초기화
# 检查是否已经安装以及版本号 >>> import sqlalchemy >>> sqlalchemy.__version__ ’1.1.4‘
>>> from sqlalchemy.ext.declarative import declarative_base
# model都是要继承自Base
>>> Base = declarative_base()
>>> from sqlalchemy import Column, Integer, String
>>> class User(Base):
... __tablename__ = 'users' # 指定数据表名
...
... id = Column(Integer, primary_key=True)
... name = Column(String(50))
... fullname = Column(String(50))
... password = Column(String(50))
...
... def __repr__(self):
... return "<User(name='%s', fullname='%s', password='%s')>" % (
... self.name, self.fullname, self.password)
# 查看创建的数据表结构
>>> User.__table__
Table('users', MetaData(bind=None),
Column('id', Integer(), table=<users>, primary_key=True, nullable=False),
Column('name', String(length=50), table=<users>),
Column('fullname', String(length=50), table=<users>),
Column('password', String(length=50), table=<users>), schema=None)데이터 테이블 정식 생성
>>> from sqlalchemy import create_engine
# 连接到mysql
>>> engine = create_engine("mysql://root:root@localhost:3306/python?charset=utf8",
encoding="utf-8", echo=True)
# 正式创建数据表
>>> Base.metadata.create_all(engine)
CREATE TABLE users (
id INTEGER NOT NULL AUTO_INCREMENT,
name VARCHAR(50),
fullname VARCHAR(50),
password VARCHAR(50),
PRIMARY KEY (id)
)다음 작업은 모두 세션 개체 작업을 통해 수행됩니다
>>> from sqlalchemy.orm import sessionmaker >>> Session = sessionmaker(bind=engine) >>> session = Session()
사용자 개체 추가
>>> ed_user = User(name='ed', fullname='Ed Jones', password='edspassword') >>> session.add(ed_user)
질의하고 filter_by를 사용하여 필터링하고 first첫 번째 쿼리된 개체만 나열
>>> our_user = session.query(User).filter_by(name='ed').first()
BEGIN (implicit)
INSERT INTO users (name, fullname, password) VALUES (?, ?, ?)
('ed', 'Ed Jones', 'edspassword')
SELECT users.id AS users_id,
users.name AS users_name,
users.fullname AS users_fullname,
users.password AS users_password
FROM users
WHERE users.name = ?
LIMIT ? OFFSET ?
('ed', 1, 0)
>>> our_user
<User(name='ed', fullname='Ed Jones', password='edspassword')>
>>> ed_user is our_user
True, 한 번에 여러 개체 추가 add_all
>>> session.add_all([ ... User(name='wendy', fullname='Wendy Williams', password='foobar'), ... User(name='mary', fullname='Mary Contrary', password='xxg527'), ... User(name='fred', fullname='Fred Flinstone', password='blah')])
# 可以直接修改ed_user对象 >>> ed_user.password = 'f8s7ccs' # session会自动知道哪些数据被修改了 >>> session.dirty IdentitySet([<User(name='ed', fullname='Ed Jones', password='f8s7ccs')>]) # session也可以知道哪些对象被新建了 >>> session.new IdentitySet([<User(name='wendy', fullname='Wendy Williams', password='foobar')>, <User(name='mary', fullname='Mary Contrary', password='xxg527')>, <User(name='fred', fullname='Fred Flinstone', password='blah')>])
, commit 문에서 객체 1개를 업데이트하고 객체 3개를 생성한 것을 볼 수 있습니다. echo
>>> session.commit()
UPDATE users SET password=? WHERE users.id = ?
('f8s7ccs', 1)
INSERT INTO users (name, fullname, password) VALUES (?, ?, ?)
('wendy', 'Wendy Williams', 'foobar')
INSERT INTO users (name, fullname, password) VALUES (?, ?, ?)
('mary', 'Mary Contrary', 'xxg527')
INSERT INTO users (name, fullname, password) VALUES (?, ?, ?)
('fred', 'Fred Flinstone', 'blah')
COMMIT
>>> ed_user.id
BEGIN (implicit)
SELECT users.id AS users_id,
users.name AS users_name,
users.fullname AS users_fullname,
users.password AS users_password
FROM users
WHERE users.id = ?
