Python 操作 MySQL 数据库的三个模块
python使用MySQL主要有两个模块，pymysql（MySQLdb）和SQLAchemy。
pymysql（MySQLdb）为原生模块，直接执行sql语句，其中pymysql模块支持python 2和python3，MySQLdb只支持python2，两者使用起来几乎一样。SQLAchemy为一个ORM框架，将数据对象转换成SQL，然后使用数据API执行SQL并获取执行结果另外DBUtils模块提供了一个数据库连接池，方便多线程场景中python操作数据库。1.pymysql模块
安装：pip install pymysql
创建表格操作(注意中文格式设置)#coding:utf-8
import pymysql
#关于中文问题
#1. mysql命令行创建数据库，设置编码为gbk：create databse demo2 character set utf8;
#2. python代码中连接时设置charset="gbk"
#3. 创建表格时设置default charset=utf8
#连接数据库
conn = pymysql.connect(host="localhost", user="root", passwd="", db=learningsql, charset=utf8, port=3306) #和mysql服务端设置格式一样（还可设置为gbk, gb2312）
#创建游标
cursor = conn.cursor()
#执行sql语句
cursor.execute("""create table if not exists t_sales(
id int primary key auto_increment not null,
nickName varchar(128) not null,
color varchar(128) not null,
size varchar(128) not null,
comment text not null,
saledate varchar(128) not null)engine=InnoDB default charset=utf8;""")
# cursor.execute("""insert into t_sales(nickName,color,size,comment,saledate)
# values(%s,%s,%s,%s,%s);""" % ("zack", "黑色", "L", "大小合适", "2019-04-20"))
cursor.execute("""insert into t_sales(nickName,color,size,comment,saledate)
values(%s,%s,%s,%s,%s);""" , ("zack", "黑色", "L", "大小合适", "2019-04-20"))
#提交
conn.commit()
#关闭游标
cursor.close()
#关闭连接
conn.close()增删改查：注意execute执行sql语句参数的两种情况：
execute("insert into t_sales(nickName, size) values(%s,%s);" % ("zack","L") ) #此时的%s为字符窜拼接占位符，需要引号加%s （有sql注入风险）execute("insert into t_sales(nickName, size) values(%s,%s);" , ("zack","L") ) #此时的%s为sql语句占位符，不需要引号%s#***************************增删改查******************************************************
conn = pymysql.connect(host="localhost", user="root", passwd="", db=learningsql, charset=utf8, port=3306) #和mysql服务端设置格式一样（还可设置为gbk, gb2312）
#创建游标
cursor = conn.cursor()
insert_sql = "insert into t_sales(nickName,color,size,comment,saledate) values(%s,%s,%s,%s,%s);"
#返回受影响的行数
row1 = cursor.execute(insert_sql,("Bob", "黑色", "XL", "便宜实惠", "2019-04-20"))
update_sql = "update t_sales set color=白色 where id=%s;"
#返回受影响的行数
row2 = cursor.execute(update_sql,(1,))
select_sql = "select * from t_sales where id>%s;"
#返回受影响的行数
row3 = cursor.execute(select_sql,(1,))
delete_sql = "delete from t_sales where id=%s;"
#返回受影响的行数
row4 = cursor.execute(delete_sql,(4,))
#提交，不然无法保存新建或者修改的数据(增删改得提交)
conn.commit()
cursor.close()
conn.close()批量插入和自增id#***************************批量插入******************************************************
conn = pymysql.connect(host="localhost", user="root", passwd="", db=learningsql, charset=utf8, port=3306) #和mysql服务端设置格式一样（还可设置为gbk, gb2312）
#创建游标
cursor = conn.cursor()
insert_sql = "insert into t_sales(nickName,color,size,comment,saledate) values(%s,%s,%s,%s,%s);"
data = [("Bob", "黑色", "XL", "便宜实惠", "2019-04-20"),("Ted", "黄色", "M", "便宜实惠", "2019-04-20"),("Gary", "黑色", "M", "穿着舒服", "2019-04-20")]
row1 = cursor.executemany(insert_sql, data)
conn.commit()
#为插入的第一条数据的id，即插入的为5,6,7，new_id=5
new_id = cursor.lastrowid
print(new_id)
cursor.close()
conn.close()获取查询数据#***************************获取查找sql的查询数据******************************************************
conn = pymysql.connect(host="localhost", user="root", passwd="", db=learningsql, charset=utf8, port=3306) #和mysql服务端设置格式一样（还可设置为gbk, gb2312）
#创建游标
cursor = conn.cursor()
select_sql = "select id,nickname,size from t_sales where id>%s;"
cursor.execute(select_sql, (3,))
row1 = cursor.fetchone() #获取第一条数据，获取后游标会向下移动一行
row_n = cursor.fetchmany(3) #获取前n条数据，获取后游标会向下移动n行
row_all = cursor.fetchall() #获取所有数据，获取后游标会向下移动到末尾
print(row1)
