使用 SELECT 语句¶

Using SELECT Statements

中文

对于 Core 和 ORM，select() 函数生成一个 Select 构造，用于所有 SELECT 查询。在 Core 中传递给 Connection.execute() 方法，在 ORM 中传递给 Session.execute() 方法，在当前事务中发出一个 SELECT 语句，并通过返回的 Result 对象获取结果行。

ORM 读者 - 这里的内容同样适用于 Core 和 ORM 的使用，并且提到了基本的 ORM 变体用例。然而，还有更多特定于 ORM 的功能；这些记录在 ORM 查询指南。

英文

For both Core and ORM, the select() function generates a Select construct which is used for all SELECT queries. Passed to methods like Connection.execute() in Core and Session.execute() in ORM, a SELECT statement is emitted in the current transaction and the result rows available via the returned Result object.

ORM Readers - the content here applies equally well to both Core and ORM use and basic ORM variant use cases are mentioned here. However there are a lot more ORM-specific features available as well; these are documented at ORM 查询指南.

select() SQL 表达式构造¶

The select() SQL Expression Construct

中文

select() 构造的语句构建方式与 insert() 类似，使用 generative 方法，其中每个方法在对象上构建更多状态。与其他 SQL 构造一样，它可以直接字符串化:

>>> from sqlalchemy importselect>>> stmt=select(user_table).where(user_table.c.name=="spongebob")>>> print(stmt)SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = :name_1

与所有其他语句级 SQL 构造相同，要实际运行该语句，我们将其传递给执行方法。由于 SELECT 语句返回行，我们始终可以迭代结果对象以获取 Row 对象：

>>> withengine.connect()asconn:...     for row in conn.execute(stmt):...         print(row)BEGIN (implicit)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = ?
[...] ('spongebob',)
(1, 'spongebob', 'Spongebob Squarepants')ROLLBACK

使用 ORM 时，特别是对于针对 ORM 实体构建的 select() 构造，我们需要使用 Session 上的 Session.execute() 方法来执行它；使用这种方法，我们继续从结果中获取 Row 对象，但是这些行现在能够包括完整的实体，例如 User 类的实例，作为每行中的单个元素：

>>> stmt=select(User).where(User.name=="spongebob")>>> withSession(engine)assession:...     for row in session.execute(stmt):...         print(row)BEGIN (implicit)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = ?
[...] ('spongebob',)
(User(id=1, name='spongebob', fullname='Spongebob Squarepants'),)ROLLBACK

以下部分将更详细地讨论 SELECT 构造。

英文

The select() construct builds up a statement in the same way as that of insert(), using a generative approach where each method builds more state onto the object. Like the other SQL constructs, it can be stringified in place:

>>> from sqlalchemy importselect>>> stmt=select(user_table).where(user_table.c.name=="spongebob")>>> print(stmt)SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = :name_1

Also in the same manner as all other statement-level SQL constructs, to actually run the statement we pass it to an execution method. Since a SELECT statement returns rows we can always iterate the result object to get Row objects back:

>>> withengine.connect()asconn:...     for row in conn.execute(stmt):...         print(row)BEGIN (implicit)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = ?
[...] ('spongebob',)
(1, 'spongebob', 'Spongebob Squarepants')ROLLBACK

When using the ORM, particularly with a select() construct that’s composed against ORM entities, we will want to execute it using the Session.execute() method on the Session; using this approach, we continue to get Row objects from the result, however these rows are now capable of including complete entities, such as instances of the User class, as individual elements within each row:

>>> stmt=select(User).where(User.name=="spongebob")>>> withSession(engine)assession:...     for row in session.execute(stmt):...         print(row)BEGIN (implicit)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = ?
[...] ('spongebob',)
(User(id=1, name='spongebob', fullname='Spongebob Squarepants'),)ROLLBACK

The following sections will discuss the SELECT construct in more detail.

设置 COLUMNS 和 FROM 子句¶

Setting the COLUMNS and FROM clause

中文

select() 函数接受表示任意数量的 Column 和/或 Table 表达式的位置元素，以及广泛的兼容对象，这些对象被解析为要从中 SELECT 的 SQL 表达式列表，这些表达式将作为结果集中返回的列。这些元素在更简单的情况下也用于创建 FROM 子句，该子句是从传递的列和类似表的表达式推断出来的:

>>> print(select(user_table))SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account

要使用 Core 方法从单个列 SELECT，Column 对象从 Table.c 访问器访问并可以直接发送；FROM 子句将被推断为那些列所代表的所有 Table 和其他 FromClause 对象的集合:

>>> print(select(user_table.c.name,user_table.c.fullname))SELECT user_account.name, user_account.fullname
FROM user_account

或者，当使用任何 FromClause （如 Table）的 FromClause.c 集合时，可以通过使用字符串名称的元组为 select() 指定多个列:

>>> print(select(user_table.c["name","fullname"]))SELECT user_account.name, user_account.fullname
FROM user_account

在 2.0 版本加入: 为 FromClause.c 集合添加了元组访问器功能

英文

The select() function accepts positional elements representing any number of Column and/or Table expressions, as well as a wide range of compatible objects, which are resolved into a list of SQL expressions to be SELECTed from that will be returned as columns in the result set. These elements also serve in simpler cases to create the FROM clause, which is inferred from the columns and table-like expressions passed:

>>> print(select(user_table))SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account

To SELECT from individual columns using a Core approach, Column objects are accessed from the Table.c accessor and can be sent directly; the FROM clause will be inferred as the set of all Table and other FromClause objects that are represented by those columns:

>>> print(select(user_table.c.name,user_table.c.fullname))SELECT user_account.name, user_account.fullname
FROM user_account

Alternatively, when using the FromClause.c collection of any FromClause such as Table, multiple columns may be specified for a select() by using a tuple of string names:

>>> print(select(user_table.c["name","fullname"]))SELECT user_account.name, user_account.fullname
FROM user_account

在 2.0 版本加入: Added tuple-accessor capability to the FromClause.c collection

选择 ORM 实体和列¶

Selecting ORM Entities and Columns

中文

ORM 实体，如我们的 User 类以及它上面映射到列的属性如 User.name，也参与表示表和列的 SQL 表达式语言系统。下面展示了一个从 User 实体中 SELECT 的示例，最终呈现的方式与我们直接使用 user_table 一样：

>>> print(select(User))
{printsql}SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account

当使用 ORM Session.execute() 方法执行如上语句时，与从 user_table 中 SELECT 相比，有一个重要的区别，即 实体本身作为每行中的单个元素返回。也就是说，当我们从上述语句中获取行时，由于要获取的内容列表中只有 User 实体，我们返回的 Row 对象只有一个元素，其中包含 User 类的实例：

>>> row = session.execute(select(User)).first()
{execsql}BEGIN...
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
[...] (){stop}
>>> row
(User(id=1, name='spongebob', fullname='Spongebob Squarepants'),)

上述 Row 只有一个元素，表示 User 实体：

>>> row[0]
User(id=1, name='spongebob', fullname='Spongebob Squarepants')

一种强烈推荐的实现上述相同结果的便捷方法是直接使用 Session.scalars() 方法执行语句；该方法将返回一个 ScalarResult 对象，该对象一次提供每行的第一个“列”，在这种情况下，是 User 类的实例：

>>> user = session.scalars(select(User)).first()
{execsql}SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
[...] (){stop}
>>> user
User(id=1, name='spongebob', fullname='Spongebob Squarepants')

或者，我们可以选择 ORM 实体的单个列作为结果行中的独立元素，通过使用类绑定的属性；当这些属性传递给诸如 select() 之类的构造时，它们将解析为每个属性表示的 Column 或其他 SQL 表达式：

>>> print(select(User.name, User.fullname))
{printsql}SELECT user_account.name, user_account.fullname
FROM user_account

当我们使用 Session.execute() 调用*这个*语句时，我们现在收到的行对于每个值都有单独的元素，每个元素对应一个单独的列或其他 SQL 表达式：

>>> row = session.execute(select(User.name, User.fullname)).first()
{execsql}SELECT user_account.name, user_account.fullname
FROM user_account
[...] (){stop}
>>> row
('spongebob', 'Spongebob Squarepants')

这些方法也可以混合使用，如下所示，我们将 User 实体的 name 属性作为行的第一个元素，并将完整的 Address 实体作为第二个元素：

>>> session.execute(
...     select(User.name, Address).where(User.id == Address.user_id).order_by(Address.id)
... ).all()
{execsql}SELECT user_account.name, address.id, address.email_address, address.user_id
FROM user_account, address
WHERE user_account.id = address.user_id ORDER BY address.id
[...] (){stop}
[('spongebob', Address(id=1, email_address='spongebob@sqlalchemy.org')),
('sandy', Address(id=2, email_address='sandy@sqlalchemy.org')),
('sandy', Address(id=3, email_address='sandy@squirrelpower.org'))]

选择 ORM 实体和列的方法以及将行转换为常见方法将在选择 ORM 实体和属性中进一步讨论。

参见

选择 ORM 实体和属性 - 在 ORM 查询指南中

英文

ORM entities, such our User class as well as the column-mapped attributes upon it such as User.name, also participate in the SQL Expression Language system representing tables and columns. Below illustrates an example of SELECTing from the User entity, which ultimately renders in the same way as if we had used user_table directly:

>>> print(select(User))SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account

When executing a statement like the above using the ORM Session.execute() method, there is an important difference when we select from a full entity such as User, as opposed to user_table, which is that the entity itself is returned as a single element within each row. That is, when we fetch rows from the above statement, as there is only the User entity in the list of things to fetch, we get back Row objects that have only one element, which contain instances of the User class:

>>> row=session.execute(select(User)).first()BEGIN...
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
[...] ()
>>> row(User(id=1, name='spongebob', fullname='Spongebob Squarepants'),)

The above Row has just one element, representing the User entity:

>>> row[0]
User(id=1, name='spongebob', fullname='Spongebob Squarepants')

A highly recommended convenience method of achieving the same result as above is to use the Session.scalars() method to execute the statement directly; this method will return a ScalarResult object that delivers the first “column” of each row at once, in this case, instances of the User class:

>>> user=session.scalars(select(User)).first()SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
[...] ()
>>> userUser(id=1, name='spongebob', fullname='Spongebob Squarepants')

Alternatively, we can select individual columns of an ORM entity as distinct elements within result rows, by using the class-bound attributes; when these are passed to a construct such as select(), they are resolved into the Column or other SQL expression represented by each attribute:

>>> print(select(User.name,User.fullname))SELECT user_account.name, user_account.fullname
FROM user_account

When we invoke this statement using Session.execute(), we now receive rows that have individual elements per value, each corresponding to a separate column or other SQL expression:

>>> row=session.execute(select(User.name,User.fullname)).first()SELECT user_account.name, user_account.fullname
FROM user_account
[...] ()
>>> row('spongebob', 'Spongebob Squarepants')

The approaches can also be mixed, as below where we SELECT the name attribute of the User entity as the first element of the row, and combine it with full Address entities in the second element:

>>> session.execute(...     select(User.name, Address).where(User.id == Address.user_id).order_by(Address.id)... ).all()SELECT user_account.name, address.id, address.email_address, address.user_id
FROM user_account, address
WHERE user_account.id = address.user_id ORDER BY address.id
[...] ()
[('spongebob', Address(id=1, email_address='spongebob@sqlalchemy.org')),('sandy', Address(id=2, email_address='sandy@sqlalchemy.org')),('sandy', Address(id=3, email_address='sandy@squirrelpower.org'))]

Approaches towards selecting ORM entities and columns as well as common methods for converting rows are discussed further at 选择 ORM 实体和属性.