(1,)
1으로 변경한 다음 새 사용자를 추가합니다. 하지만 현재로서는 아직 Edwardo이 없다는 점을 기억하세요. commit
>>> ed_user.name = 'Edwardo' and we’ll add another erroneous user, fake_user: >>> fake_user = User(name='fakeuser', fullname='Invalid', password='12345') >>> session.add(fake_user) Querying the session, we can see that they’re flushed into the current transaction:
>>> session.query(User).filter(User.name.in_(['Edwardo', 'fakeuser'])).all()
UPDATE users SET name=? WHERE users.id = ?
('Edwardo', 1)
INSERT INTO users (name, fullname, password) VALUES (?, ?, ?)
('fakeuser', 'Invalid', '12345')
SELECT users.id AS users_id,
users.name AS users_name,
users.fullname AS users_fullname,
users.password AS users_password
FROM users
WHERE users.name IN (?, ?)
('Edwardo', 'fakeuser')
[<User(name='Edwardo', fullname='Ed Jones', password='f8s7ccs')>, <User(name='fakeuser', fullname='Invalid', password='12345')>]으로 돌아왔고 ed이 fake_user세션에서 쫓겨났음을 알 수 있습니다.
>>> session.rollback() ROLLBACK >>> ed_user.name BEGIN (implicit) SELECT users.id AS users_id, users.name AS users_name, users.fullname AS users_fullname, users.password AS users_password FROM users WHERE users.id = ? (1,) u'ed' >>> fake_user in session False issuing a SELECT illustrates the changes made to the database:
사용자 이름이 eded
>>> session.query(User).filter(User.name.in_(['ed', 'fakeuser'])).all()
SELECT users.id AS users_id,
users.name AS users_name,
users.fullname AS users_fullname,
users.password AS users_password
FROM users
WHERE users.name IN (?, ?)
('ed', 'fakeuser')
[<User(name='ed', fullname='Ed Jones', password='f8s7ccs')>]Edwordo커팅으로 다시 변경되었습니다. >>>> session.query(User).filter(User.name.like('%ed')).count()
2
>>> from sqlalchemy import func
>>> session.query(func.count(User.name), User.name).group_by(User.name).all()
[(1, u'ed'), (1, u'fred'), (1, u'mary'), (1, u'wendy')]Querying
객체query는 Query 메소드를 사용하여 생성할 수 있습니다. 사용자 ID별로 정렬하여
>>> for instance in session.query(User).order_by(User.id): ... print(instance.name, instance.fullname) ed Ed Jones wendy Wendy Williams mary Mary Contrary fred Fred Flinstone
쿼리 방법에 따라 세션에서 ORM으로 계측된 설명자를 매개변수로 받을 수도 있습니다. Query에서 반환된 튜플은 KeyedTuple 클래스에서 제공하는 명명된 튜플이며 일반 Python 객체와 매우 유사하게 처리될 수 있습니다. 이름은 속성의 속성 이름 및 클래스의 클래스 이름과 동일합니다.
>>> for name, fullname in session.query(User.name, User.fullname): ... print(name, fullname) ed Ed Jones wendy Wendy Williams mary Mary Contrary fred Fred Flinstone
모든 ColumnElement 파생 객체에서 사용할 수 있는
구문과 하나에 매핑되는 모든 클래스 속성(예: User.name)을 사용하여 개별 열 표현식의 이름을 제어할 수 있습니다.>>> for row in session.query(User, User.name).all(): ... print(row.User, row.name) <User(name='ed', fullname='Ed Jones', password='f8s7ccs')> ed <User(name='wendy', fullname='Wendy Williams', password='foobar')> wendy <User(name='mary', fullname='Mary Contrary', password='xxg527')> mary <User(name='fred', fullname='Fred Flinstone', password='blah')> fred
:label()
>>> for row in session.query(User.name.label('name_label')).all():
... print(row.name_label)
ed
wendy
mary
fred:aliased()
>>> from sqlalchemy.orm import aliased >>> user_alias = aliased(User, name='user_alias') >>> for row in session.query(user_alias, user_alias.name).all(): ... print(row.user_alias) <User(name='ed', fullname='Ed Jones', password='f8s7ccs')> <User(name='wendy', fullname='Wendy Williams', password='foobar')> <User(name='mary', fullname='Mary Contrary', password='xxg527')> <User(name='fred', fullname='Fred Flinstone', password='blah')>
