SqlAlchemy使用教程(三) CoreAPI访问与操作数据库详解

SqlAlchemy使用教程(一) 原理与环境搭建

SqlAlchemy使用教程(二) 入门示例及编程步骤

三、使用Core API访问与操作数据库

Sqlalchemy 的Core部分集成了DB API, 事务管理，schema描述等功能，ORM构筑于其上。本章介绍创建 Engine对象，使用基本的 Sql Express Language 方法，以及如何实现对数据库的CRUD操作等内容。

1、创建DB engine 对象

1.1创建database engine 对象

Engine 是db连接管理类，

语法:

python 复制代码

from sqlalchemy import create_engine
＃创建引擎对象
engine = create_engine("sqlite:///:memory:", echo=True)
＃连接数据库
conn = engine.connect()

Sqlalchemy.create_engine( ) 方法第1个参数是db连接表达式，格式为：

python 复制代码

dialect[+driver]://user:password@host/dbname

dialect 通常为数据库类型，如sqlite, mysql, mongodb， etc.
driver 是python 访问数据库的包。
如 sqlite+sqlite3, mysql+mysqlconnector

1.2 连接至各类数据库的配置

1.2.1 sqlite 连接

上面示例是sqlite的连接表达式。 Driver是python访问数据库的DBAPI库。

python 复制代码

e = create_engine('sqlite:///path/to/database.db')

如果是绝对地址 sqlite:usr/local/myproject/database.db

:memory 表示使用内存数据库，不保存在硬盘。

对于windows 系统，

python 复制代码

e = create_engine('sqlite:///C:\\myapp\\db\\main.db')

1.2.2 连接mysql

Mysql 的DBAPI，常用的有PyMysql 与 mysql-connector，其连接表达式分别为：

python 复制代码

mysql+pymysql://root:123456@192.168.99.240:3306/testdb
mysql+mysqlconnector://roprot:123456@192.168.99.240:3306/testdb

1.2.3 连接PostgreSQL

通常使用的接口库为 psycopg2

python 复制代码

postgresql+psycopg2://user:password@host:port/dbname[?key=value&key=value...]

engine = create_engine(
    "postgresql+psycopg2://scott:tiger@localhost/test",
    isolation_level="SERIALIZABLE",
)

Ssl连接

python 复制代码

engine = sa.create_engine(
   "postgresql+psycopg2://scott:tiger@192.168.0.199:5432/test?sslmode=require"
)

1.2.4 连接MongoDB

python 复制代码

engine = create_engine("mongodb:///?Server=MyServer&Port=27017&Database=test&User=test&Password=Password")

定义1个mapping类
base = declarative_base()
class restaurants(base):
__tablename__ = "restaurants"
borough = Column(String,primary_key=True)
cuisine = Column(String)

查询：

python 复制代码

engine=create_engine("mongodb:///?Server=MyServer&Port=27017&Database=test&User=test&Password=Password")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(restaurants).filter_by(Name="Morris Park Bake Shop"):
print("borough: ", instance.borough)
print("cuisine: ", instance.cuisine)
print("---------")

1.3创建connect 对象

语法：

python 复制代码

conn = engine.connect()

如

python 复制代码

e = create_engine('sqlite:///C:\\myapp\\db\\main.db')
conn = e.connect()

推荐使用context with 语法使用connect对象

python 复制代码

from sqlalchemy import create_engine, text
engine = create_engine('sqlite:///C:\\myapp\\db\\main.db')
with engine.connect() as connection:
    result = connection.execute(text("select username from users"))
    for row in result:
        print("username:", row["username"])

如果修改了数据，应调用 conn.commit() 提交transaction

2. SQL Express Language 常用方法

Sqlalchemy 对sql进行了封装，其SQL Express语法比直接使用sql 语句更方便，优势是传参与获取返回值更省事。

2.1 使用 text() 生成SQL Express语句

text()方法是CoreAPI中最基础的方法之一，主要作用，用于封装 sql 语句

python 复制代码

from sqlalchemy import text

t_sql = text("SELECT * FROM users")
result = connection.execute(t_sql)

传参：

python 复制代码

t_sql = text("SELECT * FROM users WHERE id=:user_id")
result = connection.execute(t_sql, { 'user_id': 12 } )

如果使用r" " ，则用 : 来表示:

2.2 bindparams() 方法传参

也可以通过 text(sql_statement).bindparams() 直接构建完整的SQL语句

python 复制代码

from sqlalchemy import text, bindparams
stmt = text("SELECT id, name FROM user WHERE name=:name "
            "AND timestamp=:timestamp")
stmt = stmt.bindparams(name='jack',
            timestamp=datetime.datetime(2012, 10, 8, 15, 12, 5)
)
result = conn.execute(stmt)
print(result.all())

bindparams()中可添加参数Type检查：

python 复制代码

from sqlalchemy import text
stmt = text("SELECT id, name FROM user WHERE name=:name "
            "AND timestamp=:timestamp")
stmt = stmt.bindparams(
    bindparam('name', type_=String),
    bindparam('timestamp', type_=DateTime)
)
stmt = stmt.bindparams(name='jack',
            timestamp=datetime.datetime(2012, 10, 8, 15, 12, 5))
result = conn.execute(stmt)
print(result.all())

3, 解析查询结果

查询结果类型为 sqlalchemy.engine.Result 类，是1个由 object 组成的列表。可以用多种方法访问：

all() ， return all rows in a list
columns('col_1', 'col_2') 指定返回每row 的字段， iterable
fetchall(), fetchone(), fetchmany()
first() 返回第1行。
keys() 返回row的字段名，是iterable 类型
mappings(), 列表元素为dict类型，
result.close() 关闭result对象

说明：

遍历查询结果， all()- , fetchall(), fetchmany(), columns（），结果为： list[tuple,...], 或iterable,
对row 字段，可以用key, index ， row[0], row['id'], row['name'], 也可以用row.name ，如

python 复制代码

result = conn.execute(text("select x, y from some_table"))
for row in result:
    print(f"Row: {row.x} {row.y}")

result.mapping() 返回结果的row 类型为dict,

python 复制代码

result = conn.execute(text("select x, y from some_table"))
for dict_row in result.mappings():
    x = dict_row["x"]
    y = dict_row["y"]

4. 使用connect 对象执行CRUD操作

SqlAlchemy可以用connect对象与 session 对象来执行SQL express

connect对象是直接调用DBAPI执行SQL语句，这是使用SqlAlchemy 最简单的方式,同时支持部分Sqlalchemy 的SQL Express 封装语法，但执行的SQL语句依然还要符合各数据库的接口库要求。

Session对象则实现了同1套接口适用于所有数据库。但主要用于ORM API方式。

connect对象操作数据库的好处：可使用text()方法生成SQL语句，利用bindparams() 传值，以及做类型检查。同时支持多线程访问数据库。

创建表的方法，前面已讲过。下面示例为 insert, update, delete 操作

python 复制代码

# insert row 
print("-"*50+"Insert operation")
stmt = text("INSERT INTO some_table VALUES(:x, :y)").bindparams(x=6,y=19)
with engine.connect() as conn:
    conn.execute(stmt)
    conn.commit()
    result = conn.execute( text("select * from some_table") )
    print(result.all())

# update row 
print("-"*50+"update operation")
stmt = text("UPDATE some_table SET y=:y WHERE x=:x").bindparams(y=99,x=5)
with engine.connect() as conn:
    conn.execute(stmt)
    conn.commit()
    result = conn.execute( text("select * from some_table") )
    print(result.all())

# delete row 
print("-"*50+"delete operation")
stmt = text("DELETE FROM some_table WHERE x=:x").bindparams(x=4)
with engine.connect() as conn:
    conn.execute(stmt)
    conn.commit()
    result = conn.execute( text("select * from some_table") )
    print(result.rowcount)
    print(result.all())

output:

bash 复制代码

--------------------------------------------------Insert operation
2023-12-03 15:50:36,978 INFO sqlalchemy.engine.Engine BEGIN (implicit)
2023-12-03 15:50:36,978 INFO sqlalchemy.engine.Engine INSERT INTO some_table VALUES(?, ?)
2023-12-03 15:50:36,978 INFO sqlalchemy.engine.Engine [generated in 0.00085s] (6, 19)
2023-12-03 15:50:36,979 INFO sqlalchemy.engine.Engine COMMIT
2023-12-03 15:50:36,980 INFO sqlalchemy.engine.Engine BEGIN (implicit)
2023-12-03 15:50:36,980 INFO sqlalchemy.engine.Engine select * from some_table
2023-12-03 15:50:36,981 INFO sqlalchemy.engine.Engine [generated in 0.00132s] ()
[(1, 1), (2, 4), (3, 10), (4, 11), (5, 25), (6, 19)]
2023-12-03 15:50:36,982 INFO sqlalchemy.engine.Engine ROLLBACK
--------------------------------------------------update operation
 [(1, 1), (2, 4), (3, 10), (4, 11), (5, 99), (6, 19)]
2023-12-03 15:50:36,985 INFO sqlalchemy.engine.Engine ROLLBACK
--------------------------------------------------delete operation
[(1, 1), (2, 4), (3, 10), (5, 99), (6, 19)]
2023-12-03 15:50:36,989 INFO sqlalchemy.engine.Engine ROLLBACK

5. 表间关系处理

Sqlalchemy 使用DBAPI处理表间关系语法是依据数据库规定, 但基本均支持标准SQL语法

5.1 创建外键字段的语法：

bash 复制代码

 CREATE TABLE tracks(
      ......
  trackartist   INTEGER,     -- 外键字段
FOREIGN KEY(trackartist) REFERENCES artist(artistid)
)

辅表artist.id字段须为主键或unique index。

5.2 各种表间关系的实现方式：

One to one: 还是用 foreign key来实现。
One to many: 就是外键
Many to many：需要中间表，用2个foreign key 与两张表分别建立 one to many 关系。

示例：

python 复制代码

import sqlalchemy

from sqlalchemy import create_engine
from sqlalchemy import text
from sqlalchemy.orm import sessionmaker  

engine = create_engine("sqlite:///order.db")

# create table people 
with engine.connect() as conn:
    conn.execute(text("drop table if exists people;"))
    stmt = text("""
        CREATE TABLE people(
                id  integer PRIMARY KEY,
                name TEXT, 
                age  INTEGER
            )
    """ )
    conn.execute(stmt)
    conn.execute(
         text("INSERT INTO people (id,name, age) VALUES (:id,:name, :age)"),
         [ 
            {'id': 1, "name": 'Jack','age':30 }, 
            {'id': 2, "name": 'Smith','age':28 }, 
            {'id': 3, "name": 'Wang','age':35 }, 
          ]
     )
    conn.commit()
    result = conn.execute( text("select * from people") )
    print(result.rowcount)
    print(result.all())

# create table order
# 创建会话（Session）  
with engine.connect() as conn: 
    conn.execute(text("drop table if exists teams"))
    stmt_1 = text("""
        create table teams(
                id  integer PRIMARY KEY,
                team_name  TEXT, 
                pid  integer,
                foreign key (pid) REFERENCES people(id)
        )
    """)
    conn.execute(stmt_1)
    conn.commit()
    conn.execute(
         text("INSERT INTO teams (id, team_name, pid) VALUES (:id, :team_name, :pid)"),
         [ 
            {'id': 101, "team_name": 'TV product','pid':1 }, 
            {'id': 102, "team_name": 'Software development','pid':2 }, 
            {'id': 103, "team_name": 'Electric development','pid':2 }, 
          ]
     )
conn.commit()
    # 跨表查询
    result = conn.execute( text("select a.id, a.team_name, b.name from teams as a left join people as b on a.pid=b.id") )
    print(result.rowcount)
    for row in result.mappings():
        print(row['id'], row['team_name'], row['name'])

6. 通过多线程访问Database

sqlalchemy的engine可做为全局变量，将connect对象,或 session对象传入线程，实现多线程访问：

示例：

python 复制代码

def thread_db(conn,name):
    try:  
        result = conn.execute( text("select * from people") )
        print(result.rowcount)
        print(f"thread {{ name }} result: ")
        print(result.all())
    except Exception as e:
        print("can't open connection object")
    finally: 
        conn.close()

from threading import Thread

t1 = Thread(target=thread_db, args=(engine.connect(),"thread_a"))
t2 = Thread(target=thread_db, args=(engine.connect(),"thread_b"))
t1.start()
t2.start()
t1.join()
t2.join()
print("main thread is ended")
output: 
thread { name } result:
thread { name } result:
[(1, 'Jack', 30), (2, 'Smith', 28), (3, 'Wang', 35)]
[(1, 'Jack', 30), (2, 'Smith', 28), (3, 'Wang', 35)]
main thread is ended