近段时间在看SQLAlchemy，总之万事开头难，但是么办法。
Database UrlsThe create_engine() function produces an Engine object based on a URL. These URLs follow RFC-1738 , and usually can include username, password, hostname, database name as well as optional keyword arguments for additional configuration. In some cases a file path is accepted, and in others a “data source name” replaces the “host” and “database” portions. The typical form of a database URL is:
dialect+driver://username:password@host:port/database 标准连接数据库规范 MS-SQL连接案例 Microsoft SQL ServerThe SQL Server dialect uses pyodbc as the default DBAPI. pymssql is also available:
# pyodbcengine = create_engine('mssql+pyodbc://scott:tiger@mydsn')
# pymssql
engine = create_engine('mssql+pymssql://scott:tiger@hostname:port/dbname')
More notes on connecting to SQL Server at Microsoft SQL Server .
这里面测试用的是pyodbc进行连接的，分两种
engine=create_engine("mssql+pyodbc://sa:@192.168.6.112:1433/FactoryHome?driver=SQL+Server+Native+Client+10.0")
还有一种就是通过微软的dsn进行连接，如不知道dsn连接，可以百度一下看看是什么意思
对数据的插入
from sqlalchemy import *
engine=create_engine("mssql+pyodbc://sa:@192.168.6.112:1433/FactoryHome?driver=SQL+Server+Native+Client+10.0")
metadata=MetaData()
Table_1=Table("Table_1",metadata,
Column("Code",String(10)),Column("Name",String(10)))
ins=Table_1.insert().values(Code='cccccc',Name='王二')
conn=engine.connect()
result=conn.execute(ins)
参数化的形式，感觉有点感觉比拼接SQL来的快。
result=conn.execute(Table_1.insert(),Code='kkkkk',Name='网易')
对于给定的参数也可以这样传值。
对于数据的查询，也必须的先构造一个TABLE，然后对应的字段进行查询
from sqlalchemy import *
engine=create_engine("mssql+pyodbc://sa:@192.168.6.112:1433/FactoryHome?driver=SQL+Server+Native+Client+10.0")
metadata=MetaData()
Table_1=Table("Table_1",metadata,
Column("Code",String(10)),Column("Name",String(10)))
conn=engine.connect()
result=conn.execute(select([Table_1]))
for row in result:
print(row)
SQLAlchemy最好的方式就是能像SQL语句一样能实现join连接查询
>>> s = select([users, addresses]).where(users.c.id == addresses.c.user_id)
SQL>>> for row in conn.execute(s):
... print(row)
这样可以通过相关表的关联就能查询数据。
有好多东西，再叙。