本文实例讲述了Python使用sqlalchemy模块连接数据库操作。分享给大家供大家参考,具体如下:

安装:

pip install sqlalchemy
# 安装数据库驱动:
pip install pymysql
pip install cx_oracle

举例:(在url后面加入?charset=utf8可以防止乱码)

from sqlalchemy import create_engine
engine=create_engine('mysql+pymysql://username:password@hostname:port/dbname', echo=True) #echo=True 打印sql语句信息

create_engine接受一个url,格式为:

# '数据库类型+数据库驱动名称://用户名:口令@机器地址:端口号/数据库名'
# 常用的
engine = create_engine('sqlite:///:memory:', echo=True)   # sqlite内存
engine = create_engine('sqlite:///./cnblogblog.db',echo=True) # sqlite文件
engine = create_engine("mysql+pymysql://username:password@hostname:port/dbname",echo=True) # mysql+pymysql
engine = create_engine('mssql+pymssql://username:password@hostname:port/dbname',echo=True) # mssql+pymssql
engine = create_engine('postgresql://scott:tiger@hostname:5432/dbname') # postgresql示例
engine = create_engine('oracle://scott:tiger@hostname:1521/sidname') # oracle
engine = create_engine('oracle+cx_oracle://scott:tiger@tnsname') #pdb就可以用tns连接

简单demo:

from sqlalchemy import create_engine, Column, Integer, String
from sqlalchemy.orm import sessionmaker
from sqlalchemy.ext.declarative import declarative_base
engine = create_engine('oracle://spark:a@orclpdb',echo=True) #echo要求打印sql语句等调试信息
session_maker = sessionmaker(bind=engine)
session = session_maker()
Base = declarative_base()
#对应一张表
class Student(Base):
__tablename__ = 'STUDENT'
id = Column('STUID', Integer, primary_key=True)
name = Column('STUNAME', String(32), nullable=False)
age = Column('STUAGE', Integer)
def __repr__(self):
  return '<Student(id:%s, name:%s, age:%s)>' % (self.id, self.name, self.age)
Base.metadata.create_all(engine) #若存在STUDENT表则不做,不存在则创建。
queryObject = session.query(Student).order_by(Student.id.desc())
for ins in queryObject:
print(ins.id, ins.name, ins.age)
'''
4 hey 24
3 lwtxxs 27
2 gyb 89
1 ns 23
'''

将查询结果映射为DataFrame:

import pandas as pd
df = pd.read_sql(session.query(Student).filter(Student.id > 1).statement, engine)
print(df)
'''
STUID STUNAME STUAGE
0   4   hey   24
1   2   gyb   89
2   3 lwtxxs   27
'''

查询:

session的query方法除了可以接受Base子类对象作为参数外,还可以是字段,如:

query = session.query(Student.name, Student.age) # query为一个sqlalchemy.orm.query.Query对象
for stu_name, stu_age in query:
print(stu_name, stu_age)

查询条件filter:

# = / like
query.filter(Student.name == 'wendy')
query.filter(Student.name.like('%ed%'))
# in
query.filter(Student.name.in_(['wendy', 'jack']))
query.filter(Student.name.in_(
  session.query(User.name).filter(User.name.like('%ed%'))
))
# not in
query.filter(~Student.name.in_(['ed', 'wendy', 'jack']))
# is null / is not null
query.filter(Student.name == None)
query.filter(Student.name.is_(None))
query.filter(Student.name != None)
query.filter(Student.name.isnot(None))
# and
from sqlalchemy import and_, or_
query.filter(and_(Student.name == 'ed', Student.age != 23))
query.filter(Student.name == 'ed', Student.age != 23)
query.filter(Student.name == 'ed').filter(Student.age != 23)
# or
query.filter(or_(Student.name == 'ed', Student.name == 'wendy'))
# match
query.filter(Student.name.match('wendy'))

Query的方法:

all()方法以列表形式返回结果集:

from sqlalchemy import or_, and_
queryObject = session.query(Student).filter(or_(Student.id == 1, Student.id == 2))
print(queryObject.all())  # [<Student(id:1, name:ns, age:23)>, <Student(id:2, name:gyb, age:89)>]
queryObject = session.query(Student.name).filter(or_(Student.id == 1, Student.id == 2))
print(queryObject.all())  # [('ns',), ('gyb',)]

first()方法返回单个结果。(若结果集为空则返回None)

print``(queryObject.first())  ``# ('ns',)

one()方法返回单个结果,与first()方法不同的是:当结果集中没有元素或有多于一个元素会抛出异常。 one_or_none()方法同one()一样,不同是结果集为空则返回None,为多个抛出异常。

