Release: 2.0.29 | Release Date: March 23, 2024

# SQLAlchemy 2.0 Documentation

SQLAlchemy 1.4 / 2.0 Tutorial

This page is part of the SQLAlchemy Unified Tutorial.

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# Using SELECT Statements

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 Querying Guide.

## The select() SQL Expression Construct

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 import select
    >>> 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:

    >>> with engine.connect() as conn:
    ...     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")
    >>> with Session(engine) as session:
    ...     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

select() from a Table vs. ORM class

While the SQL generated in these examples looks the same whether we invoke `select(user_table)` or `select(User)`, in the more general case they do not necessarily render the same thing, as an ORM-mapped class may be mapped to other kinds of “selectables” besides tables. The `select()` that’s against an ORM entity also indicates that ORM-mapped instances should be returned in a result, which is not the case when SELECTing from a `Table` object.

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

## Setting the COLUMNS and FROM clause

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

New in version 2.0: Added tuple-accessor capability to the `FromClause.c` collection

### Selecting ORM Entities and Columns

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
    [...] ()
    >>> user
    User(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 Selecting ORM Entities and Attributes.

See also

Selecting ORM Entities and Attributes - in the ORM Querying Guide

### Selecting from Labeled SQL Expressions

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 import func, cast
    >>> stmt = select(
    ...     ("Username: " + user_table.c.name).label("username"),
    ... ).order_by(user_table.c.name)
    >>> with engine.connect() as conn:
    ...     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: patrick
    Username: sandy
    Username: spongebob
    ROLLBACK

See also

Ordering or Grouping by a Label - the label names we create may also be referenced in the ORDER BY or GROUP BY clause of the `Select`.

### Selecting with Textual Column Expressions

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 Working with Transactions and the 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:
    ...     print(conn.execute(stmt).all())
    BEGIN (implicit)
    SELECT 'some phrase', user_account.name
    FROM user_account ORDER BY user_account.name
    [generated in ...] ()
    [('some phrase', 'patrick'), ('some phrase', 'sandy'), ('some phrase', 'spongebob')]
    ROLLBACK

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

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.

## The WHERE clause

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 import and_, 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

See also

Operator Reference - descriptions of most SQL operator functions in SQLAlchemy

## Explicit FROM clauses and JOINs

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

See also

Setting the leftmost FROM clause in a join - in the ORM Querying Guide - contains additional examples and notes regarding the interaction of `Select.select_from()` and `Select.join()`.

### Setting the ON Clause

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 The WHERE clause:

    >>> 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 Declaring Mapped Classes. This is a whole subject onto itself, which is introduced at length at Using Relationships to Join.

### OUTER and FULL join

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)`.

Tip

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

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

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.

### Aggregate functions with GROUP BY / HAVING

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 import func
    >>> 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 Working with SQL Functions.

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:

    >>> with engine.connect() as conn:
    ...     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

### Ordering or Grouping by a Label

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 import func, 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

## Using Aliases

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 Entity Aliases

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 import aliased
    >>> 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

Tip

As mentioned in Setting the ON Clause, the ORM provides for another way to join using the `relationship()` construct. The above example using aliases is demonstrated using `relationship()` at Using Relationship to join between aliased targets.

## Subqueries and CTEs

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 Aggregate functions with GROUP BY / HAVING):

    >>> 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.

### Common Table Expressions (CTEs)

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.

See also

`Select.subquery()` - further detail on subqueries

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

### ORM Entity Subqueries/CTEs

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 Entity Aliases 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)
    ... )
    >>> with Session(engine) as session:
    ...     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)
    ... )
    >>> with Session(engine) as session:
    ...     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

See also

Selecting Entities from Subqueries - in the ORM Querying Guide

## Scalar and Correlated Subqueries

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 Aggregate functions with GROUP BY / HAVING. 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:

    >>> with engine.connect() as conn:
    ...     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 correlation

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.