print(row_n)
print(row_all)
#conn.commit()
cursor.close()
conn.close()注：在fetch数据时按照顺序进行，可以使用cursor.scroll(num,mode)来移动游标位置，如：
cursor.scroll(1,mode=relative) # 相对当前位置移动cursor.scroll(2,mode=absolute) # 相对绝对位置移动fetch获取数据类型fetch获取的数据默认为元组格式，还可以获取字典类型的，如下：
#***************************获取字典格式数据******************************************************
conn = pymysql.connect(host="localhost", user="root", passwd="", db=learningsql, charset=utf8, port=3306) #和mysql服务端设置格式一样（还可设置为gbk, gb2312）
#创建游标
cursor = conn.cursor(cursor=pymysql.cursors.DictCursor)
select_sql = "select id,nickname,size from t_sales where id>%s;"
cursor.execute(select_sql, (3,))
row1 = cursor.fetchall()
print(row1)
conn.commit()
cursor.close()
conn.close()2.SQLAlchmy框架
SQLAlchemy的整体架构如下，建立在第三方的DB API上，将类和对象操作转换为数据库sql，然后利用DB API执sql语句得到结果。其适用于多种数据库。另外其内部实现了数据库连接池，方便进行多线程操作。
Engine，框架的引擎Connection Pooling ，数据库连接池Dialect，选择连接数据库的DB API种类，（pymysql，mysqldb等）``Schema/Types，架构和类型SQL Exprression Language，SQL表达式语言DB API：Python Database API Specification
安装：pip install sqlalchemy
SQLAlchmy也可以不利用ORM，使用数据库连接池，类似pymysql模块执行原生sql。
#coding:utf-8
from sqlalchemy import create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, String, Integer
import threading
engine = create_engine(
"mysql+pymysql://root@127.0.0.1:3306/learningsql?charset=utf8",
max_overflow = 0, #超过连接池大小外最多创建的连接，为0表示超过5个连接后，其他连接请求会阻塞 （默认为10）
pool_size = 5, #连接池大小（默认为5）
pool_timeout = 30, #连接线程池中，没有连接时最多等待的时间，不设置无连接时直接报错 （默认为30）
pool_recycle = -1) #多久之后对线程池中的线程进行一次连接的回收（重置） （默认为-1）
# def task():
# conn= engine.raw_connection() #建立原生连接,和pymysql的连接一样
# cur = conn.cursor()
# cur.execute("select * from t_sales where id>%s",(2,))
# result = cur.fetchone()
# cur.close()
# conn.close()
# print(result)
# def task():
# conn = engine.contextual_connect() #建立上下文管理器连接，自动打开和关闭
# with conn:
# cur = conn.execute("select * from t_sales where id>%s",(2,))
# result = cur.fetchone()
# print(result)
def task():
cur =engine.execute("select * from t_sales where id>%s",(2,)) #engine直接执行
result = cur.fetchone()
cur.close()
print(result)
if __name__=="__main__":
for i in range(10):
t = threading.Thread(target=task)
t.start()2.2 执行ORM语句A. 创建和删除表#coding:utf-8
import datetime
from sqlalchemy import create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, String, Integer, DateTime, Text
Base = declarative_base()
class User(Base):
__tablename__="users"
id = Column(Integer,primary_key=True)
name = Column(String(32),index=True, nullable=False) #创建索引，不为空
email = Column(String(32),unique=True)
ctime = Column(DateTime, default = datetime.datetime.now) #传入方法名datetime.datetime.now
extra = Column(Text,nullable=True)
__table_args__ = {
# UniqueConstraint(id, name, name=uix_id_name), #设置联合唯一约束
# Index(ix_id_name, name, email), # 创建索引
}
def create_tbs():
engine = create_engine("mysql+pymysql://root@127.0.0.1:3306/learningsql?charset=utf8",max_overflow=2,pool_size=5)
Base.metadata.create_all(engine) #创建所有定义的表
def drop_dbs():
engine = create_engine("mysql+pymysql://root@127.0.0.1:3306/learningsql?charset=utf8",max_overflow=2,pool_size=5)
Base.metadata.drop_all(engine) #删除所有创建的表
if __name__=="__main__":
create_tbs() #创建表
#drop_dbs() #删除表B.表中定义外键关系（一对多，多对多）思考：下面代码中的一对多关系，relationship 定义在了 customer 表中，应该定义在 PurchaseOrder 更合理？
注意：mysql 数据库中避免使用 order做为表的名字，order 为一个 mysql 关键字，做为表名字时必须用反引号order (键盘数字1旁边的符号)。
#coding:utf-8
from sqlalchemy import create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column,Integer,String,Text,DateTime,ForeignKey,Float
from sqlalchemy.orm import relationship
import datetime