参见

选择 ORM 实体和属性 - in the ORM 查询指南

从带标签的 SQL 表达式中选择¶

Selecting from Labeled SQL Expressions

中文

ColumnElement.label() 方法以及 ORM 属性上相同名称的方法提供了列或表达式的 SQL 标签，使其在结果集中具有特定名称。这在按名称引用结果行中的任意 SQL 表达式时非常有用：

>>> from sqlalchemy importfunc,cast>>> stmt=select(...     ("Username: " + user_table.c.name).label("username"),... ).order_by(user_table.c.name)>>> withengine.connect()asconn:...     for row in conn.execute(stmt):...         print(f"{row.username}")BEGIN (implicit)
SELECT ? || user_account.name AS username
FROM user_account ORDER BY user_account.name
[...] ('Username: ',)
Username: patrickUsername: sandyUsername: spongebobROLLBACK

参见

按标签排序或分组 - 我们创建的标签名称也可以在 Select 的 ORDER BY 或 GROUP BY 子句中引用。

英文

The ColumnElement.label() method as well as the same-named method available on ORM attributes provides a SQL label of a column or expression, allowing it to have a specific name in a result set. This can be helpful when referring to arbitrary SQL expressions in a result row by name:

>>> from sqlalchemy importfunc,cast>>> stmt=select(...     ("Username: " + user_table.c.name).label("username"),... ).order_by(user_table.c.name)>>> withengine.connect()asconn:...     for row in conn.execute(stmt):...         print(f"{row.username}")BEGIN (implicit)
SELECT ? || user_account.name AS username
FROM user_account ORDER BY user_account.name
[...] ('Username: ',)
Username: patrickUsername: sandyUsername: spongebobROLLBACK

参见

按标签排序或分组 - the label names we create may also be referenced in the ORDER BY or GROUP BY clause of the Select.

使用文本列表达式进行选择¶

Selecting with Textual Column Expressions

中文

当我们使用 select() 函数构造 Select 对象时，通常会传递一系列使用 table metadata 定义的 Table 和 Column 对象，或者在使用 ORM 时，我们可能会发送表示表列的 ORM 映射属性。然而，有时也需要在语句中制造任意 SQL 块，例如常量字符串表达式，或者只是一些更快地直接编写的任意 SQL。

在使用事务和DBAPI 中介绍的 text() 构造实际上可以直接嵌入到 Select 构造中，例如下面我们制造一个硬编码字符串字面量 'some phrase' 并将其嵌入到 SELECT 语句中:

>>> from sqlalchemy import text
>>> stmt = select(text("'some phrase'"), user_table.c.name).order_by(user_table.c.name)
>>> with engine.connect() as conn:

… print(conn.execute(stmt).all()) {execsql}BEGIN (implicit) SELECT ‘some phrase’, user_account.name FROM user_account ORDER BY user_account.name [generated in …] () {stop}[(‘some phrase’, ‘patrick’), (‘some phrase’, ‘sandy’), (‘some phrase’, ‘spongebob’)] {execsql}ROLLBACK{stop}

虽然 text() 构造可以在大多数地方用于注入字面 SQL 短语，但更多时候我们实际上在处理每个表示单个列表达式的文本单元。在这种常见情况下，我们可以使用 literal_column() 构造从我们的文本片段中获得更多功能。此对象类似于 text()，但它明确表示单个“列”，然后可以在子查询和其他表达式中标记和引用:

>>> from sqlalchemy import literal_column
>>> stmt = select(literal_column("'some phrase'").label("p"), user_table.c.name).order_by(

… user_table.c.name … ) >>> with engine.connect() as conn: … for row in conn.execute(stmt): … print(f”{row.p}, {row.name}”) {execsql}BEGIN (implicit) SELECT ‘some phrase’ AS p, user_account.name FROM user_account ORDER BY user_account.name [generated in …] () {stop}some phrase, patrick some phrase, sandy some phrase, spongebob {execsql}ROLLBACK{stop}

请注意，在两种情况下，当使用 text() 或 literal_column() 时，我们正在编写一个语法 SQL 表达式，而不是一个字面值。因此，我们必须包括所需的任何引用或语法以呈现我们希望看到的 SQL。

英文

When we construct a Select object using the select() function, we are normally passing to it a series of Table and Column objects that were defined using table metadata, or when using the ORM we may be sending ORM-mapped attributes that represent table columns. However, sometimes there is also the need to manufacture arbitrary SQL blocks inside of statements, such as constant string expressions, or just some arbitrary SQL that’s quicker to write literally.

The text() construct introduced at 使用事务和DBAPI can in fact be embedded into a Select construct directly, such as below where we manufacture a hardcoded string literal 'some phrase' and embed it within the SELECT statement:

>>> from sqlalchemy import text
>>> stmt = select(text("'some phrase'"), user_table.c.name).order_by(user_table.c.name)
>>> with engine.connect() as conn:

While the text() construct can be used in most places to inject literal SQL phrases, more often than not we are actually dealing with textual units that each represent an individual column expression. In this common case we can get more functionality out of our textual fragment using the literal_column() construct instead. This object is similar to text() except that instead of representing arbitrary SQL of any form, it explicitly represents a single “column” and can then be labeled and referred towards in subqueries and other expressions:

>>> from sqlalchemy import literal_column
>>> stmt = select(literal_column("'some phrase'").label("p"), user_table.c.name).order_by(

Note that in both cases, when using text() or literal_column(), we are writing a syntactical SQL expression, and not a literal value. We therefore have to include whatever quoting or syntaxes are necessary for the SQL we want to see rendered.

WHERE 子句¶

The WHERE clause

中文

SQLAlchemy 允许我们通过使用标准的 Python 运算符与 Column 和类似对象结合来组合 SQL 表达式，例如 name = 'squidward' 或 user_id > 10 。对于布尔表达式，大多数 Python 运算符如 ==、 !=、 <、 >= 等生成新的 SQL 表达式对象，而不是普通的布尔 True / False 值:

>>> print(user_table.c.name == "squidward")
user_account.name = :name_1

>>> print(address_table.c.user_id > 10)
address.user_id > :user_id_1

我们可以使用这些表达式通过将结果对象传递给 Select.where() 方法来生成 WHERE 子句:

>>> print(select(user_table).where(user_table.c.name=="squidward"))SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = :name_1

要生成由 AND 连接的多个表达式，可以多次调用 Select.where() 方法:

>>> print(...     select(address_table.c.email_address)...     .where(user_table.c.name == "squidward")...     .where(address_table.c.user_id == user_table.c.id)... )SELECT address.email_address
FROM address, user_account
WHERE user_account.name = :name_1 AND address.user_id = user_account.id

对 Select.where() 的单次调用也接受多个表达式，效果相同:

>>> print(...     select(address_table.c.email_address).where(...         user_table.c.name == "squidward",...         address_table.c.user_id == user_table.c.id,...     )... )SELECT address.email_address
FROM address, user_account
WHERE user_account.name = :name_1 AND address.user_id = user_account.id

“AND” 和 “OR” 连接可以直接使用 and_() 和 or_() 函数，如下所示，以 ORM 实体为例:

>>> from sqlalchemy importand_,or_>>> print(...     select(Address.email_address).where(...         and_(...             or_(User.name == "squidward", User.name == "sandy"),...             Address.user_id == User.id,...         )...     )... )SELECT address.email_address
FROM address, user_account
WHERE (user_account.name = :name_1 OR user_account.name = :name_2)
AND address.user_id = user_account.id

对于针对单个实体的简单“相等”比较，还有一种流行的方法称为 Select.filter_by()，它接受与列键或 ORM 属性名称匹配的关键字参数。它将过滤最左侧的 FROM 子句或最后一个连接的实体:

>>> print(select(User).filter_by(name="spongebob",fullname="Spongebob Squarepants"))SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = :name_1 AND user_account.fullname = :fullname_1

参见

操作符参考 - SQLAlchemy 中大多数 SQL 运算符函数的描述

英文

SQLAlchemy allows us to compose SQL expressions, such as name = 'squidward' or user_id > 10, by making use of standard Python operators in conjunction with Column and similar objects. For boolean expressions, most Python operators such as ==, !=, <, >= etc. generate new SQL Expression objects, rather than plain boolean True/False values:

>>> print(user_table.c.name == "squidward")
user_account.name = :name_1

>>> print(address_table.c.user_id > 10)
address.user_id > :user_id_1

We can use expressions like these to generate the WHERE clause by passing the resulting objects to the Select.where() method:

>>> print(select(user_table).where(user_table.c.name=="squidward"))SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = :name_1

To produce multiple expressions joined by AND, the Select.where() method may be invoked any number of times:

>>> print(...     select(address_table.c.email_address)...     .where(user_table.c.name == "squidward")...     .where(address_table.c.user_id == user_table.c.id)... )SELECT address.email_address
FROM address, user_account
WHERE user_account.name = :name_1 AND address.user_id = user_account.id

A single call to Select.where() also accepts multiple expressions with the same effect:

>>> print(...     select(address_table.c.email_address).where(...         user_table.c.name == "squidward",...         address_table.c.user_id == user_table.c.id,...     )... )SELECT address.email_address
FROM address, user_account
WHERE user_account.name = :name_1 AND address.user_id = user_account.id

“AND” and “OR” conjunctions are both available directly using the and_() and or_() functions, illustrated below in terms of ORM entities:

>>> from sqlalchemy importand_,or_>>> print(...     select(Address.email_address).where(...         and_(...             or_(User.name == "squidward", User.name == "sandy"),...             Address.user_id == User.id,...         )...     )... )SELECT address.email_address
FROM address, user_account
WHERE (user_account.name = :name_1 OR user_account.name = :name_2)
AND address.user_id = user_account.id

For simple “equality” comparisons against a single entity, there’s also a popular method known as Select.filter_by() which accepts keyword arguments that match to column keys or ORM attribute names. It will filter against the leftmost FROM clause or the last entity joined:

>>> print(select(User).filter_by(name="spongebob",fullname="Spongebob Squarepants"))SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = :name_1 AND user_account.fullname = :fullname_1

参见

操作符参考 - descriptions of most SQL operator functions in SQLAlchemy

显式 FROM 子句和 JOIN¶

Explicit FROM clauses and JOINs

中文

正如前面提到的，FROM 子句通常是根据我们在列子句中设置的表达式以及 Select 的其他元素推断出来的。

如果我们在 COLUMNS 子句中设置了某个 Table 的单列，它也会将该 Table 放入 FROM 子句中:

>>> print(select(user_table.c.name))SELECT user_account.name
FROM user_account

如果我们将两个表的列放在一起，那么我们会得到一个逗号分隔的 FROM 子句:

>>> print(select(user_table.c.name,address_table.c.email_address))SELECT user_account.name, address.email_address
FROM user_account, address

为了将这两个表连接在一起，我们通常使用 Select 上的两种方法之一。第一种是 Select.join_from() 方法，它允许我们明确表示 JOIN 的左侧和右侧:

>>> print(...     select(user_table.c.name, address_table.c.email_address).join_from(...         user_table, address_table...     )... )SELECT user_account.name, address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id

另一种是 Select.join() 方法，它只表示 JOIN 的右侧，左侧是推断出来的:

>>> print(select(user_table.c.name,address_table.c.email_address).join(address_table))SELECT user_account.name, address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id

我们也可以选择显式地将元素添加到 FROM 子句中，如果它不是我们希望从列子句中推断出来的那样。我们使用 Select.select_from() 方法来实现这一点，如下所示，我们将 user_table 作为 FROM 子句中的第一个元素，并使用 Select.join() 将 address_table 作为第二个元素:

>>> print(select(address_table.c.email_address).select_from(user_table).join(address_table))SELECT address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id

另一个我们可能希望使用 Select.select_from() 的示例是，如果我们的列子句没有足够的信息来提供 FROM 子句。例如，要从常见的 SQL 表达式 count(*) 中 SELECT，我们使用一个称为 sqlalchemy.sql.expression.func 的 SQLAlchemy 元素来生成 SQL count() 函数:

>>> from sqlalchemy importfunc>>> print(select(func.count("*")).select_from(user_table))SELECT count(:count_2) AS count_1
FROM user_account

参见

设置连接中最左边的 FROM 子句 - 在 ORM 查询指南中 - 包含更多示例和注释，讨论 Select.select_from() 和 Select.join() 的交互。

英文

As mentioned previously, the FROM clause is usually inferred based on the expressions that we are setting in the columns clause as well as other elements of the Select.

If we set a single column from a particular Table in the COLUMNS clause, it puts that Table in the FROM clause as well:

>>> print(select(user_table.c.name))SELECT user_account.name
FROM user_account

If we were to put columns from two tables, then we get a comma-separated FROM clause:

>>> print(select(user_table.c.name,address_table.c.email_address))SELECT user_account.name, address.email_address
FROM user_account, address

In order to JOIN these two tables together, we typically use one of two methods on Select. The first is the Select.join_from() method, which allows us to indicate the left and right side of the JOIN explicitly:

>>> print(...     select(user_table.c.name, address_table.c.email_address).join_from(...         user_table, address_table...     )... )SELECT user_account.name, address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id

The other is the Select.join() method, which indicates only the right side of the JOIN, the left hand-side is inferred:

>>> print(select(user_table.c.name,address_table.c.email_address).join(address_table))SELECT user_account.name, address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id

We also have the option to add elements to the FROM clause explicitly, if it is not inferred the way we want from the columns clause. We use the Select.select_from() method to achieve this, as below where we establish user_table as the first element in the FROM clause and Select.join() to establish address_table as the second:

>>> print(select(address_table.c.email_address).select_from(user_table).join(address_table))SELECT address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id

Another example where we might want to use Select.select_from() is if our columns clause doesn’t have enough information to provide for a FROM clause. For example, to SELECT from the common SQL expression count(*), we use a SQLAlchemy element known as sqlalchemy.sql.expression.func to produce the SQL count() function:

>>> from sqlalchemy importfunc>>> print(select(func.count("*")).select_from(user_table))SELECT count(:count_2) AS count_1
FROM user_account

参见

设置连接中最左边的 FROM 子句 - in the ORM 查询指南 - contains additional examples and notes regarding the interaction of Select.select_from() and Select.join().

设置 ON 子句¶

Setting the ON Clause

中文

前面的 JOIN 示例说明了 Select 构造可以在两个表之间进行连接并自动生成 ON 子句。这些示例中发生这种情况是因为 user_table 和 address_table Table 对象包含一个用于形成此 ON 子句的 ForeignKeyConstraint 定义。

如果连接的左右目标没有这样的约束，或者存在多个约束，我们需要直接指定 ON 子句。Select.join() 和 Select.join_from() 都接受一个附加参数用于 ON 子句，该子句使用与 WHERE 子句中相同的 SQL 表达机制来声明:

>>> print(...     select(address_table.c.email_address)...     .select_from(user_table)...     .join(address_table, user_table.c.id == address_table.c.user_id)... )SELECT address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id

ORM 提示 - 使用 relationship() 构造的 ORM 实体时，还有另一种生成 ON 子句的方法，例如在上一节声明映射类中设置的映射。这是一个完整的话题，在使用关系进行连接中有详细介绍。

英文

The previous examples of JOIN illustrated that the Select construct can join between two tables and produce the ON clause automatically. This occurs in those examples because the user_table and address_table Table objects include a single ForeignKeyConstraint definition which is used to form this ON clause.

If the left and right targets of the join do not have such a constraint, or there are multiple constraints in place, we need to specify the ON clause directly. Both Select.join() and Select.join_from() accept an additional argument for the ON clause, which is stated using the same SQL Expression mechanics as we saw about in WHERE 子句:

>>> print(...     select(address_table.c.email_address)...     .select_from(user_table)...     .join(address_table, user_table.c.id == address_table.c.user_id)... )SELECT address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id

ORM Tip - there’s another way to generate the ON clause when using ORM entities that make use of the relationship() construct, like the mapping set up in the previous section at 声明映射类. This is a whole subject onto itself, which is introduced at length at 使用关系进行连接.

OUTER 和 FULL 连接¶

OUTER and FULL join

中文

Select.join() 和 Select.join_from() 方法都接受关键字参数 Select.join.isouter 和 Select.join.full，分别渲染 LEFT OUTER JOIN 和 FULL OUTER JOIN:

>>> print(select(user_table).join(address_table,isouter=True))SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account LEFT OUTER JOIN address ON user_account.id = address.user_id
>>> print(select(user_table).join(address_table,full=True))SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account FULL OUTER JOIN address ON user_account.id = address.user_id

还有一个方法 Select.outerjoin() 等效于使用 .join(..., isouter=True)。

小技巧

SQL 还有一个 “RIGHT OUTER JOIN”。SQLAlchemy 不会直接渲染这个；而是反转表的顺序并使用 “LEFT OUTER JOIN”。

英文

Both the Select.join() and Select.join_from() methods accept keyword arguments Select.join.isouter and Select.join.full which will render LEFT OUTER JOIN and FULL OUTER JOIN, respectively:

>>> print(select(user_table).join(address_table,isouter=True))SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account LEFT OUTER JOIN address ON user_account.id = address.user_id
>>> print(select(user_table).join(address_table,full=True))SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account FULL OUTER JOIN address ON user_account.id = address.user_id

There is also a method Select.outerjoin() that is equivalent to using .join(..., isouter=True).

小技巧

SQL also has a “RIGHT OUTER JOIN”. SQLAlchemy doesn’t render this directly; instead, reverse the order of the tables and use “LEFT OUTER JOIN”.

ORDER BY、GROUP BY、HAVING¶

ORDER BY, GROUP BY, HAVING

中文

SELECT SQL 语句包含一个名为 ORDER BY 的子句，用于在给定的排序中返回选定的行。

GROUP BY 子句的构造类似于 ORDER BY 子句，其目的是将选定的行细分为特定的组，可以在这些组上调用聚合函数。HAVING 子句通常与 GROUP BY 一起使用，其形式类似于 WHERE 子句，只不过它应用于组内使用的聚合函数。

英文

The SELECT SQL statement includes a clause called ORDER BY which is used to return the selected rows within a given ordering.

The GROUP BY clause is constructed similarly to the ORDER BY clause, and has the purpose of sub-dividing the selected rows into specific groups upon which aggregate functions may be invoked. The HAVING clause is usually used with GROUP BY and is of a similar form to the WHERE clause, except that it’s applied to the aggregated functions used within groups.

ORDER BY¶

ORDER BY

中文

ORDER BY 子句是根据通常基于 Column 或类似对象的 SQL 表达式构造的。Select.order_by() 方法按位置接受一个或多个这些表达式:

>>> print(select(user_table).order_by(user_table.c.name))SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account ORDER BY user_account.name

升序 / 降序可以通过 ColumnElement.asc() 和 ColumnElement.desc() 修饰符实现，这些修饰符也存在于 ORM 绑定属性中:

>>> print(select(User).order_by(User.fullname.desc()))SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account ORDER BY user_account.fullname DESC

上述语句将生成按 user_account.fullname 列降序排序的行。

英文

The ORDER BY clause is constructed in terms of SQL Expression constructs typically based on Column or similar objects. The Select.order_by() method accepts one or more of these expressions positionally:

>>> print(select(user_table).order_by(user_table.c.name))SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account ORDER BY user_account.name

Ascending / descending is available from the ColumnElement.asc() and ColumnElement.desc() modifiers, which are present from ORM-bound attributes as well:

>>> print(select(User).order_by(User.fullname.desc()))SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account ORDER BY user_account.fullname DESC

The above statement will yield rows that are sorted by the user_account.fullname column in descending order.

使用 GROUP BY / HAVING 的聚合函数¶

Aggregate functions with GROUP BY / HAVING

中文

在 SQL 中，聚合函数允许跨多行的列表达式聚合在一起以生成单个结果。示例包括计数、计算平均值以及在一组值中查找最大值或最小值。

SQLAlchemy 通过一个称为 func 的命名空间以开放的方式提供 SQL 函数。这是一个特殊的构造对象，当给定特定 SQL 函数的名称时，它将创建 Function 的新实例，该名称可以是任何名称，并且可以传递零个或多个参数给函数，这些参数与所有其他情况一样，都是 SQL 表达式构造。例如，要对 user_account.id 列呈现 SQL COUNT() 函数，我们调用 count() 名称:

>>> from sqlalchemy importfunc>>> count_fn=func.count(user_table.c.id)>>> print(count_fn)count(user_account.id)

SQL 函数在本教程的后面部分使用 SQL 函数中有更详细的描述。

在 SQL 中使用聚合函数时，GROUP BY 子句是必不可少的，因为它允许将行划分为组，每个组将单独应用聚合函数。当在 SELECT 语句的 COLUMNS 子句中请求非聚合列时，SQL 要求这些列都要受到 GROUP BY 子句的约束，无论是直接的还是基于主键关联的间接的。HAVING 子句的使用方式与 WHERE 子句类似，不同之处在于它根据聚合值而不是直接行内容过滤出行。

SQLAlchemy 通过 Select.group_by() 和 Select.having() 方法提供这两个子句。下面我们展示选择用户名字段以及地址计数，对于那些有多个地址的用户：

>>> withengine.connect()asconn:...     result = conn.execute(...         select(User.name, func.count(Address.id).label("count"))...         .join(Address)...         .group_by(User.name)...         .having(func.count(Address.id) > 1)...     )...     print(result.all())BEGIN (implicit)
SELECT user_account.name, count(address.id) AS count
FROM user_account JOIN address ON user_account.id = address.user_id GROUP BY user_account.name
HAVING count(address.id) > ?
[...] (1,)
[('sandy', 2)]ROLLBACK

英文

In SQL, aggregate functions allow column expressions across multiple rows to be aggregated together to produce a single result. Examples include counting, computing averages, as well as locating the maximum or minimum value in a set of values.