를 두 번 호출하면 ORDER BY:
>>> for u in session.query(User).order_by(User.id)[1:3]: ... print(u) <User(name='wendy', fullname='Wendy Williams', password='foobar')> <User(name='mary', fullname='Mary Contrary', password='xxg527')> and filtering results, which is accomplished either with filter_by(), which uses keyword arguments: >>> for name, in session.query(User.name).\ ... filter_by(fullname='Ed Jones'): ... print(name) ed >>> for name, in session.query(User.name).\ ... filter(User.fullname=='Ed Jones'): ... print(name) ed
를 사용하여 기준을 조인할 수 있습니다. 가장 일반적으로 사용되는 연산자filter()
>>> for user in session.query(User).\ ... filter(User.name=='ed').\ ... filter(User.fullname=='Ed Jones'): ... print(user) <User(name='ed', fullname='Ed Jones', password='f8s7ccs')> Common Filter Operators
AND관계 구축객체 간의 관계를 생성하려면 주소 테이블 아래에 새 관계를 생성해 보겠습니다. 다음 작업은 Django의 ORM보다 더 번거롭습니다. 동시에 두 클래스의 관계를 설정해야 합니다filter()
equals:
query.filter(User.name == 'ed')
not equals:
query.filter(User.name != 'ed')
LIKE:
query.filter(User.name.like('%ed%'))
IN:
query.filter(User.name.in_(['ed', 'wendy', 'jack']))
# works with query objects too:
query.filter(User.name.in_(
session.query(User.name).filter(User.name.like('%ed%'))
))
NOT IN:
query.filter(User.name.in_(['ed', 'wendy', 'jack']))
IS NULL:
query.filter(User.name == None)
# alternatively, if pep8/linters are a concern
query.filter(User.name.is_(None))
IS NOT NULL:
query.filter(User.name != None)
# alternatively, if pep8/linters are a concern
query.filter(User.name.isnot(None))
AND:
# use and_()
from sqlalchemy import and_
query.filter(and_(User.name == 'ed', User.fullname == 'Ed Jones'))
# or send multiple expressions to .filter()
query.filter(User.name == 'ed', User.fullname == 'Ed Jones')
# or chain multiple filter()/filter_by() calls
query.filter(User.name == 'ed').filter(User.fullname == 'Ed Jones')
Note
Make sure you use and_() and not the Python and operator!
OR:
from sqlalchemy import or_
query.filter(or_(User.name == 'ed', User.name == 'wendy'))
Note
Make sure you use or_() and not the Python or operator!
MATCH:
query.filter(User.name.match('wendy'))
Note
match() uses a database-specific MATCH or CONTAINS function;
its behavior will vary by backend and is not available on some backends such as SQLite.>>> from sqlalchemy import ForeignKey
>>> from sqlalchemy.orm import relationship
>>> class Address(Base):
... __tablename__ = 'addresses'
... id = Column(Integer, primary_key=True)
... email_address = Column(String(50), nullable=False)
... user_id = Column(Integer, ForeignKey('users.id'))
...
... user = relationship("User", back_populates="addresses") # 将地址表和用户表关联
...
... def __repr__(self):
... return "<Address(email_address='%s')>" % self.email_address
# 在用户表中还要重新设置一次
>>> User.addresses = relationship(
... "Address", order_by=Address.id, back_populates="user")
>>> Base.metadata.create_all(engine)>>> jack = User(name='jack', fullname='Jack Bean', password='gjffdd') >>> jack.addresses []
이제 주소 개체를 통해 사용자 개체에 액세스할 수 있습니다
>>> jack.addresses = [ ... Address(email_address='jack@google.com'), ... Address(email_address='j25@yahoo.com')]
Jack Bean을 데이터베이스에 추가하고 커밋하고 해당 주소 컬렉션의 두 주소 멤버는 모두 있습니다. 캐스케이딩이라는 프로세스를 사용하여 한 번에 세션에 추가됩니다.