查询数量:

from sqlalchemy import func
session.query(func.count(Student.id)).scalar() # SELECT count("STUDENT"."STUID") AS count_1 FROM "STUDENT"

分组:

session.query(func.count(Student.id), Student.name).group_by(Student.name).all()

嵌套SQL语句:

from sqlalchemy import text
query = session.query(Student.id, Student.name).filter(text('stuid>2'))
query = session.query('stuid', 'stuname', 'stuage').from_statement(\
text("select * from student where stuname=:stuname")).params(stuname='hey').all()  #[(4, 'hey', 24)]

 

 

 

 

 

 

 

 

 

 

 

 

sqlalchemy.exc.DatabaseError: (cx_Oracle.DatabaseError) ORA-12505: TNS: 监听程序当前无法识别连接描述符中所给出的 SID 主要原因可能是目标数据库是集群部署,可以咨询一下DBA python 用sqlalchemy 连接Oracle数据库的时候报了下面这个错误:

sqlalchemy.exc.DatabaseError: (cx_Oracle.DatabaseError) ORA-12505: TNS: 监听程序当前无法识别连接描述符中所给出的 SID
(Background on this error at: http://sqlalche.me/e/4xp6)

这是因为 sqlalchemy 在create_engine的时候默认是调用cx_Oracle去连接数据库,而cx_Oracle 在创建dns连接字符串的时候是默认SID = tnsname (实例名),其实是在连接的时候调用了 cx_Oracle.makedns 来构造连接url,我们通过下面的例子来看

In[95]: import cx_Oracle
In[96]: cx_Oracle.makedsn('10.24.04.19', '1314', 'report')
Out[96]: '(DESCRIPTION=(ADDRESS=(PROTOCOL=TCP)(HOST=10.24.04.19)(PORT=1314))(CONNECT_DATA=(SID=report)))'
In[97]: cx_Oracle.makedsn('10.24.04.19', '1314', service_name='report')
Out[97]: '(DESCRIPTION=(ADDRESS=(PROTOCOL=TCP)(HOST=10.24.04.19)(PORT=1314))(CONNECT_DATA=(SERVICE_NAME=report)))'

因为cx_Oracle不会去读我们配置的tnsname.ora文件,而是通过传进去的参数来构造连接url 所以如果不指定service_name,那么这个函数就会默认将 ‘report’ 视为 SID (positional args),这样做的话对于单机部署的 Oracle数据库是没有问题的,但是如果目标数据库是集群部署的话,就会出现ORA-12505: TNS: 监听程序当前无法识别连接描述符中所给出的 SID 的情况。 更深入的解释可以看下面 由于oracle 是做的多节点,然后有一个公用的service_name,只有通过service_name去连接才能起到负载均衡的作用,而以cx_Oracle默认的连接串去连的话只能连接到实例名,而不能连接到service_name,所以oracle用service_name去匹配实例名,当然找不到。所以连接时必须指定连的是service_name而不是sid. 所以我们需要修改连接字符串 SQLAlchemy 连接方式

import cx_Oracle
from sqlalchemy import create_engine
ip = '10.24.04.19'
port = '1314'
uname = 'jiajia' # 用户名
pwd = 'yupeng' # 密码
tnsname = 'report' # 实例名

dsnStr = cx_Oracle.makedsn(ip, port, service_name=tnsname)
connect_str = "oracle://%s:%s@%s" %(uname, pwd, dsnStr)
engine = create_engine(connect_str, encoding=encoding)

cx_Oracle连接方法

conn = cx_Oracle.connect(uname, pwd, dsn=dsnStr)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

本文实例讲述了Python使用sqlalchemy模块连接数据库操作。分享给大家供大家参考,具体如下:

安装:

pip install sqlalchemy
# 安装数据库驱动:
pip install pymysql
pip install cx_oracle

举例:(在url后面加入?charset=utf8可以防止乱码)

from sqlalchemy import create_engine
engine=create_engine('mysql+pymysql://username:password@hostname:port/dbname', echo=True) #echo=True 打印sql语句信息

create_engine接受一个url,格式为:

