'
    nid = Column(Integer, primary_key=True)
    name = Column(String(32), index=True, nullable=True)
    favor_id = Column(Integer, ForeignKey("favor.nid"))
    # 与生成表结构无关，仅用于查询方便
    favor = relationship("Favor", backref='pers')

# 多对多
class ServerToGroup(Base):
    __tablename__ = 'servertogroup'
    nid = Column(Integer, primary_key=True, autoincrement=True)
    server_id = Column(Integer, ForeignKey('server.id'))
    group_id = Column(Integer, ForeignKey('group.id'))
    group = relationship("Group", backref='s2g')
    server = relationship("Server", backref='s2g')

class Group(Base):
    __tablename__ = 'group'
    id = Column(Integer, primary_key=True)
    name = Column(String(64), unique=True, nullable=False)
    port = Column(Integer, default=22)
    # group = relationship('Group',secondary=ServerToGroup,backref='host_list')


class Server(Base):
    __tablename__ = 'server'

    id = Column(Integer, primary_key=True, autoincrement=True)
    hostname = Column(String(64), unique=True, nullable=False)


def init_db():
    Base.metadata.create_all(engine)


def drop_db():
    Base.metadata.drop_all(engine)

init_db()     # 执行数据库创建
drop_db()   # 删除所有数据库表

Session = sessionmaker(bind=engine)    # 连接数据库，后续使用session进行相关操作
session = Session()
表结构和连接数据库

 

3、增删改查操作

# 增
# 增加单条数据
obj = Users(name="alex0", extra='sb')
session.add(obj)

# 增加多条数据
session.add_all([
    Users(name="alex1", extra='sb'),
    Users(name="alex2", extra='sb'),
])
session.commit()

# 删
session.query(Users).filter(Users.id > 2).delete()
session.commit()

# 改
session.query(Users).filter(Users.id > 2).update({"name" : "099"})
session.query(Users).filter(Users.id > 2).update({Users.name: Users.name + "099"}, synchronize_session=False)
session.query(Users).filter(Users.id > 2).update({"num": Users.num + 1}, synchronize_session="evaluate")
session.commit()

# 查
ret = session.query(Users).all()
ret = session.query(Users.name, Users.extra).all()
ret = session.query(Users).filter_by(name='alex').all()      #  返回的是一个类似列表的对象
ret = session.query(Users).filter_by(name='alex').first()  #   记录不存在时，first() 会返回 None

# 其他
#　条件
ret = session.query(Users).filter_by(name='alex').all()
ret = session.query(Users).filter(Users.id > 1, Users.name == 'eric').all()
ret = session.query(Users).filter(Users.id.between(1, 3), Users.name == 'eric').all()
ret = session.query(Users).filter(Users.id.in_([1,3,4])).all()
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='eric'))).all()
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 == 'eric')).all()
ret = session.query(Users).filter(
    or_(
        Users.id < 2,
        and_(Users.name == 'eric', Users.id > 3),
        Users.extra != ""
    )).all()


# 通配符
ret = session.query(Users).filter(Users.name.like('e%')).all()
ret = session.query(Users).filter(~Users.name.like('e%')).all()

# 限制
ret = session.query(Users)[1:2]

# 排序
ret = session.query(Users).order_by(Users.name.desc()).all()
ret = session.query(Users).order_by(Users.name.desc(), Users.id.asc()).all()

# 分组
from sqlalchemy.sql import func

ret = session.query(Users).group_by(Users.extra).all()
ret = session.query(
    func.max(Users.id),
    func.sum(Users.id),
    func.min(Users.id)).group_by(Users.name).all()

ret = session.query(
    func.max(Users.id),
    func.sum(Users.id),
    func.min(Users.id)).group_by(Users.name).having(func.min(Users.id) >2).all()

# 连表

ret = session.query(Users, Favor).filter(Users.id == Favor.nid).all()

ret = session.query(Person).join(Favor).all()

ret = session.query(Person).join(Favor, isouter=True).all()


# 组合
q1 = session.query(Users.name).filter(Users.id > 2)
q2 = session.query(Favor.caption).filter(Favor.nid < 2)
ret = q1.union(q2).all()

q1 = session.query(Users.name).filter(Users.id > 2)
q2 = session.query(Favor.caption).filter(Favor.nid < 2)
ret = q1.union_all(q2).all()
增删改查
#!/usr/bin/env python
# -*- coding:utf-8 -*-
from sqlalchemy import create_engine,and_,or_,func,Table
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, Integer, String,ForeignKey,UniqueConstraint,DateTime
from  sqlalchemy.orm import sessionmaker,relationship