SQLAlchemy provides for SQL functions in an open-ended way using a namespace known as func. This is a special constructor object which will create new instances of Function when given the name of a particular SQL function, which can have any name, as well as zero or more arguments to pass to the function, which are, like in all other cases, SQL Expression constructs. For example, to render the SQL COUNT() function against the user_account.id column, we call upon the count() name:

>>> from sqlalchemy importfunc>>> count_fn=func.count(user_table.c.id)>>> print(count_fn)count(user_account.id)

SQL functions are described in more detail later in this tutorial at 使用 SQL 函数.

When using aggregate functions in SQL, the GROUP BY clause is essential in that it allows rows to be partitioned into groups where aggregate functions will be applied to each group individually. When requesting non-aggregated columns in the COLUMNS clause of a SELECT statement, SQL requires that these columns all be subject to a GROUP BY clause, either directly or indirectly based on a primary key association. The HAVING clause is then used in a similar manner as the WHERE clause, except that it filters out rows based on aggregated values rather than direct row contents.

SQLAlchemy provides for these two clauses using the Select.group_by() and Select.having() methods. Below we illustrate selecting user name fields as well as count of addresses, for those users that have more than one address:

>>> withengine.connect()asconn:...     result = conn.execute(...         select(User.name, func.count(Address.id).label("count"))...         .join(Address)...         .group_by(User.name)...         .having(func.count(Address.id) > 1)...     )...     print(result.all())BEGIN (implicit)
SELECT user_account.name, count(address.id) AS count
FROM user_account JOIN address ON user_account.id = address.user_id GROUP BY user_account.name
HAVING count(address.id) > ?
[...] (1,)
[('sandy', 2)]ROLLBACK

按标签排序或分组¶

Ordering or Grouping by a Label

中文

一个重要的技术，尤其是在某些数据库后端，是能够对已经在列子句中声明的表达式进行 ORDER BY 或 GROUP BY，而无需在 ORDER BY 或 GROUP BY 子句中重新声明表达式，而是使用列子句中的列名或标签名。通过将名称的字符串文本传递给 Select.order_by() 或 Select.group_by() 方法，可以实现这种形式。传递的文本 不会直接渲染(not rendered directly) ；相反，列子句中给表达式的名称在上下文中渲染为该表达式的名称，如果没有找到匹配项，则会引发错误。单一修饰符 asc() 和 desc() 也可以以这种形式使用：

>>> from sqlalchemy importfunc,desc>>> stmt=(...     select(Address.user_id, func.count(Address.id).label("num_addresses"))...     .group_by("user_id")...     .order_by("user_id", desc("num_addresses"))... )>>> print(stmt)SELECT address.user_id, count(address.id) AS num_addresses
FROM address GROUP BY address.user_id ORDER BY address.user_id, num_addresses DESC

英文

An important technique, in particular on some database backends, is the ability to ORDER BY or GROUP BY an expression that is already stated in the columns clause, without re-stating the expression in the ORDER BY or GROUP BY clause and instead using the column name or labeled name from the COLUMNS clause. This form is available by passing the string text of the name to the Select.order_by() or Select.group_by() method. The text passed is not rendered directly; instead, the name given to an expression in the columns clause and rendered as that expression name in context, raising an error if no match is found. The unary modifiers asc() and desc() may also be used in this form:

>>> from sqlalchemy importfunc,desc>>> stmt=(...     select(Address.user_id, func.count(Address.id).label("num_addresses"))...     .group_by("user_id")...     .order_by("user_id", desc("num_addresses"))... )>>> print(stmt)SELECT address.user_id, count(address.id) AS num_addresses
FROM address GROUP BY address.user_id ORDER BY address.user_id, num_addresses DESC

使用别名¶

Using Aliases

中文

现在我们从多个表中选择并使用连接时，很快就会遇到需要在语句的 FROM 子句中多次引用同一个表的情况。我们使用 SQL 别名(aliases) 来完成这项工作，这是一种为表或子查询提供替代名称的语法，可以在语句中引用它。

在 SQLAlchemy 表达式语言中，这些“名称”由称为 Alias 构造的 FromClause 对象表示，在 Core 中使用 FromClause.alias() 方法构造。Alias 构造就像 Table 构造一样，它在 Alias.c 集合中也包含一个 Column 对象的命名空间。例如，下面的 SELECT 语句返回所有唯一的用户名对:

>>> user_alias_1=user_table.alias()>>> user_alias_2=user_table.alias()>>> print(...     select(user_alias_1.c.name, user_alias_2.c.name).join_from(...         user_alias_1, user_alias_2, user_alias_1.c.id > user_alias_2.c.id...     )... )SELECT user_account_1.name, user_account_2.name AS name_1
FROM user_account AS user_account_1
JOIN user_account AS user_account_2 ON user_account_1.id > user_account_2.id

英文

Now that we are selecting from multiple tables and using joins, we quickly run into the case where we need to refer to the same table multiple times in the FROM clause of a statement. We accomplish this using SQL aliases, which are a syntax that supplies an alternative name to a table or subquery from which it can be referenced in the statement.

In the SQLAlchemy Expression Language, these “names” are instead represented by FromClause objects known as the Alias construct, which is constructed in Core using the FromClause.alias() method. An Alias construct is just like a Table construct in that it also has a namespace of Column objects within the Alias.c collection. The SELECT statement below for example returns all unique pairs of user names:

>>> user_alias_1=user_table.alias()>>> user_alias_2=user_table.alias()>>> print(...     select(user_alias_1.c.name, user_alias_2.c.name).join_from(...         user_alias_1, user_alias_2, user_alias_1.c.id > user_alias_2.c.id...     )... )SELECT user_account_1.name, user_account_2.name AS name_1
FROM user_account AS user_account_1
JOIN user_account AS user_account_2 ON user_account_1.id > user_account_2.id

ORM 实体别名¶

ORM Entity Aliases

中文

ORM 等效于 FromClause.alias() 方法的是 ORM aliased() 函数，可以应用于 User 和 Address 等实体。这会在内部生成一个针对原始映射 Table 对象的 Alias 对象，同时保持 ORM 功能。下面的 SELECT 语句从 User 实体中选择包含两个特定电子邮件地址的所有对象:

>>> from sqlalchemy.orm importaliased>>> address_alias_1=aliased(Address)>>> address_alias_2=aliased(Address)>>> print(...     select(User)...     .join_from(User, address_alias_1)...     .where(address_alias_1.email_address == "patrick@aol.com")...     .join_from(User, address_alias_2)...     .where(address_alias_2.email_address == "patrick@gmail.com")... )SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
JOIN address AS address_1 ON user_account.id = address_1.user_id
JOIN address AS address_2 ON user_account.id = address_2.user_id
WHERE address_1.email_address = :email_address_1
AND address_2.email_address = :email_address_2

小技巧

如设置 ON 子句中所述，ORM 提供了另一种使用 relationship() 构造进行连接的方法。上述使用别名的示例在使用关系在别名目标之间进行连接中使用 relationship() 进行演示。

英文

The ORM equivalent of the FromClause.alias() method is the ORM aliased() function, which may be applied to an entity such as User and Address. This produces a Alias object internally that’s against the original mapped Table object, while maintaining ORM functionality. The SELECT below selects from the User entity all objects that include two particular email addresses:

>>> from sqlalchemy.orm importaliased>>> address_alias_1=aliased(Address)>>> address_alias_2=aliased(Address)>>> print(...     select(User)...     .join_from(User, address_alias_1)...     .where(address_alias_1.email_address == "patrick@aol.com")...     .join_from(User, address_alias_2)...     .where(address_alias_2.email_address == "patrick@gmail.com")... )SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
JOIN address AS address_1 ON user_account.id = address_1.user_id
JOIN address AS address_2 ON user_account.id = address_2.user_id
WHERE address_1.email_address = :email_address_1
AND address_2.email_address = :email_address_2

小技巧

As mentioned in 设置 ON 子句, the ORM provides for another way to join using the relationship() construct. The above example using aliases is demonstrated using relationship() at 使用关系在别名目标之间进行连接.

子查询和 CTE¶

Subqueries and CTEs

中文

SQL 中的子查询是一个在括号内呈现的 SELECT 语句，并放置在封闭语句的上下文中，通常是 SELECT 语句，但不一定是。

本节将介绍所谓的“非标量”子查询，通常放在封闭 SELECT 的 FROM 子句中。我们还将介绍公共表表达式（CTE），它的用法与子查询类似，但包含额外的功能。

SQLAlchemy 使用 Subquery 对象来表示子查询，使用 CTE 来表示 CTE，通常分别从 Select.subquery() 和 Select.cte() 方法中获得。任何一个对象都可以作为较大的 select() 构造中的 FROM 元素使用。

我们可以构造一个 Subquery 来选择 address 表中的行的聚合计数（聚合函数和 GROUP BY 在使用 GROUP BY / HAVING 的聚合函数中已介绍）：

>>> subq = (
...     select(func.count(address_table.c.id).label("count"), address_table.c.user_id)
...     .group_by(address_table.c.user_id)
...     .subquery()
... )

单独将子查询字符串化，而不是嵌入另一个 Select 或其他语句中，会生成没有任何括号的普通 SELECT 语句:

>>> print(subq)SELECT count(address.id) AS count, address.user_id
FROM address GROUP BY address.user_id

Subquery 对象的行为类似于任何其他 FROM 对象，如 Table，尤其是它包含一个选中列的 Subquery.c 命名空间。我们可以使用这个命名空间来引用 user_id 列以及我们自定义标签的 count 表达式:

>>> print(select(subq.c.user_id,subq.c.count))SELECT anon_1.user_id, anon_1.count
FROM (SELECT count(address.id) AS count, address.user_id AS user_id
FROM address GROUP BY address.user_id) AS anon_1

在 subq 对象中包含的行选择中，我们可以将对象应用于更大的 Select，将数据连接到 user_account 表中:

>>> stmt=select(user_table.c.name,user_table.c.fullname,subq.c.count).join_from(...     user_table, subq... )>>> print(stmt)SELECT user_account.name, user_account.fullname, anon_1.count
FROM user_account JOIN (SELECT count(address.id) AS count, address.user_id AS user_id
FROM address GROUP BY address.user_id) AS anon_1 ON user_account.id = anon_1.user_id

为了从 user_account 连接到 address，我们使用了 Select.join_from() 方法。如前所述，此连接的 ON 子句再次基于外键约束 推断(inferred) 。尽管 SQL 子查询本身没有任何约束，SQLAlchemy 可以通过确定 subq.c.user_id 列是从 address_table.c.user_id 列 派生(derived) 的来作用于表示在列上的约束，该列确实表达了指回 user_table.c.id 列的外键关系，然后用于生成 ON 子句。

英文

A subquery in SQL is a SELECT statement that is rendered within parenthesis and placed within the context of an enclosing statement, typically a SELECT statement but not necessarily.