# '数据库类型+数据库驱动名称://用户名:口令@机器地址:端口号/数据库名'
# 常用的
engine = create_engine('sqlite:///:memory:', echo=True)   # sqlite内存
engine = create_engine('sqlite:///./cnblogblog.db',echo=True) # sqlite文件
engine = create_engine("mysql+pymysql://username:password@hostname:port/dbname",echo=True) # mysql+pymysql
engine = create_engine('mssql+pymssql://username:password@hostname:port/dbname',echo=True) # mssql+pymssql
engine = create_engine('postgresql://scott:tiger@hostname:5432/dbname') # postgresql示例
engine = create_engine('oracle://scott:tiger@hostname:1521/sidname') # oracle
engine = create_engine('oracle+cx_oracle://scott:tiger@tnsname') #pdb就可以用tns连接

简单demo:

from sqlalchemy import create_engine, Column, Integer, String
from sqlalchemy.orm import sessionmaker
from sqlalchemy.ext.declarative import declarative_base 
engine = create_engine('oracle://spark:a@orclpdb',echo=True) #echo要求打印sql语句等调试信息
session_maker = sessionmaker(bind=engine)
session = session_maker()
Base = declarative_base()
#对应一张表
class Student(Base): 
  __tablename__ = 'STUDENT'
  id = Column('STUID', Integer, primary_key=True)
  name = Column('STUNAME', String(32), nullable=False)
  age = Column('STUAGE', Integer)
  def __repr__(self):
    return '<Student(id:%s, name:%s, age:%s)>' % (self.id, self.name, self.age)
Base.metadata.create_all(engine) #若存在STUDENT表则不做,不存在则创建。
queryObject = session.query(Student).order_by(Student.id.desc())
for ins in queryObject:
  print(ins.id, ins.name, ins.age)
'''
4 hey 24
3 lwtxxs 27
2 gyb 89
1 ns 23
'''

将查询结果映射为DataFrame:

import pandas as pd
df = pd.read_sql(session.query(Student).filter(Student.id > 1).statement, engine) 
print(df)
'''
  STUID STUNAME STUAGE
0   4   hey   24
1   2   gyb   89
2   3 lwtxxs   27
'''

查询:

session的query方法除了可以接受Base子类对象作为参数外,还可以是字段,如:

query = session.query(Student.name, Student.age) # query为一个sqlalchemy.orm.query.Query对象
for stu_name, stu_age in query:
  print(stu_name, stu_age)

查询条件filter:

# = / like
query.filter(Student.name == 'wendy')
query.filter(Student.name.like('%ed%'))
# in
query.filter(Student.name.in_(['wendy', 'jack']))
query.filter(Student.name.in_(
    session.query(User.name).filter(User.name.like('%ed%'))
))
# not in
query.filter(~Student.name.in_(['ed', 'wendy', 'jack']))
# is null / is not null
query.filter(Student.name == None)
query.filter(Student.name.is_(None))
query.filter(Student.name != None)
query.filter(Student.name.isnot(None))
# and
from sqlalchemy import and_, or_
query.filter(and_(Student.name == 'ed', Student.age != 23))
query.filter(Student.name == 'ed', Student.age != 23)
query.filter(Student.name == 'ed').filter(Student.age != 23)
# or
query.filter(or_(Student.name == 'ed', Student.name == 'wendy'))
# match
query.filter(Student.name.match('wendy'))

Query的方法:

all()方法以列表形式返回结果集:

from sqlalchemy import or_, and_
queryObject = session.query(Student).filter(or_(Student.id == 1, Student.id == 2))
print(queryObject.all())  # [<Student(id:1, name:ns, age:23)>, <Student(id:2, name:gyb, age:89)>]
queryObject = session.query(Student.name).filter(or_(Student.id == 1, Student.id == 2))
print(queryObject.all())  # [('ns',), ('gyb',)]

first()方法返回单个结果。(若结果集为空则返回None)

print(queryObject.first())  # ('ns',)

one()方法返回单个结果,与first()方法不同的是:当结果集中没有元素或有多于一个元素会抛出异常。 one_or_none()方法同one()一样,不同是结果集为空则返回None,为多个抛出异常。