다음
데이터베이스에 저장
>>> jack.addresses[1] <Address(email_address='j25@yahoo.com')> >>> jack.addresses[1].user <User(name='jack', fullname='Jack Bean', password='gjffdd')>
Jack을 쿼리하면 아직 Jack이 반환되지 않습니다. Jack의 주소:
>>> session.add(jack)
>>> session.commit()
sqlalchemy.engine.base.Engine INSERT INTO addresses (email_address, user_id) VALUES (%s, %s)
sqlalchemy.engine.base.Engine ('jack@google.com', 5L)
sqlalchemy.engine.base.Engine INSERT INTO addresses (email_address, user_id) VALUES (%s, %s)
sqlalchemy.engine.base.Engine ('j25@yahoo.com', 5L)
sqlalchemy.engine.base.Engine COMMIT주소 컬렉션에 액세스했을 때 SQL이 갑자기 실행되었습니다. 이는 지연 로딩 관계의 예입니다. 이제 주소 컬렉션이 로드되어 일반 목록처럼 작동합니다. 이 컬렉션의 로딩을 약간 최적화하는 방법입니다.commit
>>> session.delete(jack) >>> session.query(User).filter_by(name='jack').count() 0 So far, so good. How about Jack’s Address objects ? >>> session.query(Address).filter( ... Address.email_address.in_(['jack@google.com', 'j25@yahoo.com']) ... ).count() 2
Uh oh, they’re still there ! Analyzing the flush SQL, we can see that the user_id column of each address was set to NULL, but the rows weren’t deleted. SQLAlchemy doesn’t assume that deletes cascade, you have to tell it to do so. Configuring delete/delete-orphan Cascade. We will configure cascade options on the User.addresses relationship to change the behavior. While SQLAlchemy allows you to add new attributes and relationships to mappings at any point in time, in this case the existing relationship needs to be removed, so we need to tear down the mappings completely and start again - we’ll close the Session:
直接close来rollback，并不进行commit
>>> session.close() ROLLBACK
Use a new declarative_base():
>>> Base = declarative_base()
Next we’ll declare the User class, adding in the addresses relationship
including the cascade configuration (we’ll leave the constructor out too):
>>> class User(Base):
... __tablename__ = 'users'
...
... id = Column(Integer, primary_key=True)
... name = Column(String(50))
... fullname = Column(String(50))
... password = Column(String(50))
...
... addresses = relationship("Address", back_populates='user',
... cascade="all, delete, delete-orphan")
...
... def __repr__(self):
... return "<User(name='%s', fullname='%s', password='%s')>" % (
... self.name, self.fullname, self.password)Then we recreate Address, noting that in this case
we’ve created the Address.user relationship via the User class already:
>>> class Address(Base):
... __tablename__ = 'addresses'
... id = Column(Integer, primary_key=True)
... email_address = Column(String(50), nullable=False)
... user_id = Column(Integer, ForeignKey('users.id'))
... user = relationship("User", back_populates="addresses")
...
... def __repr__(self):
... return "<Address(email_address='%s')>" % self.email_addressNow when we load the user jack (below using get(), which loads by primary key), removing an address from the corresponding addresses collection will result in that Address being deleted:
# load Jack by primary key >>> jack = session.query(User).get(5) # remove one Address (lazy load fires off) >>> del jack.addresses[1] # only one address remains >>> session.query(Address).filter( ... Address.email_address.in_(['jack@google.com', 'j25@yahoo.com']) ... ).count() 1
Deleting Jack will delete both Jack and the remaining Address associated with the user:
>>> session.delete(jack) >>> session.query(User).filter_by(name='jack').count() 0 >>> session.query(Address).filter( ... Address.email_address.in_(['jack@google.com', 'j25@yahoo.com']) ... ).count() 0
Further detail on configuration of cascades is at Cascades. The cascade functionality can also integrate smoothly with the ON DELETE CASCADE functionality of the relational database. See Using Passive Deletes for details.
上面同时设置两个relationship太麻烦了，可以使用backref
from sqlalchemy import Integer, ForeignKey, String, Column
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import relationship
Base = declarative_base()
class User(Base):
__tablename__ = 'user'
id = Column(Integer, primary_key=True)
name = Column(String)
addresses = relationship("Address", backref="user")
class Address(Base):
__tablename__ = 'address'
id = Column(Integer, primary_key=True)
email = Column(String)
user_id = Column(Integer, ForeignKey('user.id'))The above configuration establishes a collection of Address objects on User called User.addresses. It also establishes a .user attribute on Address which will refer to the parent User object.
