摘要：因?yàn)槭枪ぷ髟谝粋€內(nèi)部，有時候我們可能不小心做了一些誤刪除的操作，可以回滾。我們先修改的用戶名為，然后重新添加一個新，但是記住這個時候我們還沒有。集合類型可以是各種合法類型，比如，但是默認(rèn)集合是一個。
官方文檔
Initialization
# 檢查是否已經(jīng)安裝以及版本號
>>> import sqlalchemy
>>> sqlalchemy.__version__
’1.1.4‘
>>> from sqlalchemy.ext.declarative import declarative_base
# model都是要繼承自Base
>>> Base = declarative_base()
>>> from sqlalchemy import Column, Integer, String
>>> class User(Base):
... __tablename__ = "users" # 指定數(shù)據(jù)表名
...
... id = Column(Integer, primary_key=True)
... name = Column(String(50))
... fullname = Column(String(50))
... password = Column(String(50))
...
... def __repr__(self):
... return "" % (
... self.name, self.fullname, self.password)
# 查看創(chuàng)建的數(shù)據(jù)表結(jié)構(gòu)
>>> User.__table__
Table("users", MetaData(bind=None),
Column("id", Integer(), table=, primary_key=True, nullable=False),
Column("name", String(length=50), table=),
Column("fullname", String(length=50), table=),
Column("password", String(length=50), table=), schema=None)
正式創(chuàng)建數(shù)據(jù)表
>>> from sqlalchemy import create_engine
# 連接到mysql
>>> engine = create_engine("mysql://root:root@localhost:3306/python?charset=utf8",
encoding="utf-8", echo=True)
# 正式創(chuàng)建數(shù)據(jù)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)
)
Creating a Session
下面的操作都是要通過會話對象操作
>>> from sqlalchemy.orm import sessionmaker
>>> Session = sessionmaker(bind=engine)
>>> session = Session()
Adding and Updating Objects
添加一個User對象
>>> ed_user = User(name="ed", fullname="Ed Jones", password="edspassword")
>>> session.add(ed_user)
查詢一下，使用filter_by來過濾，first只列出第一個查詢到的對象
>>> 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
>>> ed_user is our_user
True
使用add_all，一次性添加多個對象
>>> 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")])
Session很智能，比如說，它知道Ed Jones被修改了
# 可以直接修改ed_user對象
>>> ed_user.password = "f8s7ccs"
# session會自動知道哪些數(shù)據(jù)被修改了
>>> session.dirty
IdentitySet([])
# session也可以知道哪些對象被新建了
>>> session.new
IdentitySet([,
,
])
對數(shù)據(jù)庫進(jìn)行了變更，自然要進(jìn)行commit，從echo語句我們可以看出，我們更新了1個對象，創(chuàng)建了3個對象。
>>> 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,)
1
Rolling Back
因?yàn)镾ession是工作在一個transaction內(nèi)部，有時候我們可能不小心做了一些誤刪除的操作，可以回滾。我們先修改ed_user的用戶名為Edwardo，然后重新添加一個新User，但是記住這個時候我們還沒有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:
查詢檢驗(yàn)一下
>>> 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")
[, ]
回滾，我們可以知道ed_user‘s name is back to ed以及fake_user has been kicked out of the session
>>> 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:
這個時候再查詢，很明顯fakeuser已經(jīng)消失了，ed用戶的名字重新變回了ed而不是Edwordo
>>> 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")
[]
Couting
用于查詢操作相對應(yīng)的count()操作
>>> 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
一個通過在Session上使用query方法可以創(chuàng)建一個Query object
按照用戶id進(jìn)行排序來進(jìn)行查詢
>>> 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
query方法也可以接收ORM-instrumented descriptors作為參數(shù)。返回結(jié)果是一個named tuples
>>> 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
The tuples returned by Query are named tuples, supplied by the KeyedTuple class, and can be treated much like an ordinary Python object. The names are the same as the attribute’s name for an attribute, and the class name for a class:
>>> for row in session.query(User, User.name).all():
... print(row.User, row.name)
ed
wendy
mary
fred
You can control the names of individual column expressions using the label() construct, which is available from any ColumnElement-derived object, as well as any class attribute which is mapped to one (such as User.name):
>>> for row in session.query(User.name.label("name_label")).all():
... print(row.name_label)
ed
wendy
mary
fred
The name given to a full entity such as User, assuming that multiple entities are present in the call to query(), can be controlled using 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)
Basic operations with Query include issuing LIMIT and OFFSET, most conveniently using Python array slices and typically in conjunction with ORDER BY:
>>> for u in session.query(User).order_by(User.id)[1:3]:
... print(u)
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
The Query object is fully generative, meaning that most method calls return a new Query object upon which further criteria may be added. For example, to query for users named “ed” with a full name of “Ed Jones”, you can call filter() twice, which joins criteria using AND:
>>> for user in session.query(User).