engine = create_engine("mysql+pymysql://root@127.0.0.1:3306/learningsql?charset=utf8") #数据库有密码时，//root:12345678@127.0.0.1:3306/
Base = declarative_base()
class Customer(Base):
__tablename__="customer" #数据库中保存的表名字
id = Column(Integer,primary_key=True)
name = Column(String(64),index=True,nullable=False)
phone = Column(String(16),nullable=False)
address = Column(String(256),nullable=False)
purchase_order_id = Column(Integer,ForeignKey("purchase_order.id")) #关键关系，关联表的__tablename__="purchase_order"
# 和建立表结构无关，方便外键关系查询，backref反向查询时使用order_obj.customer
purchase_order = relationship("PurchaseOrder",backref="customer")
class PurchaseOrder(Base):
__tablename__ = "purchase_order" #mysql数据库中表的名字避免使用order，order为一个关键字，使用时必须用反引号`order` (键盘数字1旁边的符号)
id=Column(Integer,primary_key=True)
cost = Column(Float,nullable=True)
ctime = Column(DateTime,default =datetime.datetime.now)
desc = Column(String(528))
#多对多关系时，secondary为中间表
product = relationship("Product",secondary="order_to_product",backref="purchase_order")
class Product(Base):
__tablename__ = "product"
id = Column(Integer,primary_key=True)
name = Column(String(256))
price = Column(Float,nullable=False)
class OrdertoProduct(Base):
__tablename__ = "order_to_product"
id = Column(Integer,primary_key=True)
product_id = Column(Integer,ForeignKey("product.id"))
purchase_order_id = Column(Integer,ForeignKey("purchase_order.id"))
if __name__ == "__main__":
Base.metadata.create_all(engine)
#Base.metadata.drop_all(engine)C.增删改查操作增删改查
#coding:utf-8
from sqlalchemy import create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column,Integer,String,Text,DateTime,ForeignKey,Float
from sqlalchemy.orm import relationship,sessionmaker
from sqlalchemy.sql import text
import datetime
engine = create_engine("mysql+pymysql://root@127.0.0.1:3306/learningsql?charset=utf8") #数据库有密码时，//root:12345678@127.0.0.1:3306/, 设置utf8防止中文乱码
Base = declarative_base()
class Customer(Base):
__tablename__="customer" #数据库中保存的表名字
id = Column(Integer,primary_key=True)
name = Column(String(64),index=True,nullable=False)
phone = Column(String(16),nullable=False)
address = Column(String(256),nullable=False)
purchase_order_id = Column(Integer,ForeignKey("purchase_order.id")) #关键关系，关联表的__tablename__="purchase_order"
# 和建立表结构无关，方便外键关系查询，backref反向查询时使用order_obj.customer
purchase_order = relationship("PurchaseOrder",backref="customer")
class PurchaseOrder(Base):
__tablename__ = "purchase_order" #mysql数据库中表的名字避免使用order，order为一个关键字，使用时必须用反引号`order` (键盘数字1旁边的符号)
id=Column(Integer,primary_key=True)
cost = Column(Float,nullable=True)
ctime = Column(DateTime,default =datetime.datetime.now)
desc = Column(String(528))
#多对多关系时，secondary为中间表
product = relationship("Product",secondary="order_to_product",backref="purchase_order")
class Product(Base):
__tablename__ = "product"
id = Column(Integer,primary_key=True)
name = Column(String(256))
price = Column(Float,nullable=False)
class OrdertoProduct(Base):
__tablename__ = "order_to_product"
id = Column(Integer,primary_key=True)
product_id = Column(Integer,ForeignKey("product.id"))
purchase_order_id = Column(Integer,ForeignKey("purchase_order.id"))
if __name__ == "__main__":
#Base.metadata.create_all(engine)
#Base.metadata.drop_all(engine)
Session = sessionmaker(bind=engine)
#每次进行数据库操作时都要创建session
session = Session()
#*****************增加数据********************
# pur_order = PurchaseOrder(cost=19.7,desc="python编程之路")
# session.add(pur_order)
# session.add_all(
# [PurchaseOrder(cost=39.7,desc="linux操作系统"),
# PurchaseOrder(cost=59.6,desc="python cookbook")])
# session.commit()
#*****************修改数据********************