查询数量:

from sqlalchemy import func
session.query(func.count(Student.id)).scalar() # SELECT count("STUDENT"."STUID") AS count_1 FROM "STUDENT"

分组:

session.query(func.count(Student.id), Student.name).group_by(Student.name).all()

嵌套SQL语句:

from sqlalchemy import text
query = session.query(Student.id, Student.name).filter(text('stuid>2'))
query = session.query('stuid', 'stuname', 'stuage').from_statement(\
text("select * from student where stuname=:stuname")).params(stuname='hey').all()  #[(4, 'hey', 24)]

 

 

 

 

 

 

 

 

 

 

 

 

 

基本查询结果

# 1 查看sql原生语句
rs =session.query(User).filter(User.username=='budong')
print(rs)
# 2 query(module) .all()
rs =session.query(User).filter(User.username=='budong').all()  #  .all list
print(rs, type(rs[0]))          # 索引取值 当query(module) 类型为User类的实例对象
print(rs[0].username,rs[0].id)       # rs[0]这个实例对象通过.username,.id取得值

 

# 3 hasattr() getattr()
# 没有这条数据则会报错超出索引 先判断是否存在hasattr() ,再取值getattr()数据不存在报错
print(hasattr(rs[0], 'username'))   # 判断是否有这个username属性  返回值True False
if hasattr(rs[0], 'username'):
    print(getattr(rs[0],'username'))  # 安全取值
    print(rs[0].username)     

>>> True
>>> budong
>>> budong

 

 

# 4 .first()  [0]
rs =session.query(User).filter(User.username=='budong').first() # 返回一条数据,无则返回none
rs1 =session.query(User).filter(User.username=='budong')[0] # 取第一条数据无则报错
print(rs, rs1,sep='\n')
print(rs.id, rs1.username)   # 取出值
if rs != None:
    print(rs)
>>> <User(id=1,username=budong,password=qwe123,createtime=2018-03-07 16:57:09)>    <User(id=1,username=budong,password=qwe123,createtime=2018-03-07 16:57:09)>
>>> 1 budong
>>> <User(id=1,username=budong,password=qwe123,createtime=2018-03-07 16:57:09)>

 

 

# 5 query(module的属性)
rs =session.query(User.id).filter(User.username=='tj').all() # all返回list
print(rs)     # list
print(rs[0])   # 当query(module的属性)   返回结果为元祖
print(rs[0][0])

>>> [(2,)]
>>> (2,)
>>> 2

 

 

# 6 条件查询
# filter_by(直接跟module的属性,以字典形式传参)  并且只能判断 =
rs = session.query(User).filter_by(username='budong').all()
print(rs)
# filter(module.属性)  能判断 ==  != >=  常用
rs = session.query(User).filter(User.username=='budong').all()
print(rs)

>>> [<User(id=1,username=budong,password=qwe123,createtime=2018-03-07 16:57:09)>, <User(id=5,username=budong,password=qweq,createtime=2018-03-08 05:10:38)>]
>>> [<User(id=1,username=budong,password=qwe123,createtime=2018-03-07 16:57:09)>, <User(id=5,username=budong,password=qweq,createtime=2018-03-08 05:10:38)>]

 

模糊查询

 

# 7 模糊查询
    # like_  notlike
rs = session.query(User).filter(User.username.like('%don%')).all()
print(rs)
rs = session.query(User).filter(User.username.notlike('%don%')).all() # 相反
print(rs)
>>> [<User(id=1,username=budong,password=qwe123,createtime=2018-03-07 16:57:09)>, <User(id=5,username=budong,password=qweq,createtime=2018-03-08 05:10:38)>]
>>> [<User(id=2,username=tj,password=123qwe1,createtime=2018-03-07 16:54:09)>, <User(id=3,username=tj1,password=123qwe2,createtime=2018-03-07 16:58:09)>,    <User(id=4,username=tj2,password=123qwe3,createtime=2018-03-07 16:07:09)>]

 

 

# 8 in_ notin                       满足一个条件即可rs = session.query(User).filter(User.username.in_(['budong','tj'])).all()
print(rs)
rs = session.query(User).filter(User.username.notin_(['budong','tj'])).all() # 相反
print(rs)