In fact, the backref keyword is only a common shortcut for placing a second relationship() onto the Address mapping, including the establishment of an event listener on both sides which will mirror attribute operations in both directions. The above configuration is equivalent to:
rom sqlalchemy import Integer, ForeignKey, String, Column
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import relationship
Base = declarative_base()
class User(Base):
__tablename__ = 'user'
id = Column(Integer, primary_key=True)
name = Column(String)
addresses = relationship("Address", back_populates="user")
class Address(Base):
__tablename__ = 'address'
id = Column(Integer, primary_key=True)
email = Column(String)
user_id = Column(Integer, ForeignKey('user.id'))
user = relationship("User", back_populates="addresses")Above, we add a .user relationship to Address explicitly. On both relationships, the back_populates directive tells each relationship about the other one, indicating that they should establish “bidirectional” behavior between each other. The primary effect of this configuration is that the relationship adds event handlers to both attributes which have the behavior of “when an append or set event occurs here, set ourselves onto the incoming attribute using this particular attribute name”. The behavior is illustrated as follows. Start with a User and an Address instance. The .addresses collection is empty, and the .user attribute is None:
>>> u1 = User() >>> a1 = Address() >>> u1.addresses [] >>> print(a1.user) None
However, once the Address is appended to the u1.addresses collection, both the collection and the scalar attribute have been populated:
>>> u1.addresses.append(a1) >>> u1.addresses [<__main__.Address object at 0x12a6ed0>] >>> a1.user <__main__.User object at 0x12a6590>
This behavior of course works in reverse for removal operations as well, as well as for equivalent operations on both sides. Such as when .user is set again to None, the Address object is removed from the reverse collection:
>>> a1.user = None >>> u1.addresses []
The manipulation of the .addresses collection and the .user attribute occurs entirely in Python without any interaction with the SQL database. Without this behavior, the proper state would be apparent on both sides once the data has been flushed to the database, and later reloaded after a commit or expiration operation occurs. The backref/back_populates behavior has the advantage that common bidirectional operations can reflect the correct state without requiring a database round trip.
Remember, when the backref keyword is used on a single relationship, it’s exactly the same as if the above two relationships were created inpidually using back_populates on each.
检验一下我们上面的成果以及熟悉创建的mysql表的结构
地址表的结构
> SHOW CREATE TABLE addresses; +-----------+----------------+ | Table | Create Table | |-----------+----------------| | addresses | CREATE TABLE `addresses` ( `id` int(11) NOT NULL AUTO_INCREMENT, `email_address` varchar(50) NOT NULL, `user_id` int(11) DEFAULT NULL, PRIMARY KEY (`id`), KEY `user_id` (`user_id`), CONSTRAINT `addresses_ibfk_1` FOREIGN KEY (`user_id`) REFERENCES `users` (`id`) ) ENGINE=InnoDB AUTO_INCREMENT=5 DEFAULT CHARSET=utf8 | +-----------+----------------+ 1 row in set Time: 0.005s > DESC addresses; +---------------+-------------+--------+-------+-----------+----------------+ | Field | Type | Null | Key | Default | Extra | |---------------+-------------+--------+-------+-----------+----------------| | id | int(11) | NO | PRI | <null> | auto_increment | | email_address | varchar(50) | NO | | <null> | | | user_id | int(11) | YES | MUL | <null> | | +---------------+-------------+--------+-------+-----------+----------------+ 3 rows in set Time: 0.002s
用户表的结构