See also

`Lateral`

`Select.lateral()`

## UNION, UNION ALL and other set operations

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 import union_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)
    >>> with engine.connect() as conn:
    ...     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)
    ... )
    >>> with engine.connect() as conn:
    ...     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

### Selecting ORM Entities from Unions

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)
    >>> with Session(engine) as session:
    ...     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 Entity Subqueries/CTEs, 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)
    >>> with Session(engine) as session:
    ...     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

See also

Selecting Entities from UNIONs and other set operations - in the ORM Querying Guide

## EXISTS subqueries

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()
    >>> with engine.connect() as conn:
    ...     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()
    >>> with engine.connect() as conn:
    ...     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

## Working with SQL Functions

First introduced earlier in this section at Aggregate functions with GROUP BY / HAVING, the `func` object serves as a factory for creating new `Function` objects, which when used in a construct like `select()`, produce a SQL function display, typically consisting of a name, some parenthesis (although not always), and possibly some arguments. Examples of typical SQL functions include:

- the `count()` function, an aggregate function which counts how many rows are returned:

      >>> print(select(func.count()).select_from(user_table))
      SELECT count(*) AS count_1
      FROM user_account

- the `lower()` function, a string function that converts a string to lower case:

      >>> print(select(func.lower("A String With Much UPPERCASE")))
      SELECT lower(:lower_2) AS lower_1

- the `now()` function, which provides for the current date and time; as this is a common function, SQLAlchemy knows how to render this differently for each backend, in the case of SQLite using the CURRENT_TIMESTAMP function:

      >>> stmt = select(func.now())
      >>> with engine.connect() as conn:
      ...     result = conn.execute(stmt)
      ...     print(result.all())
      BEGIN (implicit)
      SELECT CURRENT_TIMESTAMP AS now_1
      [...] ()
      [(datetime.datetime(...),)]
      ROLLBACK

As most database backends feature dozens if not hundreds of different SQL functions, `func` tries to be as liberal as possible in what it accepts. Any name that is accessed from this namespace is automatically considered to be a SQL function that will render in a generic way:

    >>> print(select(func.some_crazy_function(user_table.c.name, 17)))
    SELECT some_crazy_function(user_account.name, :some_crazy_function_2) AS some_crazy_function_1
    FROM user_account

At the same time, a relatively small set of extremely common SQL functions such as `count`, `now`, `max`, `concat` include pre-packaged versions of themselves which provide for proper typing information as well as backend-specific SQL generation in some cases. The example below contrasts the SQL generation that occurs for the PostgreSQL dialect compared to the Oracle dialect for the `now` function:

    >>> from sqlalchemy.dialects import postgresql
    >>> print(select(func.now()).compile(dialect=postgresql.dialect()))
    SELECT now() AS now_1
    >>> from sqlalchemy.dialects import oracle
    >>> print(select(func.now()).compile(dialect=oracle.dialect()))
    SELECT CURRENT_TIMESTAMP AS now_1 FROM DUAL

### Functions Have Return Types

As functions are column expressions, they also have SQL datatypes that describe the data type of a generated SQL expression. We refer to these types here as “SQL return types”, in reference to the type of SQL value that is returned by the function in the context of a database-side SQL expression, as opposed to the “return type” of a Python function.

The SQL return type of any SQL function may be accessed, typically for debugging purposes, by referring to the `Function.type` attribute:

    >>> func.now().type
    DateTime()

These SQL return types are significant when making use of the function expression in the context of a larger expression; that is, math operators will work better when the datatype of the expression is something like `Integer` or `Numeric`, JSON accessors in order to work need to be using a type such as `JSON`. Certain classes of functions return entire rows instead of column values, where there is a need to refer to specific columns; such functions are known as table valued functions.

The SQL return type of the function may also be significant when executing a statement and getting rows back, for those cases where SQLAlchemy has to apply result-set processing. A prime example of this are date-related functions on SQLite, where SQLAlchemy’s `DateTime` and related datatypes take on the role of converting from string values to Python `datetime()` objects as result rows are received.