#session.query(PurchaseOrder).filter(PurchaseOrder.id>2).update({"cost":29.7})
#session.query(PurchaseOrder).filter(PurchaseOrder.id==2).update({"cost":PurchaseOrder.cost+40.1},synchronize_session=False) #synchronize_session用于query在进行delete or update操作时，对session的同步策略。
#session.commit()
#*****************删除数据********************
#session.query(PurchaseOrder).filter(PurchaseOrder.id==1).delete()
#session.commit()
#*****************查询数据********************
#ret = session.query(PurchaseOrder).all()
# ret = session.query(PurchaseOrder).filter(PurchaseOrder.id==2).all() #包含对象的列表
# ret = session.query(PurchaseOrder).filter(PurchaseOrder.id==2).first() #单个对象
# ret = session.query(PurchaseOrder).filter_by(id=2).all() #通过列名字的表达式
# ret = session.query(PurchaseOrder).filter_by(id=2).first()
#ret = session.query(PurchaseOrder).filter(text("id<:value and cost>:price")).params(value=6,price=15).order_by(PurchaseOrder.cost).all()
#ret = session.query(PurchaseOrder).from_statement(text("SELECT * FROM purchase_order WHERE cost>:price")).params(price=40).all()
# print ret
# for i in ret:
# print i.id, i.cost, i.ctime,i.desc
#ret2 = session.query(PurchaseOrder.id,PurchaseOrder.cost.label(totalcost)).all() #只查询两列，ret2为列表
#print ret2
#关闭session
session.close()查询语句
# 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()补充
#coding:utf-8
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from sqlalchemy.sql import text, func
from sqlalchemy_orm2 import PurchaseOrder #导入定义的PurchaseOrder表格类
engine = create_engine("mysql+pymysql://root@127.0.0.1:3306/learningsql?charset=utf8")
Session = sessionmaker(bind=engine)
session = Session()
#查询
ret = session.execute("select * from purchase_order where id=:value",params={"value":3})
print ret
for i in ret:
print i.id, i.cost, i.ctime,i.desc
#插入
purchase_order = PurchaseOrder.__table__ #拿到PurchaseOrder表格对象
ret=session.execute(purchase_order.insert(),
[{"cost":46.3,"desc":python2},
{"cost":43.3,"desc":python3}])
session.commit()
print(ret.lastrowid)
session.close()
# 关联子查询
subqry = session.query(func.count(Server.id).label("sid")).filter(Server.id == Group.id).correlate(Group).as_scalar()
result = session.query(Group.name, subqry)
"""
SELECT `group`.name AS group_name, (SELECT count(server.id) AS sid
FROM server
WHERE server.id = `group`.id) AS anon_1
FROM `group`
"""D.多线程操作#coding:utf-8
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from sqlalchemy_orm2 import Product
from threading import Thread
engine = create_engine("mysql+pymysql://root@127.0.0.1:3306/learningsql?charset=utf8",max_overflow=0,pool_size=5)
Session = sessionmaker(bind=engine)
def task(name,price):
session = Session()
pro = Product(name=name,price=price)
session.add(pro)
session.commit()
session.close()
if __name__=="__main__":
for i in range(6):
t = Thread(target=task,args=("pro"+str(i),i*5))
t.start()E. 通过relationship操纵一对多和多对多关系一对多
#coding:utf-8
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from sqlalchemy.sql import text, func
from sqlalchemy_orm2 import PurchaseOrder,Product,OrdertoProduct,Customer #导入定义的表格类
engine = create_engine("mysql+pymysql://root@127.0.0.1:3306/learningsql?charset=utf8")
Session = sessionmaker(bind=engine)
session = Session()
# #通过定义的关键关系添加(id值)
# cus1 = Customer(name="zack",phone="13567682333",address="Nanjing",purchase_order_id=3)
# session.add(cus1)
# #通过relationship正向添加
# cus2 = Customer(name="zack2",phone="13567682333",address="Nanjing",purchase_order=PurchaseOrder(cost=53,desc="java"))
# session.add(cus2)
# session.commit()
#通过relationship反向添加
