SQLAlchemy 使用总结

使用 sqlalchemy 有3种方式:

方式1, 使用raw sql;

方式2, 使用SqlAlchemy的sql expression;

方式3, 使用ORM.

前两种方式可以统称为 core 方式. 本文讲解 core 方式访问数据库, 不涉及 ORM.

对于绝大多数应用, 推荐使用 SqlAlchemy. 即使是使用raw sql, SqlAlchemy 也可以带来如下好处:

1. 内建数据库连接池. [注意]如果是sqlalchemy+cx_oracle的话, 需要禁掉 connection pool, 否则会有异常. 方法是设置sqlalchemy.poolclass为sqlalchemy.pool.NullPool

2. 强大的log功能

3. 数据库中立的写法, 包括: sql参数写法, limit语法

4. 特别提一下, where()条件的==your_value, 如果your_value等于None, 真正的Sql会转为Is None

SqlAlchemy的sql expression和raw sql的比较:

1. sql expression 写法是纯python代码, 阅读性更好, 尤其是在使用insert()方法时, 字段名和取值成对出现.

2. raw sql 比 sql expression 更灵活, 如果SQL/DDL很复杂, raw sql就更有优势了.

具体案例如下

# coding: utf-8

from sqlalchemy import *
import tushare as ts
import pandas as pd
from sqlalchemy.orm import sessionmaker,mapper
from datetime import *

engine = create_engine('mysql+pymysql://root:123456@127.0.0.1/mystock?charset=utf8')

#%%  1  hand-written SQL 方法
result = engine.execute('select * from stock_basics where pe < %s', 2)

# sqlalchemy推荐使用text()函数封装一下sql字符串,不同数据库, 可以使用统一的sql参数传递写法. 参数须以:号引出.
result = engine.execute(text('select * from stock_basics where pe < :pe'), {'pe': 2})

# 遍历result时, 得到的每一个行都是RowProxy对象, 获取字段的方法非常灵活, 下标和字段名甚至属性都行.
# rowproxy[0] == rowproxy['id'] == rowproxy.id
ans = result.fetchall() # 获取所有数据
ans1 = pd.DataFrame(ans) # 将数据转成 DataFrame格式

#  事务处理
conn = engine.connect()
conn.begin()
try:
dosomething(connection)
conn.commit()
except:
conn.rollback()
conn.close()

#%%  SQL-expressions in Python 方法
meta = MetaData(bind=engine, reflect=True)
table = meta.tables['stock_basics']
result2 = list(engine.execute(table.select(table.c.pe < 2)))   # pe为stock_basics的一个列名

#%% ORM 方法   表中要有主键
engine.echo = True  # We want to see the SQL we're creating
metadata = MetaData(engine)

# The stock_basics table already exists, so no need to redefine it. Just
# load it from the database using the "autoload" feature.
users = Table('stock_basics', metadata, autoload=True)

def run(stmt):
rs = stmt.execute()
for row in rs:
print(row)

# Most WHERE clauses can be constructed via normal comparisons
s = users.select(users.c.code == '000001')
run(s)
s = users.select(users.c.pe < 1)  # pe为stock_basics的一个列名
rs = s.execute().fetchall()
ans2 = pd.DataFrame(rs)    #将结果转换成 DataFrame格式

# Python keywords like "and", "or", and "not" can't be overloaded, so
# SQLAlchemy uses functions instead
s = users.select(and_(users.c.age < 40, users.c.name != 'Mary'))
s = users.select(or_(users.c.age < 40, users.c.name != 'Mary'))
s = users.select(not_(users.c.name == 'Susan'))

# Or you could use &, | and ~ -- but watch out for priority!
s = users.select((users.c.age < 40) & (users.c.name != 'Mary'))  #最好添加（），注意优先级
s = users.select((users.c.age < 40) | (users.c.name != 'Mary'))
s = users.select(~(users.c.name == 'Susan'))

# There's other functions too, such as "like", "startswith", "endswith"
s = users.select(users.c.name.startswith('M'))
s = users.select(users.c.name.like('%a%'))
s = users.select(users.c.name.endswith('n'))

# The "in" and "between" operations are also available
s = users.select(users.c.age.between(30,39))
# Extra underscore after "in" to avoid conflict with Python keyword
s = users.select(users.c.name.in_('Mary', 'Susan'))