>>> [<User(id=1,username=budong,password=qwe123,createtime=2018-03-07 16:57:09)>, <User(id=2,username=tj,password=123qwe1,createtime=2018-03-07 16:54:09)>,    <User(id=5,username=budong,password=qweq,createtime=2018-03-08 05:10:38)>]
>>> [<User(id=3,username=tj1,password=123qwe2,createtime=2018-03-07 16:58:09)>, <User(id=4,username=tj2,password=123qwe3,createtime=2018-03-07 16:07:09)>]

 

 

# 9 is_  isnot         is 用来判断是否为空 是空则取值
rs = session.query(User.username).filter(User.username.is_(None)).all()
print(rs)
rs = session.query(User.username).filter(User.username.isnot(None)).all()  # 相反
print(rs)

>>> []
>>> [('budong',), ('tj',), ('tj1',), ('tj2',), ('budong',)]

 

 

# 10 limit  限制数据条数
rs =session.query(User).filter(User.username=='budong').all()
print(rs)
rs =session.query(User).filter(User.username=='budong').limit(1).all()
print(rs)

>>> [<User(id=1,username=budong,password=qwe123,createtime=2018-03-07 16:57:09)>, <User(id=5,username=budong,password=qweq,createtime=2018-03-08 05:10:38)>]
>>> [<User(id=1,username=budong,password=qwe123,createtime=2018-03-07 16:57:09)>]

 

# 11 offset(n) 不取前n条数据 称为 偏移量:偏移n条数据
rs = session.query(User).filter(User.username=='budong').offset(1).all()
print(rs)

>>> [<User(id=5,username=budong,password=qweq,createtime=2018-03-08 05:10:38)>]
# 12 slice 切片 左闭右开
rs = session.query(User).filter(User.username=='budong').slice(0,1).all()
print(rs)

>>> [<User(id=1,username=budong,password=qwe123,createtime=2018-03-07 16:57:09)>]
# 13 one 只有一条数据则取值 反之 报错
# rs = session.query(User).filter(User.username=='budong').one()  # 满足条件的超过1条,报错
rs = session.query(User).filter(User.username=='tj').one()
print(rs)
>>> <User(id=2,username=tj,password=123qwe1,createtime=2018-03-07 16:54:09)>

 

# 14 order_by(*args) 排序(按asc)
    # 升序
rs = session.query(User.id).filter(User.username=='budong').order_by(User.id).all()
print(rs)
    # 降序 需导入降序desc
from sqlalchemy import desc
rs = session.query(User.id).filter(User.username=='budong').order_by(desc(User.id)).all()
print(rs)

>>> [(1,), (5,)]
>>> [(5,), (1,)]

 

 

# 15 group_by
from sqlalchemy import func,extract
    # 按query的属性 进行分组 再统计该属性的所有值出现的次数
rs = session.query(User.username,func.count(User.id)).group_by(desc(User.username)).all()
print(rs)

>>> [('tj2', 1), ('tj1', 1), ('tj', 1), ('budong', 2)]

 

 

# 16 group_by + having(判断条件 常跟func的count sum avg 等使用) 先分组在执行having
rs = session.query(User.username,func.count(User.id)).group_by(desc(User.username)).\
    having(func.count(User.id)>1).all()
print(rs)
rs = session.query(User.username,func.max(User.id)).group_by(User.username).all()
print(rs)    # 通过username分组 多条数据的取id最大的那条
rs = session.query(User.username,func.min(User.id)).group_by(User.username).all()
print(rs)    # 通过username分组 多条数据的取id最小的那条

>>> [('budong', 2)]
>>> [('budong', 5), ('tj', 2), ('tj1', 3), ('tj2', 4)]
>>> [('budong', 1), ('tj', 2), ('tj1', 3), ('tj2', 4)]

 

 

# 17 extract 能获取某部分时间(year,month,day,hour,minute,second) 进行分组及统计
rs = session.query(extract('minute',User.creatime).label('minute'),func.count('minute')).\
    group_by('minute').all()            # label 取别名
print(rs)

>>> [(7, 1), (10, 1), (54, 1), (57, 1), (58, 1)]

 

# 18 or_ 或者 满足其中一个条件即可   类似in_ notin
rs = session.query(User.username).filter(or_(User.password=='qwe123',User.id>2)).all()
print(rs)

