全球观察：Python 操作 MySQL 数据库的三个模块
2022-09-02 06:02:00来源：数据STUDIO
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
(资料图片仅供参考)
#coding:utf-8import 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=5new_id = cursor.lastrowidprint(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 Specification2.1 执行原生sql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-8from sqlalchemy import create_enginefrom sqlalchemy.ext.declarative import declarative_basefrom sqlalchemy import Column, String, Integerimport threadingengine = 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-8import datetimefrom sqlalchemy import create_enginefrom sqlalchemy.ext.declarative import declarative_basefrom sqlalchemy import Column, String, Integer, DateTime, TextBase = 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-8from sqlalchemy import create_enginefrom sqlalchemy.ext.declarative import declarative_basefrom sqlalchemy import Column,Integer,String,Text,DateTime,ForeignKey,Floatfrom sqlalchemy.orm import relationshipimport datetimeengine = 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-8from sqlalchemy import create_enginefrom sqlalchemy.ext.declarative import declarative_basefrom sqlalchemy import Column,Integer,String,Text,DateTime,ForeignKey,Floatfrom sqlalchemy.orm import relationship,sessionmakerfrom sqlalchemy.sql import textimport datetimeengine = 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 funcret = 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-8from sqlalchemy import create_enginefrom sqlalchemy.orm import sessionmakerfrom sqlalchemy.sql import text, funcfrom 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 retfor 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-8from sqlalchemy import create_enginefrom sqlalchemy.orm import sessionmakerfrom sqlalchemy_orm2 import Productfrom threading import Threadengine = 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-8from sqlalchemy import create_enginefrom sqlalchemy.orm import sessionmakerfrom sqlalchemy.sql import text, funcfrom 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-8from sqlalchemy import create_enginefrom sqlalchemy.orm import sessionmakerfrom sqlalchemy.sql import text, funcfrom 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 pymysqlimport 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 Lockimport pymysqlimport threadingconn = 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-8from DBUtils.PersistentDB import PersistentDBimport pymysqlimport threadingpool = 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 PooledDBimport pymysqlimport threadingimport timepool = 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）