# purchase_order=PurchaseOrder(cost=53,desc="php")
# cus3 = Customer(name="zack3",phone="13567682333",address="Nanjing")
# cus4 = Customer(name="zack4",phone="13567682333",address="Nanjing")
# purchase_order.customer=[cus3,cus4] #cus3,cus4的purchase_order_id都是purchase_order.id值,即同时添加了两组外键关系
# session.add(purchase_order)
# session.commit()
##通过relationship正向查询
cus = session.query(Customer).first()
print(cus.purchase_order_id)
print(cus.purchase_order.desc)
#通过relationship反向查询
pur = session.query(PurchaseOrder).filter(PurchaseOrder.id==3).first()
print(pur.desc)
print(pur.customer) #返回一个list多对多
#coding:utf-8
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from sqlalchemy.sql import text, func
from sqlalchemy_orm2 import PurchaseOrder,Product,OrdertoProduct,Customer #导入定义的表格类
engine = create_engine("mysql+pymysql://root@127.0.0.1:3306/learningsql?charset=utf8")
Session = sessionmaker(bind=engine)
session = Session()
# session.add_all([Product(name="java",price=24),
# Product(name="python",price=34),
# Product(name="php",price=27)])
# session.commit()
# #通过定义的关键关系添加(id值)
# op = OrdertoProduct(product_id=1,purchase_order_id=16)
# session.add(op)
# session.commit()
# #通过relationship添加
# pur = PurchaseOrder(cost=27,desc="xxxx")
# pur.product = [Product(name="C++",price=60),] #正向
# session.add(pur)
# pro = Product(name="C",price=40)
# pro.purchase_order=[PurchaseOrder(cost=27,desc="xxxx"),] #反向
# session.add(pro)
# session.commit()
#通过relationship正向查询
pur = session.query(PurchaseOrder).filter(PurchaseOrder.id==19).first()
print(pur.desc)
print(pur.product) #结果为列表
#通过relationship反向查询
pro = session.query(Product).filter(Product.id==5).first()
print(pro.name)
print(pro.purchase_order) #结果为列表
session.close()3.数据库连接池
对于ORM框架，其内部维护了链接池，可以直接通过多线程操控数据库。对于pymysql模块，通过多线程操控数据库容易出错，得加锁串行执行。进行并发时，可以利用DBUtils模块来维护数据库连接池。
3.1 多线程操控pymysql不采用DBUtils连接池时, pymysql多线程代码如下：
每个线程创建链接
import pymysql
import threadind
#**************************无连接池*******************************
#每个线程都要创立一次连接，线程并发操作间可能有问题？
def func():
conn = pymysql.connect(host="127.0.0.1",port=3306,db="learningsql",user="root",passwd="",charset="utf8")
cursor = conn.cursor()
cursor.execute("select * from user where nid>1;")
result = cursor.fetchone()
print(result)
cursor.close()
conn.close()
if __name__=="__main__":
for i in range(5):
t = threading.Thread(target=func,name="thread-%s"%i)
t.start()一个连接串行执行
#**************************无连接池*******************************
#创建一个连接，加锁串行执行
from threading import Lock
import pymysql
import threading
conn = pymysql.connect(host="127.0.0.1",port=3306,db="learningsql",user="root",passwd="",charset="utf8")
lock = Lock()
def func():
with lock:
cursor = conn.cursor()
cursor.execute("select * from user where nid>1;")
result = cursor.fetchone()
print(result)
cursor.close()
#conn.close()不能在线程中关闭连接，否则其他线程不可用了
if __name__=="__main__":
threads = []
for i in range(5):
t = threading.Thread(target=func,name="thread-%s"%i)
threads.append(t)
t.start()
for t in threads:
t.join()
conn.close()3.2 DBUtils连接池DBUtils连接池有两种连接模式：PersistentDB和PooledDB
官网文档：https://cito.github.io/DBUtils/UsersGuide.html
模式一(DBUtils.PersistentDB):为每个线程创建一个连接，线程即使调用了close方法，也不会关闭，只是把连接重新放到连接池，供自己线程再次使用。当线程终止时，连接自动关闭。
PersistentDB使用代码如下：
#coding:utf-8
from DBUtils.PersistentDB import PersistentDB
import pymysql
import threading
pool = PersistentDB(
creator = pymysql, # 使用链接数据库的模块
maxusage = None, # 一个链接最多被重复使用的次数，None表示无限制
setsession=[], # 开始会话前执行的命令列表。如：["set datestyle to ...", "set time zone ..."]