>>> [('budong',), ('tj1',), ('tj2',), ('budong',)]

User这个类创建的表                                User1这个类创建的表

 

 

# 19 多表查询              
# mysql中的 内链接cross join  内链接inner join  两者没区别, 内链接的结果会产生笛卡儿积 table1(的每条数据) X table2(的所有数据)
rs = session.query(User.username,User1.name).filter(User.id==User1.id).all()   # 通过,直接query两张表= select * from table1,table2 属于内链接cross join
print(rs)
rs = session.query(User.username,User1.name).join(User1,User.id==User1.id).all() # join =内链接inner join
print(rs)
# mysql中的 外链接left join  和 外链接left outer join也没区别
# 外链接outerjoin = left outer join     -- sqlalchemy  没有right outer join
rs = session.query(User.username,User1.name).outerjoin(User1,User.id==User1.id).all()
print(rs)we
# 已左表为准   两个表的数据并排显示,左表有多少条数据则显示多少,右边有多余的数据则不取,少于的数据则显示为None数据链接到左表
rs = session.query(User1.name,User.username).outerjoin(User,User.id==User1.id).all() #与上面相比交换表的位置
print(rs)

>>> [('budong', 'D'), ('tj', 'A'), ('tj1', 'B'), ('tj2', 'C')]
>>> [('budong', 'D'), ('tj', 'A'), ('tj1', 'B'), ('tj2', 'C')]
>>> [('budong', 'D'), ('tj', 'A'), ('tj1', 'B'), ('tj2', 'C'), ('budong', None)]
>>> [('D', 'budong'), ('A', 'tj'), ('B', 'tj1'), ('C', 'tj2')]

 

 

# 20 联合查询 两个表并排显示
rs1 = session.query(User1.name)
rs2 = session.query(User.username)
print(rs1.union(rs2).all())        # union 去重 
print(rs1.union_all(rs2).all())    # 显示所有包括重复的数据  'budong'为重复的数据

>>> [('D',), ('A',), ('B',), ('C',), ('budong',), ('tj',), ('tj1',), ('tj2',)]
>>> [('D',), ('A',), ('B',), ('C',), ('budong',), ('tj',), ('tj1',), ('tj2',), ('budong',)]

 

 

# 21 子表查询   cross join 产生笛卡儿积# 原生sql是  select * from table1,table2;   table2是这儿的子表
# 声明子表subquery() 子表可以是多个表取出的数据 所以比直接使用 cross join or inner join 能查更多表的相关数据
sql = session.query(User1.name).subquery()
# 父表的每一条数据都匹配子表的所有数据
print(session.query(User.username,sql.c.name).all())  # 固定写法  申明子表的sql.c.属性

>>> [('budong', 'D'), ('tj', 'D'), ('tj1', 'D'), ('tj2', 'D'), ('budong', 'D'), ('budong', 'A'), ('tj', 'A'), ('tj1', 'A'), ('tj2', 'A'), ('budong', 'A'),    ('budong', 'B'), ('tj', 'B'), ('tj1', 'B'), ('tj2', 'B'), ('budong', 'B'), ('budong', 'C'), ('tj', 'C'), ('tj1', 'C'), ('tj2', 'C'), ('budong', 'C')]

 

原生sql语句查询

 

# 原生SQL查询
sql_1='select username from `user`'
row = session.execute(sql_1)      # row =5条数据  row是一个对象 可以 for in 取值  dir(对象)
print(row.fetchone())   # 取出第一条数据  row -1 =4
print(row.fetchmany(2)) # 去出两条数据    row -2 =2
print(row.fetchall())  # 取出所有的数据  row =0

>>> ('budong',)
>>> [('tj',), ('tj1',)]
>>> [('tj2',), ('budong',)]

 

 sql是字符串 可以用到字符串拼接

 

sql = '''
    select * from user where id<%s;
''' %(3)
row = session.execute(sql)
for i in row:
    print(i)   # 元祖

>>> (1, 'budong', 'qwe123', datetime.datetime(2018, 3, 7, 16, 57, 9))
>>> (2, 'tj', '123qwe1', datetime.datetime(2018, 3, 7, 16, 54, 9))

 

 

 

posted on 2022-08-10 08:45  root-123  阅读(2415)  评论(0编辑  收藏  举报