To apply a specific type to a function we’re creating, we pass it using the `Function.type_` parameter; the type argument may be either a `TypeEngine` class or an instance. In the example below we pass the `JSON` class to generate the PostgreSQL `json_object()` function, noting that the SQL return type will be of type JSON:

    >>> from sqlalchemy import JSON
    >>> function_expr = func.json_object('{a, 1, b, "def", c, 3.5}', type_=JSON)

By creating our JSON function with the `JSON` datatype, the SQL expression object takes on JSON-related features, such as that of accessing elements:

    >>> stmt = select(function_expr["def"])
    >>> print(stmt)
    SELECT json_object(:json_object_1)[:json_object_2] AS anon_1

### Built-in Functions Have Pre-Configured Return Types

For common aggregate functions like `count`, `max`, `min` as well as a very small number of date functions like `now` and string functions like `concat`, the SQL return type is set up appropriately, sometimes based on usage. The `max` function and similar aggregate filtering functions will set up the SQL return type based on the argument given:

    >>> m1 = func.max(Column("some_int", Integer))
    >>> m1.type
    Integer()

    >>> m2 = func.max(Column("some_str", String))
    >>> m2.type
    String()

Date and time functions typically correspond to SQL expressions described by `DateTime`, `Date` or `Time`:

    >>> func.now().type
    DateTime()
    >>> func.current_date().type
    Date()

A known string function such as `concat` will know that a SQL expression would be of type `String`:

    >>> func.concat("x", "y").type
    String()

However, for the vast majority of SQL functions, SQLAlchemy does not have them explicitly present in its very small list of known functions. For example, while there is typically no issue using SQL functions `func.lower()` and `func.upper()` to convert the casing of strings, SQLAlchemy doesn’t actually know about these functions, so they have a “null” SQL return type:

    >>> func.upper("lowercase").type
    NullType()

For simple functions like `upper` and `lower`, the issue is not usually significant, as string values may be received from the database without any special type handling on the SQLAlchemy side, and SQLAlchemy’s type coercion rules can often correctly guess intent as well; the Python `+` operator for example will be correctly interpreted as the string concatenation operator based on looking at both sides of the expression:

    >>> print(select(func.upper("lowercase") + " suffix"))
    SELECT upper(:upper_1) || :upper_2 AS anon_1

Overall, the scenario where the `Function.type_` parameter is likely necessary is:

1. the function is not already a SQLAlchemy built-in function; this can be evidenced by creating the function and observing the `Function.type` attribute, that is:

       >>> func.count().type
       Integer()

   vs.:

       >>> func.json_object('{"a", "b"}').type
       NullType()

2. Function-aware expression support is needed; this most typically refers to special operators related to datatypes such as `JSON` or `ARRAY`

3. Result value processing is needed, which may include types such as `DateTime`, `Boolean`, `Enum`, or again special datatypes such as `JSON`, `ARRAY`.

### Advanced SQL Function Techniques

The following subsections illustrate more things that can be done with SQL functions. While these techniques are less common and more advanced than basic SQL function use, they nonetheless are extremely popular, largely as a result of PostgreSQL’s emphasis on more complex function forms, including table- and column-valued forms that are popular with JSON data.

#### Using Window Functions

A window function is a special use of a SQL aggregate function which calculates the aggregate value over the rows being returned in a group as the individual result rows are processed. Whereas a function like `MAX()` will give you the highest value of a column within a set of rows, using the same function as a “window function” will given you the highest value for each row, as of that row.

In SQL, window functions allow one to specify the rows over which the function should be applied, a “partition” value which considers the window over different sub-sets of rows, and an “order by” expression which importantly indicates the order in which rows should be applied to the aggregate function.

In SQLAlchemy, all SQL functions generated by the `func` namespace include a method `FunctionElement.over()` which grants the window function, or “OVER”, syntax; the construct produced is the `Over` construct.