Base = declarative_base() #生成一个SqlORM 基类

# 服务器账号和组
# HostUser2Group = Table('hostuser_2_group',Base.metadata,
#     Column('hostuser_id',ForeignKey('host_user.id'),primary_key=True),
#     Column('group_id',ForeignKey('group.id'),primary_key=True),
# )

# 用户和组关系表，用户可以属于多个组，一个组可以有多个人
UserProfile2Group = Table('userprofile_2_group',Base.metadata,
    Column('userprofile_id',ForeignKey('user_profile.id'),primary_key=True),
    Column('group_id',ForeignKey('group.id'),primary_key=True),
)

# 程序登陆用户和服务器账户，一个人可以有多个服务器账号，一个服务器账号可以给多个人用
UserProfile2HostUser= Table('userprofile_2_hostuser',Base.metadata,
    Column('userprofile_id',ForeignKey('user_profile.id'),primary_key=True),
    Column('hostuser_id',ForeignKey('host_user.id'),primary_key=True),
)


class Host(Base):
    __tablename__='host'
    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)
    def __repr__(self):
        return  "<id=%s,hostname=%s, ip_addr=%s>" %(self.id,
                                                    self.hostname,
                                                    self.ip_addr)


class HostUser(Base):
    __tablename__ = 'host_user'
    id = Column(Integer,primary_key=True)
    AuthTypes = [
        (u'ssh-passwd',u'SSH/Password'),
        (u'ssh-key',u'SSH/KEY'),
    ]
    # auth_type = Column(ChoiceType(AuthTypes))
    auth_type = Column(String(64))
    username = Column(String(64),unique=True,nullable=False)
    password = Column(String(255))

    host_id = Column(Integer,ForeignKey('host.id'))
    
    # groups = relationship('Group',
    #                       secondary=HostUser2Group,
    #                       backref='host_list')

    __table_args__ = (UniqueConstraint('host_id','username', name='_host_username_uc'),)

    def __repr__(self):
        return  "<id=%s,name=%s>" %(self.id,self.username)


class Group(Base):
    __tablename__ = 'group'
    id = Column(Integer,primary_key=True)
    name = Column(String(64),unique=True,nullable=False)
    def __repr__(self):
        return  "<id=%s,name=%s>" %(self.id,self.name)


class UserProfile(Base):
    __tablename__ = 'user_profile'
    id = Column(Integer,primary_key=True)
    username = Column(String(64),unique=True,nullable=False)
    password = Column(String(255),nullable=False)
    # host_list = relationship('HostUser',
    #                       secondary=UserProfile2HostUser,
    #                       backref='userprofiles')
    # groups = relationship('Group',
    #                       secondary=UserProfile2Group,
    #                       backref='userprofiles')
    def __repr__(self):
        return  "<id=%s,name=%s>" %(self.id,self.username)


class AuditLog(Base):
    __tablename__ = 'audit_log'
    id = Column(Integer,primary_key=True)
    userprofile_id = Column(Integer,ForeignKey('user_profile.id'))
    hostuser_id = Column(Integer,ForeignKey('host_user.id'))
    action_choices2 = [
        (u'cmd',u'CMD'),
        (u'login',u'Login'),
        (u'logout',u'Logout'),
    ]
    action_type = Column(ChoiceType(action_choices2))
    #action_type = Column(String(64))
    cmd = Column(String(255))
    date = Column(DateTime)

    # user_profile = relationship("UserProfile")
    #bind_host = relationship("BindHost")


engine = create_engine("mysql+pymsql://root:123@localhost:3306/stupid_jumpserver",echo=False)
Base.metadata.create_all(engine) #创建所有表结构
alex堡垒机案例

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。

更多功能参见文档，猛击这里下载PDF

原文地址：http://www.cnblogs.com/wupeiqi/articles/5699254.html

 补充知识：代码优先 and 数据库优先

数据库优先：先创建数据库，包括表和字段的建立，然后根据数据库生成ORM的代码，它是先创建数据库，再创建相关程序代码

代码优先：与数据库优先恰恰相反，先写代码，后根据代码生成数据库相关内容

参考资料：

http://blog.csdn.net/ycl295644/article/details/49796111

posted @ 2016-07-19 16:13  每天进步一点点！！！  阅读(1436)  评论(0)  编辑  收藏  举报