This section will cover a so-called “non-scalar” subquery, which is typically placed in the FROM clause of an enclosing SELECT. We will also cover the Common Table Expression or CTE, which is used in a similar way as a subquery, but includes additional features.

SQLAlchemy uses the Subquery object to represent a subquery and the CTE to represent a CTE, usually obtained from the Select.subquery() and Select.cte() methods, respectively. Either object can be used as a FROM element inside of a larger select() construct.

We can construct a Subquery that will select an aggregate count of rows from the address table (aggregate functions and GROUP BY were introduced previously at 使用 GROUP BY / HAVING 的聚合函数):

>>> subq = (
...     select(func.count(address_table.c.id).label("count"), address_table.c.user_id)
...     .group_by(address_table.c.user_id)
...     .subquery()
... )

Stringifying the subquery by itself without it being embedded inside of another Select or other statement produces the plain SELECT statement without any enclosing parenthesis:

>>> print(subq)SELECT count(address.id) AS count, address.user_id
FROM address GROUP BY address.user_id

The Subquery object behaves like any other FROM object such as a Table, notably that it includes a Subquery.c namespace of the columns which it selects. We can use this namespace to refer to both the user_id column as well as our custom labeled count expression:

>>> print(select(subq.c.user_id,subq.c.count))SELECT anon_1.user_id, anon_1.count
FROM (SELECT count(address.id) AS count, address.user_id AS user_id
FROM address GROUP BY address.user_id) AS anon_1

With a selection of rows contained within the subq object, we can apply the object to a larger Select that will join the data to the user_account table:

>>> stmt=select(user_table.c.name,user_table.c.fullname,subq.c.count).join_from(...     user_table, subq... )>>> print(stmt)SELECT user_account.name, user_account.fullname, anon_1.count
FROM user_account JOIN (SELECT count(address.id) AS count, address.user_id AS user_id
FROM address GROUP BY address.user_id) AS anon_1 ON user_account.id = anon_1.user_id

In order to join from user_account to address, we made use of the Select.join_from() method. As has been illustrated previously, the ON clause of this join was again inferred based on foreign key constraints. Even though a SQL subquery does not itself have any constraints, SQLAlchemy can act upon constraints represented on the columns by determining that the subq.c.user_id column is derived from the address_table.c.user_id column, which does express a foreign key relationship back to the user_table.c.id column which is then used to generate the ON clause.

通用表表达式 (CTE)¶

Common Table Expressions (CTEs)

中文

在 SQLAlchemy 中，CTE 构造的用法与 Subquery 构造几乎相同。通过将 Select.subquery() 方法的调用更改为使用 Select.cte()，我们可以以相同的方式使用生成的对象作为 FROM 元素，但渲染的 SQL 是非常不同的公共表表达式语法:

>>> subq=(...     select(func.count(address_table.c.id).label("count"), address_table.c.user_id)...     .group_by(address_table.c.user_id)...     .cte()... )>>> stmt=select(user_table.c.name,user_table.c.fullname,subq.c.count).join_from(...     user_table, subq... )>>> print(stmt)WITH anon_1 AS
(SELECT count(address.id) AS count, address.user_id AS user_id
FROM address GROUP BY address.user_id)
SELECT user_account.name, user_account.fullname, anon_1.count
FROM user_account JOIN anon_1 ON user_account.id = anon_1.user_id

CTE 构造还具有以“递归”样式使用的能力，并且在更复杂的情况下可以从 INSERT、UPDATE 或 DELETE 语句的 RETURNING 子句中组成。CTE 的文档字符串中包含有关这些附加模式的详细信息。

在这两种情况下，子查询和 CTE 在 SQL 级别使用“匿名”名称命名。在 Python 代码中，我们根本不需要提供这些名称。Subquery 或 CTE 实例的对象标识在渲染时作为对象的语法标识。如果传递名称作为 Select.subquery() 或 Select.cte() 方法的第一个参数，则可以在 SQL 中渲染该名称。

参见

Select.subquery() - 有关子查询的更多详细信息

Select.cte() - 包括如何使用 RECURSIVE 以及面向 DML 的 CTE 的示例

英文

Usage of the CTE construct in SQLAlchemy is virtually the same as how the Subquery construct is used. By changing the invocation of the Select.subquery() method to use Select.cte() instead, we can use the resulting object as a FROM element in the same way, but the SQL rendered is the very different common table expression syntax:

>>> subq=(...     select(func.count(address_table.c.id).label("count"), address_table.c.user_id)...     .group_by(address_table.c.user_id)...     .cte()... )>>> stmt=select(user_table.c.name,user_table.c.fullname,subq.c.count).join_from(...     user_table, subq... )>>> print(stmt)WITH anon_1 AS
(SELECT count(address.id) AS count, address.user_id AS user_id
FROM address GROUP BY address.user_id)
SELECT user_account.name, user_account.fullname, anon_1.count
FROM user_account JOIN anon_1 ON user_account.id = anon_1.user_id

The CTE construct also features the ability to be used in a “recursive” style, and may in more elaborate cases be composed from the RETURNING clause of an INSERT, UPDATE or DELETE statement. The docstring for CTE includes details on these additional patterns.

In both cases, the subquery and CTE were named at the SQL level using an “anonymous” name. In the Python code, we don’t need to provide these names at all. The object identity of the Subquery or CTE instances serves as the syntactical identity of the object when rendered. A name that will be rendered in the SQL can be provided by passing it as the first argument of the Select.subquery() or Select.cte() methods.

参见

Select.subquery() - further detail on subqueries

Select.cte() - examples for CTE including how to use RECURSIVE as well as DML-oriented CTEs

ORM 实体子查询/CTE¶

ORM Entity Subqueries/CTEs

中文

在 ORM 中，aliased() 构造可用于将 ORM 实体（例如我们的 User 或 Address 类）与表示行源的任何 FromClause 概念关联。前一节 ORM 实体别名说明了使用 aliased() 将映射类与其映射的 Table 的 Alias 关联。在这里，我们说明 aliased() 对 Subquery 和 CTE 的应用，这些都是针对 Select 构造生成的，最终派生自相同的映射 Table。

下面是将 aliased() 应用于 Subquery 构造的示例，以便从其行中提取 ORM 实体。结果显示了一系列 User 和 Address 对象，其中每个 Address 对象的数据最终来自对 address 表的子查询，而不是直接来自该表：

>>> subq=select(Address).where(~Address.email_address.like("%@aol.com")).subquery()>>> address_subq=aliased(Address,subq)>>> stmt=(...     select(User, address_subq)...     .join_from(User, address_subq)...     .order_by(User.id, address_subq.id)... )>>> withSession(engine)assession:...     for user, address in session.execute(stmt):...         print(f"{user} {address}")BEGIN (implicit)
SELECT user_account.id, user_account.name, user_account.fullname,
anon_1.id AS id_1, anon_1.email_address, anon_1.user_id
FROM user_account JOIN
(SELECT address.id AS id, address.email_address AS email_address, address.user_id AS user_id
FROM address
WHERE address.email_address NOT LIKE ?) AS anon_1 ON user_account.id = anon_1.user_id
ORDER BY user_account.id, anon_1.id
[...] ('%@aol.com',)
User(id=1, name='spongebob', fullname='Spongebob Squarepants') Address(id=1, email_address='spongebob@sqlalchemy.org')User(id=2, name='sandy', fullname='Sandy Cheeks') Address(id=2, email_address='sandy@sqlalchemy.org')User(id=2, name='sandy', fullname='Sandy Cheeks') Address(id=3, email_address='sandy@squirrelpower.org')ROLLBACK

下面是一个相同的示例，只是它使用 CTE 构造：

>>> cte_obj=select(Address).where(~Address.email_address.like("%@aol.com")).cte()>>> address_cte=aliased(Address,cte_obj)>>> stmt=(...     select(User, address_cte)...     .join_from(User, address_cte)...     .order_by(User.id, address_cte.id)... )>>> withSession(engine)assession:...     for user, address in session.execute(stmt):...         print(f"{user} {address}")BEGIN (implicit)
WITH anon_1 AS
(SELECT address.id AS id, address.email_address AS email_address, address.user_id AS user_id
FROM address
WHERE address.email_address NOT LIKE ?)
SELECT user_account.id, user_account.name, user_account.fullname,
anon_1.id AS id_1, anon_1.email_address, anon_1.user_id
FROM user_account
JOIN anon_1 ON user_account.id = anon_1.user_id
ORDER BY user_account.id, anon_1.id
[...] ('%@aol.com',)
User(id=1, name='spongebob', fullname='Spongebob Squarepants') Address(id=1, email_address='spongebob@sqlalchemy.org')User(id=2, name='sandy', fullname='Sandy Cheeks') Address(id=2, email_address='sandy@sqlalchemy.org')User(id=2, name='sandy', fullname='Sandy Cheeks') Address(id=3, email_address='sandy@squirrelpower.org')ROLLBACK

参见

从子查询中选择实体 - 在 ORM 查询指南中

英文

In the ORM, the aliased() construct may be used to associate an ORM entity, such as our User or Address class, with any FromClause concept that represents a source of rows. The preceding section ORM 实体别名 illustrates using aliased() to associate the mapped class with an Alias of its mapped Table. Here we illustrate aliased() doing the same thing against both a Subquery as well as a CTE generated against a Select construct, that ultimately derives from that same mapped Table.

Below is an example of applying aliased() to the Subquery construct, so that ORM entities can be extracted from its rows. The result shows a series of User and Address objects, where the data for each Address object ultimately came from a subquery against the address table rather than that table directly:

>>> subq=select(Address).where(~Address.email_address.like("%@aol.com")).subquery()>>> address_subq=aliased(Address,subq)>>> stmt=(...     select(User, address_subq)...     .join_from(User, address_subq)...     .order_by(User.id, address_subq.id)... )>>> withSession(engine)assession:...     for user, address in session.execute(stmt):...         print(f"{user} {address}")BEGIN (implicit)
SELECT user_account.id, user_account.name, user_account.fullname,
anon_1.id AS id_1, anon_1.email_address, anon_1.user_id
FROM user_account JOIN
(SELECT address.id AS id, address.email_address AS email_address, address.user_id AS user_id
FROM address
WHERE address.email_address NOT LIKE ?) AS anon_1 ON user_account.id = anon_1.user_id
ORDER BY user_account.id, anon_1.id
[...] ('%@aol.com',)
User(id=1, name='spongebob', fullname='Spongebob Squarepants') Address(id=1, email_address='spongebob@sqlalchemy.org')User(id=2, name='sandy', fullname='Sandy Cheeks') Address(id=2, email_address='sandy@sqlalchemy.org')User(id=2, name='sandy', fullname='Sandy Cheeks') Address(id=3, email_address='sandy@squirrelpower.org')ROLLBACK

Another example follows, which is exactly the same except it makes use of the CTE construct instead:

>>> cte_obj=select(Address).where(~Address.email_address.like("%@aol.com")).cte()>>> address_cte=aliased(Address,cte_obj)>>> stmt=(...     select(User, address_cte)...     .join_from(User, address_cte)...     .order_by(User.id, address_cte.id)... )>>> withSession(engine)assession:...     for user, address in session.execute(stmt):...         print(f"{user} {address}")BEGIN (implicit)
WITH anon_1 AS
(SELECT address.id AS id, address.email_address AS email_address, address.user_id AS user_id
FROM address
WHERE address.email_address NOT LIKE ?)
SELECT user_account.id, user_account.name, user_account.fullname,
anon_1.id AS id_1, anon_1.email_address, anon_1.user_id
FROM user_account
JOIN anon_1 ON user_account.id = anon_1.user_id
ORDER BY user_account.id, anon_1.id
[...] ('%@aol.com',)
User(id=1, name='spongebob', fullname='Spongebob Squarepants') Address(id=1, email_address='spongebob@sqlalchemy.org')User(id=2, name='sandy', fullname='Sandy Cheeks') Address(id=2, email_address='sandy@sqlalchemy.org')User(id=2, name='sandy', fullname='Sandy Cheeks') Address(id=3, email_address='sandy@squirrelpower.org')ROLLBACK

参见

从子查询中选择实体 - in the ORM 查询指南

标量和相关子查询¶

Scalar and Correlated Subqueries

中文

标量子查询是一个返回零或一行且仅包含一列的子查询。然后，该子查询在封闭的 SELECT 语句的 COLUMNS 或 WHERE 子句中使用，与常规子查询不同，它不在 FROM 子句中使用。相关子查询是一个标量子查询，它引用封闭 SELECT 语句中的表。

SQLAlchemy 使用 ScalarSelect 构造来表示标量子查询，它是 ColumnElement 表达式层次结构的一部分，而常规子查询由 Subquery 构造表示，它属于 FromClause 层次结构。

标量子查询通常（但不一定）与聚合函数一起使用，前面在使用 GROUP BY / HAVING 的聚合函数中介绍过。通过使用 Select.scalar_subquery() 方法明确指示标量子查询，如下所示。其默认字符串形式在单独字符串化时呈现为从两个表中选择的普通 SELECT 语句:

>>> subq=(...     select(func.count(address_table.c.id))...     .where(user_table.c.id == address_table.c.user_id)...     .scalar_subquery()... )>>> print(subq)(SELECT count(address.id) AS count_1
FROM address, user_account
WHERE user_account.id = address.user_id)

上述 subq 对象现在位于 ColumnElement SQL 表达式层次结构中，可以像任何其他列表达式一样使用:

>>> print(subq==5)(SELECT count(address.id) AS count_1
FROM address, user_account
WHERE user_account.id = address.user_id) = :param_1

尽管标量子查询本身在字符串化时会在其 FROM 子句中呈现 user_account 和 address，但当将其嵌入到处理 user_account 表的封闭 select() 构造中时，user_account 表会自动 关联(correlated) ，这意味着它不会在子查询的 FROM 子句中呈现:

>>> stmt=select(user_table.c.name,subq.label("address_count"))>>> print(stmt)SELECT user_account.name, (SELECT count(address.id) AS count_1
FROM address
WHERE user_account.id = address.user_id) AS address_count
FROM user_account

简单的相关子查询通常会做出所需的正确操作。然而，在关联不明确的情况下，SQLAlchemy 会让我们知道需要更多的清晰度:

>>> stmt = (
...     select(
...         user_table.c.name,
...         address_table.c.email_address,
...         subq.label("address_count"),
...     )
...     .join_from(user_table, address_table)
...     .order_by(user_table.c.id, address_table.c.id)
... )
>>> print(stmt)
Traceback (most recent call last):
...
InvalidRequestError: Select statement '<... Select object at ...>' returned
no FROM clauses due to auto-correlation; specify correlate(<tables>) to
control correlation manually.

要指定我们希望关联的表是 user_table，我们可以使用 ScalarSelect.correlate() 或 ScalarSelect.correlate_except() 方法:

>>> subq = (
...     select(func.count(address_table.c.id))
...     .where(user_table.c.id == address_table.c.user_id)
...     .scalar_subquery()
...     .correlate(user_table)
... )

然后，该语句可以像任何其他列一样返回此列的数据：

>>> withengine.connect()asconn:...     result = conn.execute(...         select(...             user_table.c.name,...             address_table.c.email_address,...             subq.label("address_count"),...         )...         .join_from(user_table, address_table)...         .order_by(user_table.c.id, address_table.c.id)...     )...     print(result.all())BEGIN (implicit)
SELECT user_account.name, address.email_address, (SELECT count(address.id) AS count_1
FROM address
WHERE user_account.id = address.user_id) AS address_count
FROM user_account JOIN address ON user_account.id = address.user_id ORDER BY user_account.id, address.id
[...] ()
[('spongebob', 'spongebob@sqlalchemy.org', 1), ('sandy', 'sandy@sqlalchemy.org', 2),('sandy', 'sandy@squirrelpower.org', 2)]ROLLBACK

英文

A scalar subquery is a subquery that returns exactly zero or one row and exactly one column. The subquery is then used in the COLUMNS or WHERE clause of an enclosing SELECT statement and is different than a regular subquery in that it is not used in the FROM clause. A correlated subquery is a scalar subquery that refers to a table in the enclosing SELECT statement.

SQLAlchemy represents the scalar subquery using the ScalarSelect construct, which is part of the ColumnElement expression hierarchy, in contrast to the regular subquery which is represented by the Subquery construct, which is in the FromClause hierarchy.

Scalar subqueries are often, but not necessarily, used with aggregate functions, introduced previously at 使用 GROUP BY / HAVING 的聚合函数. A scalar subquery is indicated explicitly by making use of the Select.scalar_subquery() method as below. It’s default string form when stringified by itself renders as an ordinary SELECT statement that is selecting from two tables:

>>> subq=(...     select(func.count(address_table.c.id))...     .where(user_table.c.id == address_table.c.user_id)...     .scalar_subquery()... )>>> print(subq)(SELECT count(address.id) AS count_1
FROM address, user_account
WHERE user_account.id = address.user_id)

The above subq object now falls within the ColumnElement SQL expression hierarchy, in that it may be used like any other column expression:

>>> print(subq==5)(SELECT count(address.id) AS count_1
FROM address, user_account
WHERE user_account.id = address.user_id) = :param_1

Although the scalar subquery by itself renders both user_account and address in its FROM clause when stringified by itself, when embedding it into an enclosing select() construct that deals with the user_account table, the user_account table is automatically correlated, meaning it does not render in the FROM clause of the subquery:

>>> stmt=select(user_table.c.name,subq.label("address_count"))>>> print(stmt)SELECT user_account.name, (SELECT count(address.id) AS count_1
FROM address
WHERE user_account.id = address.user_id) AS address_count
FROM user_account

Simple correlated subqueries will usually do the right thing that’s desired. However, in the case where the correlation is ambiguous, SQLAlchemy will let us know that more clarity is needed:

>>> stmt = (
...     select(
...         user_table.c.name,
...         address_table.c.email_address,
...         subq.label("address_count"),
...     )
...     .join_from(user_table, address_table)
...     .order_by(user_table.c.id, address_table.c.id)
... )
>>> print(stmt)
Traceback (most recent call last):
...
InvalidRequestError: Select statement '<... Select object at ...>' returned
no FROM clauses due to auto-correlation; specify correlate(<tables>) to
control correlation manually.

To specify that the user_table is the one we seek to correlate we specify this using the ScalarSelect.correlate() or ScalarSelect.correlate_except() methods:

>>> subq = (
...     select(func.count(address_table.c.id))
...     .where(user_table.c.id == address_table.c.user_id)
...     .scalar_subquery()
...     .correlate(user_table)
... )

The statement then can return the data for this column like any other:

>>> withengine.connect()asconn:...     result = conn.execute(...         select(...             user_table.c.name,...             address_table.c.email_address,...             subq.label("address_count"),...         )...         .join_from(user_table, address_table)...         .order_by(user_table.c.id, address_table.c.id)...     )...     print(result.all())BEGIN (implicit)
SELECT user_account.name, address.email_address, (SELECT count(address.id) AS count_1
FROM address
WHERE user_account.id = address.user_id) AS address_count
FROM user_account JOIN address ON user_account.id = address.user_id ORDER BY user_account.id, address.id
[...] ()
[('spongebob', 'spongebob@sqlalchemy.org', 1), ('sandy', 'sandy@sqlalchemy.org', 2),('sandy', 'sandy@squirrelpower.org', 2)]ROLLBACK

LATERAL 相关¶

LATERAL correlation

中文

LATERAL 关联是 SQL 关联的一个特殊子类别，允许选择单元在单个 FROM 子句中引用另一个选择单元。这是一个非常特殊的用例，虽然是 SQL 标准的一部分，但已知只有 PostgreSQL 的最新版本支持。

通常，如果 SELECT 语句在其 FROM 子句中引用 table1 JOIN (SELECT ...) AS subquery，则右侧的子查询可能不会引用左侧的 “table1” 表达式；关联可能只引用完全封闭此 SELECT 的另一个 SELECT 的表。LATERAL 关键字允许我们改变这种行为，并允许从右侧 JOIN 进行关联。

SQLAlchemy 使用 Select.lateral() 方法支持此功能，该方法创建一个称为 Lateral 的对象。Lateral 属于与 Subquery 和 Alias 相同的家族，但在将构造添加到封闭 SELECT 的 FROM 子句时，也包括关联行为。以下示例说明了一个使用 LATERAL 的 SQL 查询，选择“用户账户 / 电子邮件地址计数”数据，如前一节所述:

>>> subq=(...     select(...         func.count(address_table.c.id).label("address_count"),...         address_table.c.email_address,...         address_table.c.user_id,...     )...     .where(user_table.c.id == address_table.c.user_id)...     .lateral()... )>>> stmt=(...     select(user_table.c.name, subq.c.address_count, subq.c.email_address)...     .join_from(user_table, subq)...     .order_by(user_table.c.id, subq.c.email_address)... )>>> print(stmt)SELECT user_account.name, anon_1.address_count, anon_1.email_address
FROM user_account
JOIN LATERAL (SELECT count(address.id) AS address_count,
address.email_address AS email_address, address.user_id AS user_id
FROM address
WHERE user_account.id = address.user_id) AS anon_1
ON user_account.id = anon_1.user_id
ORDER BY user_account.id, anon_1.email_address

上述示例中，JOIN 的右侧是一个关联到 JOIN 左侧 user_account 表的子查询。

使用 Select.lateral() 时，Select.correlate() 和 Select.correlate_except() 方法的行为也适用于 Lateral 构造。

参见

Lateral

Select.lateral()

英文

LATERAL correlation is a special sub-category of SQL correlation which allows a selectable unit to refer to another selectable unit within a single FROM clause. This is an extremely special use case which, while part of the SQL standard, is only known to be supported by recent versions of PostgreSQL.

Normally, if a SELECT statement refers to table1 JOIN (SELECT ...) AS subquery in its FROM clause, the subquery on the right side may not refer to the “table1” expression from the left side; correlation may only refer to a table that is part of another SELECT that entirely encloses this SELECT. The LATERAL keyword allows us to turn this behavior around and allow correlation from the right side JOIN.