ping = 0, # ping MySQL服务端，检查是否服务可用。# 如：0 = None = never, 1 = default = whenever it is requested, 2 = when a cursor is created, 4 = when a query is executed, 7 = always
closeable = False, # 如果为False时， conn.close() 实际上被忽略，供下次使用，再线程关闭时，才会自动关闭链接。如果为True时， conn.close()则关闭链接，那么再次调用pool.connection时就会报错，因为已经真的关闭了连接（pool.steady_connection()可以获取一个新的链接）
threadlocal = None, # 本线程独享值得对象，用于保存链接对象，如果链接对象被重置
host="127.0.0.1",
port = 3306,
user = "root",
password="",
database="learningsql",
charset = "utf8"
)
def func():
conn = pool.connection()
cursor = conn.cursor()
cursor.execute("select * from user where nid>1;")
result = cursor.fetchone()
print(result)
cursor.close()
conn.close()
if __name__ == "__main__":
for i in range(5):
t = threading.Thread(target=func,name="thread-%s"%i)
t.start()模式二(DBUtils.PooledDB)：创建一批连接到连接池，供所有线程共享使用。
(由于pymysql、MySQLdb等threadsafety值为1，所以该模式连接池中的线程会被所有线程共享。)
PooledDB使用代码如下：
from DBUtils.PooledDB import PooledDB
import pymysql
import threading
import time
pool = PooledDB(
creator = pymysql, # 使用链接数据库的模块
maxconnections = 6, # 连接池允许的最大连接数，0和None表示不限制连接数
mincached = 2, # 初始化时，链接池中至少创建的空闲的链接，0表示不创建
maxcached = 5, # 链接池中最多闲置的链接，0和None不限制
maxshared = 3, # 链接池中最多共享的链接数量，0和None表示全部共享。PS: 无用，因为pymysql和MySQLdb等模块的 threadsafety都为1，所有值无论设置为多少，_maxcached永远为0，所以永远是所有链接都共享。
blocking = True, # 连接池中如果没有可用连接后，是否阻塞等待。True，等待；False，不等待然后报错
maxusage = None, # 一个链接最多被重复使用的次数，None表示无限制
setsession = [], # 开始会话前执行的命令列表。如：["set datestyle to ...", "set time zone ..."]
ping = 0, # ping MySQL服务端，检查是否服务可用。# 如：0 = None = never, 1 = default = whenever it is requested, 2 = when a cursor is created, 4 = when a query is executed, 7 = always
host="127.0.0.1",
port = 3306,
user="root",
password="",
database = "learningsql",
charset = "utf8"
)
def func():
conn = pool.connection()
cursor = conn.cursor()
cursor.execute("select * from user where nid>1;")
result = cursor.fetchone()
print(result)
time.sleep(5) #为了查看mysql端的线程数量
cursor.close()
conn.close()
if __name__=="__main__":
for i in range(5):
t = threading.Thread(target=func,name="thread-%s"%i)
t.start()上述代码中加入了sleep(5)使线程连接数据库时间延长，方便查看mysql数据库连接线程情况，下图分别为代码执行中和执行后的线程连接情况，可以发现，代码执行时，同时有6个线程连接上了数据库（有一个为mysql命令客户端），代码执行后，只有一个线程连接数据库，但仍有5个线程等待连接。
（show status like "Threads%" 查看线程连接情况）
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