> SHOW CREATE TABLE users; +---------+----------------+ | Table | Create Table | |---------+----------------| | users | CREATE TABLE `users` ( `id` int(11) NOT NULL AUTO_INCREMENT, `name` varchar(50) DEFAULT NULL, `fullname` varchar(50) DEFAULT NULL, `password` varchar(50) DEFAULT NULL, PRIMARY KEY (`id`) ) ENGINE=InnoDB AUTO_INCREMENT=6 DEFAULT CHARSET=utf8 | +---------+----------------+ 1 row in set Time: 0.002s > DESC users; +----------+-------------+--------+-------+-----------+----------------+ | Field | Type | Null | Key | Default | Extra | |----------+-------------+--------+-------+-----------+----------------| | id | int(11) | NO | PRI | <null> | auto_increment | | name | varchar(50) | YES | | <null> | | | fullname | varchar(50) | YES | | <null> | | | password | varchar(50) | YES | | <null> | | +----------+-------------+--------+-------+-----------+----------------+ 4 rows in set Time: 0.003s
详细数据
> SELECT * FROM addresses; +------+-----------------+-----------+ | id | email_address | user_id | |------+-----------------+-----------| | 3 | jack@google.com | 5 | | 4 | j25@yahoo.com | 5 | +------+-----------------+-----------+ 2 rows in set Time: 0.002s > SELECT * FROM users; +------+--------+----------------+------------+ | id | name | fullname | password | |------+--------+----------------+------------| | 1 | ed | Ed Jones | f8s7ccs | | 2 | wendy | Wendy Williams | foobar | | 3 | mary | Mary Contrary | xxg527 | | 4 | fred | Fred Flinstone | blah | | 5 | jack | Jack Bean | gjffdd | +------+--------+----------------+------------+ 5 rows in set Time: 0.003s
from sqlalchemy import Column, String, Integer, create_engine, SmallInteger from sqlalchemy.orm import sessionmaker from sqlalchemy.ext.declarative import declarative_base DB_URI = 'sqlite:///user.db' Base = declarative_base() engine = create_engine(DB_URI) Base.metadata.bind = engine Session = sessionmaker(bind=engine) session = Session()
class User(Base):
__tablename__ = 'live_user'
id = Column(Integer, unique=True, primary_key=True, autoincrement=True)
speaker_id = Column(String(40), index=True, unique=True)
name = Column(String(40), index=True, nullable=False)
gender = Column(SmallInteger, default=2)
headline = Column(String(200))
avatar_url = Column(String(100), nullable=False)
bio = Column(String(200))
description = Column(String())
@classmethod
def add(cls, **kwargs):
speaker_id = kwargs.get('speaker_id', None)
if id is not None:
r = session.query(cls).filter_by(speaker_id=speaker_id).first()
if r:
return r
try:
r = cls(**kwargs)
session.add(r)
session.commit()
except:
session.rollback()
raise
else:
return rBase.metadata.create_all()
接口分为2种：
http://www.php.cn/ (未结束)
http://www.php.cn/ (已结束)
elasticsearch-dsl-py相比elasticsearch-py做了各种封装，DSL也支持用类代表一个doc_type（类似数据库中的Table），实现ORM的效果。我们就用它来写Live模型：
from elasticsearch_dsl import DocType, Date, Integer, Text, Float, Boolean from elasticsearch_dsl.connections import connections from elasticsearch_dsl.query import SF, Q from config import SEARCH_FIELDS from .speaker import User, session connections.create_connection(hosts=['localhost'])
class Live(DocType):
id = Integer()
speaker_id = Integer()
feedback_score = Float() # 评分
topic_names = Text(analyzer='ik_max_word') # 话题标签名字
seats_taken = Integer() # 参与人数
subject = Text(analyzer='ik_max_word') # 标题
amount = Float() # 价格(RMB)
description = Text(analyzer='ik_max_word')
status = Boolean() # public(True)/ended(False)
starts_at = Date()
outline = Text(analyzer='ik_max_word') # Live内容
speaker_message_count = Integer()
tag_names = Text(analyzer='ik_max_word')
liked_num = Integer()
class Meta:
index = 'live'
@classmethod
def add(cls, **kwargs):
id = kwargs.pop('id', None)
if id is None:
return False
live = cls(meta={'id': id}, **kwargs)
live.save()
return live它允许我们用一种非常可维护的方法来组织字典：
In : from elasticsearch_dsl.query import Q
In : Q('multi_match', subject='python').to_dict()
Out: {'multi_match': {'subject': 'python'}}In : from elasticsearch import Elasticsearch
In : from elasticsearch_dsl import Search, Q
In : s = Search(using=client, index='live')
In : s = s.query('match', subject='python').query(~Q('match', description='量化'))
In : s.execute()
Out: <Response: [<Hit(live/live/789840559912009728): {'subject': 'Python 工程师的入门和进阶', 'feedback_score': 4.5, 'stat...}>]>上述例子表示从live这个索引（类似数据库中的Database）中找到subject字典包含python，但是description字段不包含量化的Live。
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