... filter(User.name=="ed").
... filter(User.fullname=="Ed Jones"):
... print(user)
Common Filter Operators
下面列出了filter()最常用的一些operators
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.
Building a Relationship
創(chuàng)建對象與對象之間的關(guān)系，下面我們新建一個Address表，下面的操作相比django的orm繁瑣一些，要同時在兩個class內(nèi)部同時設(shè)置relationship
>>> 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") # 將地址表和用戶表關(guān)聯(lián)
...
... def __repr__(self):
... return "" % self.email_address
# 在用戶表中還要重新設(shè)置一次
>>> User.addresses = relationship(
... "Address", order_by=Address.id, back_populates="user")
>>> Base.metadata.create_all(engine)
Working with Related Objects
現(xiàn)在我們創(chuàng)建了一個User，與它對應(yīng)的一個空addresses集合也將創(chuàng)立。集合類型可以是各種合法類型，比如set/dictionaries(see Customizing Collection Access for details)，但是默認(rèn)集合是一個list。
現(xiàn)在我們再來創(chuàng)建一個用戶Jack
>>> jack = User(name="jack", fullname="Jack Bean", password="gjffdd")
>>> jack.addresses
[]
We are free to add Address objects on our User object. In this case we just assign a full list directly:
現(xiàn)在我們將用戶Jack和一些地址關(guān)聯(lián)起來
>>> jack.addresses = [
... Address(email_address="jack@google.com"),
... Address(email_address="j25@yahoo.com")]
When using a bidirectional relationship, elements added in one direction automatically become visible in the other direction. This behavior occurs based on attribute on-change events and is evaluated in Python, without using any SQL:
現(xiàn)在可以通過地址對象訪問用戶對象了
>>> jack.addresses[1]
>>> jack.addresses[1].user
Let’s add and commit Jack Bean to the database. jack as well as the two Address members in the corresponding addresses collection are both added to the session at once, using a process known as cascading:
接下來commit保存到數(shù)據(jù)庫
>>> 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 COMMIT
Querying for Jack, we get just Jack back. No SQL is yet issued for Jack’s addresses:
>>> jack = session.query(User).
... filter_by(name="jack").one()
>>> jack
Let’s look at the addresses collection. Watch the SQL:
>>> jack.addresses
[, ]
When we accessed the addresses collection, SQL was suddenly issued. This is an example of a lazy loading relationship. The addresses collection is now loaded and behaves just like an ordinary list. We’ll cover ways to optimize the loading of this collection in a bit.
Delete
刪除操作，接下來我們嘗試刪除jack對象，注意地址對象并不會因此而刪除
>>> 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來rollback，并不進(jìn)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 "" % (
... 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 "" % self.email_address
Now 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.
backref
上面同時設(shè)置兩個relationship太麻煩了，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 individually using back_populates on each.
mysql操作
檢驗(yàn)一下我們上面的成果以及熟悉創(chuàng)建的mysql表的結(jié)構(gòu)
地址表的結(jié)構(gòu)
> 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 | | auto_increment |
| email_address | varchar(50) | NO | | | |
| user_id | int(11) | YES | MUL | | |
+---------------+-------------+--------+-------+-----------+----------------+
3 rows in set
Time: 0.002s
用戶表的結(jié)構(gòu)
> 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 | | auto_increment |
| name | varchar(50) | YES | | | |
| fullname | varchar(50) | YES | | | |
| password | varchar(50) | YES | | | |
+----------+-------------+--------+-------+-----------+----------------+
4 rows in set
Time: 0.003s
詳細(xì)數(shù)據(jù)
> 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
知乎live設(shè)計模型
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 r
Base.metadata.create_all()
接口分為2種：
https://api.zhihu.com/lives/o... (未結(jié)束)
https://api.zhihu.com/lives/e... (已結(jié)束)
elasticsearch-dsl-py相比elasticsearch-py做了各種封裝，DSL也支持用類代表一個doc_type（類似數(shù)據(jù)庫中的Table），實(shí)現(xiàn)ORM的效果。我們就用它來寫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() # 評分
topic_names = Text(analyzer="ik_max_word") # 話題標(biāo)簽名字
seats_taken = Integer() # 參與人數(shù)
subject = Text(analyzer="ik_max_word") # 標(biāo)題
amount = Float() # 價格(RMB)
description = Text(analyzer="ik_max_word")
status = Boolean() # public(True)/ended(False)
starts_at = Date()
outline = Text(analyzer="ik_max_word") # Live內(nèi)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
它允許我們用一種非常可維護(hù)的方法來組織字典：
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: ]>
上述例子表示從live這個索引（類似數(shù)據(jù)庫中的Database）中找到subject字典包含python，但是description字段不包含量化的Live。