SQLAlchemy supports this feature using the Select.lateral() method, which creates an object known as Lateral. Lateral is in the same family as Subquery and Alias, but also includes correlation behavior when the construct is added to the FROM clause of an enclosing SELECT. The following example illustrates a SQL query that makes use of LATERAL, selecting the “user account / count of email address” data as was discussed in the previous section:

>>> subq=(...     select(...         func.count(address_table.c.id).label("address_count"),...         address_table.c.email_address,...         address_table.c.user_id,...     )...     .where(user_table.c.id == address_table.c.user_id)...     .lateral()... )>>> stmt=(...     select(user_table.c.name, subq.c.address_count, subq.c.email_address)...     .join_from(user_table, subq)...     .order_by(user_table.c.id, subq.c.email_address)... )>>> print(stmt)SELECT user_account.name, anon_1.address_count, anon_1.email_address
FROM user_account
JOIN LATERAL (SELECT count(address.id) AS address_count,
address.email_address AS email_address, address.user_id AS user_id
FROM address
WHERE user_account.id = address.user_id) AS anon_1
ON user_account.id = anon_1.user_id
ORDER BY user_account.id, anon_1.email_address

Above, the right side of the JOIN is a subquery that correlates to the user_account table that’s on the left side of the join.

When using Select.lateral(), the behavior of Select.correlate() and Select.correlate_except() methods is applied to the Lateral construct as well.

参见

Lateral

Select.lateral()

UNION、UNION ALL 和其他集合操作¶

UNION, UNION ALL and other set operations

中文

在 SQL 中，SELECT 语句可以使用 UNION 或 UNION ALL SQL 操作合并在一起，从而生成一个或多个语句共同生成的所有行的集合。其他集合操作如 INTERSECT [ALL] 和 EXCEPT [ALL] 也是可能的。

SQLAlchemy 的 Select 构造通过函数如 union()、intersect() 和 except_() 以及它们的“all”对应物 union_all()、intersect_all() 和 except_all() 支持这种性质的组合。这些函数都接受任意数量的子选择项，通常是 Select 构造，但也可以是现有的组合。

这些函数生成的构造是 CompoundSelect，其使用方式与 Select 构造相同，只是它的方法较少。例如，由 union_all() 生成的 CompoundSelect 可以直接使用 Connection.execute() 调用:

>>> from sqlalchemy importunion_all>>> stmt1=select(user_table).where(user_table.c.name=="sandy")>>> stmt2=select(user_table).where(user_table.c.name=="spongebob")>>> u=union_all(stmt1,stmt2)>>> withengine.connect()asconn:...     result = conn.execute(u)...     print(result.all())BEGIN (implicit)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = ?
UNION ALL SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = ?
[generated in ...] ('sandy', 'spongebob')
[(2, 'sandy', 'Sandy Cheeks'), (1, 'spongebob', 'Spongebob Squarepants')]ROLLBACK

要将 CompoundSelect 用作子查询，就像 Select 一样，它提供了一个 SelectBase.subquery() 方法，该方法将生成一个 Subquery 对象，并带有一个可以在封闭的 select() 中引用的 FromClause.c 集合:

>>> u_subq=u.subquery()>>> stmt=(...     select(u_subq.c.name, address_table.c.email_address)...     .join_from(address_table, u_subq)...     .order_by(u_subq.c.name, address_table.c.email_address)... )>>> withengine.connect()asconn:...     result = conn.execute(stmt)...     print(result.all())BEGIN (implicit)
SELECT anon_1.name, address.email_address
FROM address JOIN
(SELECT user_account.id AS id, user_account.name AS name, user_account.fullname AS fullname
FROM user_account
WHERE user_account.name = ?
UNION ALL
SELECT user_account.id AS id, user_account.name AS name, user_account.fullname AS fullname
FROM user_account
WHERE user_account.name = ?)
AS anon_1 ON anon_1.id = address.user_id
ORDER BY anon_1.name, address.email_address
[generated in ...] ('sandy', 'spongebob')
[('sandy', 'sandy@sqlalchemy.org'), ('sandy', 'sandy@squirrelpower.org'), ('spongebob', 'spongebob@sqlalchemy.org')]ROLLBACK

英文

In SQL, SELECT statements can be merged together using the UNION or UNION ALL SQL operation, which produces the set of all rows produced by one or more statements together. Other set operations such as INTERSECT [ALL] and EXCEPT [ALL] are also possible.

SQLAlchemy’s Select construct supports compositions of this nature using functions like union(), intersect() and except_(), and the “all” counterparts union_all(), intersect_all() and except_all(). These functions all accept an arbitrary number of sub-selectables, which are typically Select constructs but may also be an existing composition.

The construct produced by these functions is the CompoundSelect, which is used in the same manner as the Select construct, except that it has fewer methods. The CompoundSelect produced by union_all() for example may be invoked directly using Connection.execute():

>>> from sqlalchemy importunion_all>>> stmt1=select(user_table).where(user_table.c.name=="sandy")>>> stmt2=select(user_table).where(user_table.c.name=="spongebob")>>> u=union_all(stmt1,stmt2)>>> withengine.connect()asconn:...     result = conn.execute(u)...     print(result.all())BEGIN (implicit)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = ?
UNION ALL SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = ?
[generated in ...] ('sandy', 'spongebob')
[(2, 'sandy', 'Sandy Cheeks'), (1, 'spongebob', 'Spongebob Squarepants')]ROLLBACK

To use a CompoundSelect as a subquery, just like Select it provides a SelectBase.subquery() method which will produce a Subquery object with a FromClause.c collection that may be referenced in an enclosing select():

>>> u_subq=u.subquery()>>> stmt=(...     select(u_subq.c.name, address_table.c.email_address)...     .join_from(address_table, u_subq)...     .order_by(u_subq.c.name, address_table.c.email_address)... )>>> withengine.connect()asconn:...     result = conn.execute(stmt)...     print(result.all())BEGIN (implicit)
SELECT anon_1.name, address.email_address
FROM address JOIN
(SELECT user_account.id AS id, user_account.name AS name, user_account.fullname AS fullname
FROM user_account
WHERE user_account.name = ?
UNION ALL
SELECT user_account.id AS id, user_account.name AS name, user_account.fullname AS fullname
FROM user_account
WHERE user_account.name = ?)
AS anon_1 ON anon_1.id = address.user_id
ORDER BY anon_1.name, address.email_address
[generated in ...] ('sandy', 'spongebob')
[('sandy', 'sandy@sqlalchemy.org'), ('sandy', 'sandy@squirrelpower.org'), ('spongebob', 'spongebob@sqlalchemy.org')]ROLLBACK

从联合中选择 ORM 实体¶

Selecting ORM Entities from Unions

中文

前面的示例说明了如何构造给定两个 Table 对象的 UNION，以返回数据库行。如果我们想使用 UNION 或其他集合操作选择行，然后作为 ORM 对象接收它们，有两种方法可以使用。在这两种情况下，我们首先构造一个表示我们要执行的 SELECT / UNION / 等语句的 select() 或 CompoundSelect 对象；该语句应针对目标 ORM 实体或其底层映射的 Table 对象组成:

>>> stmt1 = select(User).where(User.name == "sandy")
>>> stmt2 = select(User).where(User.name == "spongebob")
>>> u = union_all(stmt1, stmt2)

对于未嵌套在子查询内的简单 SELECT with UNION，通常可以使用 Select.from_statement() 方法在 ORM 对象获取上下文中使用它们。通过这种方法，UNION 语句表示整个查询；在使用 Select.from_statement() 后不能添加其他条件:

>>> orm_stmt=select(User).from_statement(u)>>> withSession(engine)assession:...     for obj in session.execute(orm_stmt).scalars():...         print(obj)BEGIN (implicit)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = ? UNION ALL SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = ?
[generated in ...] ('sandy', 'spongebob')
User(id=2, name='sandy', fullname='Sandy Cheeks')User(id=1, name='spongebob', fullname='Spongebob Squarepants')ROLLBACK

要以更灵活的方式使用 UNION 或其他集合相关构造作为实体相关组件，可以使用 CompoundSelect.subquery() 方法将 CompoundSelect 构造组织成子查询，然后使用 aliased() 函数链接到 ORM 对象。这与 ORM 实体子查询/CTE 中介绍的方式相同，首先创建我们所需实体到子查询的临时“映射”，然后像选择其他映射类一样从该新实体中选择。在下面的示例中，我们可以在 UNION 本身之外添加其他条件（如 ORDER BY），因为我们可以过滤或按子查询导出的列排序:

>>> user_alias=aliased(User,u.subquery())>>> orm_stmt=select(user_alias).order_by(user_alias.id)>>> withSession(engine)assession:...     for obj in session.execute(orm_stmt).scalars():...         print(obj)BEGIN (implicit)
SELECT anon_1.id, anon_1.name, anon_1.fullname
FROM (SELECT user_account.id AS id, user_account.name AS name, user_account.fullname AS fullname
FROM user_account
WHERE user_account.name = ? UNION ALL SELECT user_account.id AS id, user_account.name AS name, user_account.fullname AS fullname
FROM user_account
WHERE user_account.name = ?) AS anon_1 ORDER BY anon_1.id
[generated in ...] ('sandy', 'spongebob')
User(id=1, name='spongebob', fullname='Spongebob Squarepants')User(id=2, name='sandy', fullname='Sandy Cheeks')ROLLBACK

参见

从 UNION 和其他集合操作中选择实体 - 在 ORM 查询指南

英文

The preceding examples illustrated how to construct a UNION given two Table objects, to then return database rows. If we wanted to use a UNION or other set operation to select rows that we then receive as ORM objects, there are two approaches that may be used. In both cases, we first construct a select() or CompoundSelect object that represents the SELECT / UNION / etc statement we want to execute; this statement should be composed against the target ORM entities or their underlying mapped Table objects:

>>> stmt1 = select(User).where(User.name == "sandy")
>>> stmt2 = select(User).where(User.name == "spongebob")
>>> u = union_all(stmt1, stmt2)

For a simple SELECT with UNION that is not already nested inside of a subquery, these can often be used in an ORM object fetching context by using the Select.from_statement() method. With this approach, the UNION statement represents the entire query; no additional criteria can be added after Select.from_statement() is used:

>>> orm_stmt=select(User).from_statement(u)>>> withSession(engine)assession:...     for obj in session.execute(orm_stmt).scalars():...         print(obj)BEGIN (implicit)
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = ? UNION ALL SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = ?
[generated in ...] ('sandy', 'spongebob')
User(id=2, name='sandy', fullname='Sandy Cheeks')User(id=1, name='spongebob', fullname='Spongebob Squarepants')ROLLBACK

To use a UNION or other set-related construct as an entity-related component in in a more flexible manner, the CompoundSelect construct may be organized into a subquery using CompoundSelect.subquery(), which then links to ORM objects using the aliased() function. This works in the same way introduced at ORM 实体子查询/CTE, to first create an ad-hoc “mapping” of our desired entity to the subquery, then selecting from that new entity as though it were any other mapped class. In the example below, we are able to add additional criteria such as ORDER BY outside of the UNION itself, as we can filter or order by the columns exported by the subquery:

>>> user_alias=aliased(User,u.subquery())>>> orm_stmt=select(user_alias).order_by(user_alias.id)>>> withSession(engine)assession:...     for obj in session.execute(orm_stmt).scalars():...         print(obj)BEGIN (implicit)
SELECT anon_1.id, anon_1.name, anon_1.fullname
FROM (SELECT user_account.id AS id, user_account.name AS name, user_account.fullname AS fullname
FROM user_account
WHERE user_account.name = ? UNION ALL SELECT user_account.id AS id, user_account.name AS name, user_account.fullname AS fullname
FROM user_account
WHERE user_account.name = ?) AS anon_1 ORDER BY anon_1.id
[generated in ...] ('sandy', 'spongebob')
User(id=1, name='spongebob', fullname='Spongebob Squarepants')User(id=2, name='sandy', fullname='Sandy Cheeks')ROLLBACK

参见

从 UNION 和其他集合操作中选择实体 - in the ORM 查询指南

EXISTS 子查询¶

EXISTS subqueries

中文

SQL EXISTS 关键字是一个运算符，用于标量子查询，根据 SELECT 语句是否返回行来返回布尔值 true 或 false。SQLAlchemy 包含 ScalarSelect 对象的变体，称为 Exists，它将生成一个 EXISTS 子查询，并且最方便的是使用 SelectBase.exists() 方法生成。下面我们生成一个 EXISTS，以便返回在 address 中有多于一行相关行的 user_account 行：

>>> subq=(...     select(func.count(address_table.c.id))...     .where(user_table.c.id == address_table.c.user_id)...     .group_by(address_table.c.user_id)...     .having(func.count(address_table.c.id) > 1)... ).exists()>>> withengine.connect()asconn:...     result = conn.execute(select(user_table.c.name).where(subq))...     print(result.all())BEGIN (implicit)
SELECT user_account.name
FROM user_account
WHERE EXISTS (SELECT count(address.id) AS count_1
FROM address
WHERE user_account.id = address.user_id GROUP BY address.user_id
HAVING count(address.id) > ?)
[...] (1,)
[('sandy',)]ROLLBACK

EXISTS 构造更多地用作否定，例如 NOT EXISTS，因为它提供了一种 SQL 高效形式，用于查找相关表没有行的行。下面我们选择没有电子邮件地址的用户名；请注意在第二个 WHERE 子句中使用的二元否定运算符（ ~ ）：

>>> subq=(...     select(address_table.c.id).where(user_table.c.id == address_table.c.user_id)... ).exists()>>> withengine.connect()asconn:...     result = conn.execute(select(user_table.c.name).where(~subq))...     print(result.all())BEGIN (implicit)
SELECT user_account.name
FROM user_account
WHERE NOT (EXISTS (SELECT address.id
FROM address
WHERE user_account.id = address.user_id))
[...] ()
[('patrick',)]ROLLBACK

英文

The SQL EXISTS keyword is an operator that is used with scalar subqueries to return a boolean true or false depending on if the SELECT statement would return a row. SQLAlchemy includes a variant of the ScalarSelect object called Exists, which will generate an EXISTS subquery and is most conveniently generated using the SelectBase.exists() method. Below we produce an EXISTS so that we can return user_account rows that have more than one related row in address:

>>> subq=(...     select(func.count(address_table.c.id))...     .where(user_table.c.id == address_table.c.user_id)...     .group_by(address_table.c.user_id)...     .having(func.count(address_table.c.id) > 1)... ).exists()>>> withengine.connect()asconn:...     result = conn.execute(select(user_table.c.name).where(subq))...     print(result.all())BEGIN (implicit)
SELECT user_account.name
FROM user_account
WHERE EXISTS (SELECT count(address.id) AS count_1
FROM address
WHERE user_account.id = address.user_id GROUP BY address.user_id
HAVING count(address.id) > ?)
[...] (1,)
[('sandy',)]ROLLBACK

The EXISTS construct is more often than not used as a negation, e.g. NOT EXISTS, as it provides a SQL-efficient form of locating rows for which a related table has no rows. Below we select user names that have no email addresses; note the binary negation operator (~) used inside the second WHERE clause:

>>> subq=(...     select(address_table.c.id).where(user_table.c.id == address_table.c.user_id)... ).exists()>>> withengine.connect()asconn:...     result = conn.execute(select(user_table.c.name).where(~subq))...     print(result.all())BEGIN (implicit)
SELECT user_account.name
FROM user_account
WHERE NOT (EXISTS (SELECT address.id
FROM address
WHERE user_account.id = address.user_id))
[...] ()
[('patrick',)]ROLLBACK

数据强制转换和类型强制转换¶

Data Casts and Type Coercion

中文

在 SQL 中，我们经常需要显式地指示表达式的数据类型，或者告诉数据库在一个模糊的表达式中预期的数据类型，或者在某些情况下，我们想将 SQL 表达式的隐含数据类型转换为其他类型。SQL CAST 关键字用于此任务，在 SQLAlchemy 中由 cast() 函数提供。此函数接受列表达式和数据类型对象作为参数，如下所示，我们从 user_table.c.id 列对象生成 SQL 表达式 CAST(user_account.id AS VARCHAR):

>>> from sqlalchemy importcast>>> stmt=select(cast(user_table.c.id,String))>>> withengine.connect()asconn:...     result = conn.execute(stmt)...     result.all()BEGIN (implicit)
SELECT CAST(user_account.id AS VARCHAR) AS id
FROM user_account
[...] ()
[('1',), ('2',), ('3',)]ROLLBACK

cast() 函数不仅呈现 SQL CAST 语法，还生成一个 SQLAlchemy 列表达式，该表达式在 Python 端也将作为给定的数据类型。例如，一个被 cast() 为 JSON 的字符串表达式将获得 JSON 下标和比较运算符:

>>> from sqlalchemy importJSON>>> print(cast("{'a': 'b'}",JSON)["a"])CAST(:param_1 AS JSON)[:param_2]

英文

In SQL, we often need to indicate the datatype of an expression explicitly, either to tell the database what type is expected in an otherwise ambiguous expression, or in some cases when we want to convert the implied datatype of a SQL expression into something else. The SQL CAST keyword is used for this task, which in SQLAlchemy is provided by the cast() function. This function accepts a column expression and a data type object as arguments, as demonstrated below where we produce a SQL expression CAST(user_account.id AS VARCHAR) from the user_table.c.id column object:

>>> from sqlalchemy importcast>>> stmt=select(cast(user_table.c.id,String))>>> withengine.connect()asconn:...     result = conn.execute(stmt)...     result.all()BEGIN (implicit)
SELECT CAST(user_account.id AS VARCHAR) AS id
FROM user_account
[...] ()
[('1',), ('2',), ('3',)]ROLLBACK

The cast() function not only renders the SQL CAST syntax, it also produces a SQLAlchemy column expression that will act as the given datatype on the Python side as well. A string expression that is cast() to JSON will gain JSON subscript and comparison operators, for example:

>>> from sqlalchemy importJSON>>> print(cast("{'a': 'b'}",JSON)["a"])CAST(:param_1 AS JSON)[:param_2]

type_coerce() - 仅限 Python 的“强制转换”¶

type_coerce() - a Python-only “cast”

中文

有时需要让 SQLAlchemy 知道表达式的数据类型，出于上述所有原因，但不要在 SQL 端呈现 CAST 表达式，因为它可能会干扰已经可以正常工作的 SQL 操作。对于这种相当常见的用例，有另一个函数 type_coerce() 与 cast() 密切相关，因为它将 Python 表达式设置为具有特定的 SQL 数据库类型，但不会在数据库端呈现 CAST 关键字或数据类型。type_coerce() 在处理 JSON 数据类型时特别重要，因为它通常与不同平台上的字符串导向数据类型有着复杂的关系，甚至可能不是显式的数据类型，如在 SQLite 和 MariaDB 上。下面，我们使用 type_coerce() 将 Python 结构作为 JSON 字符串传递到 MySQL 的一个 JSON 函数中：

>>> import json>>> from sqlalchemy importJSON>>> from sqlalchemy importtype_coerce>>> from sqlalchemy.dialects importmysql>>> s=select(type_coerce({"some_key":{"foo":"bar"}},JSON)["some_key"])>>> print(s.compile(dialect=mysql.dialect()))SELECT JSON_EXTRACT(%s, %s) AS anon_1

上面，MySQL 的 JSON_EXTRACT SQL 函数被调用，因为我们使用 type_coerce() 指示我们的 Python 字典应被视为 JSON。Python 的 __getitem__ 运算符，在本例中为 ['some_key']，因此可用并允许呈现 JSON_EXTRACT 路径表达式（未显示，但在本例中最终将是 '$."some_key"'）。

英文

Sometimes there is the need to have SQLAlchemy know the datatype of an expression, for all the reasons mentioned above, but to not render the CAST expression itself on the SQL side, where it may interfere with a SQL operation that already works without it. For this fairly common use case there is another function type_coerce() which is closely related to cast(), in that it sets up a Python expression as having a specific SQL database type, but does not render the CAST keyword or datatype on the database side. type_coerce() is particularly important when dealing with the JSON datatype, which typically has an intricate relationship with string-oriented datatypes on different platforms and may not even be an explicit datatype, such as on SQLite and MariaDB. Below, we use type_coerce() to deliver a Python structure as a JSON string into one of MySQL’s JSON functions:

>>> import json>>> from sqlalchemy importJSON>>> from sqlalchemy importtype_coerce>>> from sqlalchemy.dialects importmysql>>> s=select(type_coerce({"some_key":{"foo":"bar"}},JSON)["some_key"])>>> print(s.compile(dialect=mysql.dialect()))SELECT JSON_EXTRACT(%s, %s) AS anon_1

Above, MySQL’s JSON_EXTRACT SQL function was invoked because we used type_coerce() to indicate that our Python dictionary should be treated as JSON. The Python __getitem__ operator, ['some_key'] in this case, became available as a result and allowed a JSON_EXTRACT path expression (not shown, however in this case it would ultimately be '$."some_key"') to be rendered.

使用 SELECT 语句¶

select() SQL 表达式构造¶

设置 COLUMNS 和 FROM 子句¶

选择 ORM 实体和列¶

从带标签的 SQL 表达式中选择¶

使用文本列表达式进行选择¶

WHERE 子句¶

显式 FROM 子句和 JOIN¶

设置 ON 子句¶

OUTER 和 FULL 连接¶

ORDER BY、GROUP BY、HAVING¶

ORDER BY¶

使用 GROUP BY / HAVING 的聚合函数¶

按标签排序或分组¶

使用别名¶

ORM 实体别名¶

子查询和 CTE¶

通用表表达式 (CTE)¶

ORM 实体子查询/CTE¶

标量和相关子查询¶

LATERAL 相关¶

UNION、UNION ALL 和其他集合操作¶

从联合中选择 ORM 实体¶

EXISTS 子查询¶

使用 SQL 函数¶

函数具有返回类型¶

内置函数具有预配置的返回类型¶

高级 SQL 函数技术¶

使用窗口函数¶

特殊修饰符 WITHIN GROUP、FILTER¶

表值函数¶

列值函数 - 表值函数作为标量列¶

数据强制转换和类型强制转换¶

type_coerce() - 仅限 Python 的“强制转换”¶