# If you want to call an SQL function, use "func"
s = users.select(func.substr(users.c.name, 2, 1) == 'a')

# You don't have to call select() on a table; it's got a bare form
s = select([users], users.c.name != 'Carl')
s = select([users.c.name, users.c.age], users.c.name != 'Carl')

# This can be handy for things like count()
s = select([func.count(users.c.user_id)])
# Here's how to do count(*)
s = select([func.count("*")], from_obj=[users])
#%%多表联查
#    现在存在两个表
users = Table('users', metadata,
Column('user_id', Integer, primary_key=True),
Column('name', String(40)),
Column('age', Integer),)
users.create()
#    emails = Table('emails', metadata,
#        Column('email_id', Integer, primary_key=True),
#        Column('address', String),
#        Column('user_id', Integer, ForeignKey('users.user_id')),)
s = select([users, emails], emails.c.user_id == users.c.user_id)
# 查询部分列
s = select([users.c.name, emails.c.address], emails.c.user_id == users.c.user_id)
#基于外键的李娜和查询
s = join(users, emails).select()
#使用 outerjoin 查询所有用户，不论是否有邮箱
s = outerjoin(users, emails).select()

#%% 将数据库中的对象映射到对象中
users = Table('users', metadata, autoload=True)
# These are the empty classes that will become our data classes
class User(object):
pass

usermapper = mapper(User, users)
session = DBSession()
#  查询 -----------------
query = session.query(User)
print(query) # 显示SQL 语句
print(query.statement) # 同上
for user in query: # 遍历时查询
print(user.name)
print(query.all()) # 返回的是一个类似列表的对象
print(query.first().name) # 记录不存在时，first() 会返回 None
# print(query.one().name) # 不存在，或有多行记录时会抛出异常
print(query.filter(User.id == 2).first().name)
print(query.get(2).name) # 以主键获取，等效于上句
print(query.filter('id = 2').first().name) # 支持字符串
query2 = session.query(User.name)
print(query2.all()) # 每行是个元组
print(query2.limit(1).all()) # 最多返回 1 条记录
print(query2.offset(1).all()) # 从第 2 条记录开始返回
print(query2.order_by(User.name).all())
print(query2.order_by('name').all())
print(query2.order_by(User.name.desc()).all())
print(query2.order_by('name desc').all())
print(session.query(User.id).order_by(User.name.desc(), User.id).all())
print(query2.filter(User.id == 1).scalar()) # 如果有记录，返回第一条记录的第一个元素
print(session.query('id').select_from(User).filter('id = 1').scalar())
print(query2.filter(User.id > 1, User.name != 'a').scalar()) # and
query3 = query2.filter(User.id > 1) # 多次拼接的 filter 也是 and
query3 = query3.filter(User.name != 'a')
print(query3.scalar())
print(query2.filter(or_(User.id == 1, User.id == 2)).all()) # or
print(query2.filter(User.id.in_((1, 2))).all()) # in
query4 = session.query(User.id)
print(query4.filter(User.name == None).scalar())
print(query4.filter('name is null').scalar())
print(query4.filter(not_(User.name == None)).all()) # not
print(query4.filter(User.name != None).all())
print(query4.count())
print(session.query(func.count('*')).select_from(User).scalar())
print(session.query(func.count('1')).select_from(User).scalar())
print(session.query(func.count(User.id)).scalar())
print(session.query(func.count('*')).filter(User.id > 0).scalar()) # filter() 中包含 User，因此不需要指定表
print(session.query(func.count('*')).filter(User.name == 'a').limit(1).scalar() == 1) # 可以用 limit() 限制 count() 的返回数
print(session.query(func.sum(User.id)).scalar())
print(session.query(func.now()).scalar()) # func 后可以跟任意函数名，只要该数据库支持
print(session.query(func.current_timestamp()).scalar())
print(session.query(func.md5(User.name)).filter(User.id == 1).scalar())
# 修删------
query.filter(User.id == 1).update({User.name: 'c'})
user = query.get(1)
print(user.name)
user.name = 'd'
session.flush() # 写数据库，但并不提交
print(query.get(1).name)
session.delete(user)
session.flush()
session.rollback()  # 回滚
query.filter(User.id == 1).delete()
session.commit()  #提交，保存到数据库
print query.get(1)
session.close()  # 关闭session