A common function used with window functions is the `row_number()` function which simply counts rows. We may partition this row count against user name to number the email addresses of individual users:

    >>> stmt = (
    ...     select(
    ...         func.row_number().over(partition_by=user_table.c.name),
    ...         user_table.c.name,
    ...         address_table.c.email_address,
    ...     )
    ...     .select_from(user_table)
    ...     .join(address_table)
    ... )
    >>> with engine.connect() as conn:
    ...     result = conn.execute(stmt)
    ...     print(result.all())
    BEGIN (implicit)
    SELECT row_number() OVER (PARTITION BY user_account.name) AS anon_1,
    user_account.name, address.email_address
    FROM user_account JOIN address ON user_account.id = address.user_id
    [...] ()
    [(1, 'sandy', 'sandy@sqlalchemy.org'), (2, 'sandy', 'sandy@squirrelpower.org'), (1, 'spongebob', 'spongebob@sqlalchemy.org')]
    ROLLBACK

Above, the `FunctionElement.over.partition_by` parameter is used so that the `PARTITION BY` clause is rendered within the OVER clause. We also may make use of the `ORDER BY` clause using `FunctionElement.over.order_by`:

    >>> stmt = (
    ...     select(
    ...         func.count().over(order_by=user_table.c.name),
    ...         user_table.c.name,
    ...         address_table.c.email_address,
    ...     )
    ...     .select_from(user_table)
    ...     .join(address_table)
    ... )
    >>> with engine.connect() as conn:
    ...     result = conn.execute(stmt)
    ...     print(result.all())
    BEGIN (implicit)
    SELECT count(*) OVER (ORDER BY user_account.name) AS anon_1,
    user_account.name, address.email_address
    FROM user_account JOIN address ON user_account.id = address.user_id
    [...] ()
    [(2, 'sandy', 'sandy@sqlalchemy.org'), (2, 'sandy', 'sandy@squirrelpower.org'), (3, 'spongebob', 'spongebob@sqlalchemy.org')]
    ROLLBACK

Further options for window functions include usage of ranges; see `over()` for more examples.

Tip

It’s important to note that the `FunctionElement.over()` method only applies to those SQL functions which are in fact aggregate functions; while the `Over` construct will happily render itself for any SQL function given, the database will reject the expression if the function itself is not a SQL aggregate function.

#### Special Modifiers WITHIN GROUP, FILTER

The “WITHIN GROUP” SQL syntax is used in conjunction with an “ordered set” or a “hypothetical set” aggregate function. Common “ordered set” functions include `percentile_cont()` and `rank()`. SQLAlchemy includes built in implementations `rank`, `dense_rank`, `mode`, `percentile_cont` and `percentile_disc` which include a `FunctionElement.within_group()` method:

    >>> print(
    ...     func.unnest(
    ...         func.percentile_disc([0.25, 0.5, 0.75, 1]).within_group(user_table.c.name)
    ...     )
    ... )
    unnest(percentile_disc(:percentile_disc_1) WITHIN GROUP (ORDER BY user_account.name))

“FILTER” is supported by some backends to limit the range of an aggregate function to a particular subset of rows compared to the total range of rows returned, available using the `FunctionElement.filter()` method:

    >>> stmt = (
    ...     select(
    ...         func.count(address_table.c.email_address).filter(user_table.c.name == "sandy"),
    ...         func.count(address_table.c.email_address).filter(
    ...             user_table.c.name == "spongebob"
    ...         ),
    ...     )
    ...     .select_from(user_table)
    ...     .join(address_table)
    ... )
    >>> with engine.connect() as conn:
    ...     result = conn.execute(stmt)
    ...     print(result.all())
    BEGIN (implicit)
    SELECT count(address.email_address) FILTER (WHERE user_account.name = ?) AS anon_1,
    count(address.email_address) FILTER (WHERE user_account.name = ?) AS anon_2
    FROM user_account JOIN address ON user_account.id = address.user_id
    [...] ('sandy', 'spongebob')
    [(2, 1)]
    ROLLBACK

#### Table-Valued Functions

Table-valued SQL functions support a scalar representation that contains named sub-elements. Often used for JSON and ARRAY-oriented functions as well as functions like `generate_series()`, the table-valued function is specified in the FROM clause, and is then referenced as a table, or sometimes even as a column. Functions of this form are prominent within the PostgreSQL database, however some forms of table valued functions are also supported by SQLite, Oracle, and SQL Server.

See also

Table values, Table and Column valued functions, Row and Tuple objects - in the PostgreSQL documentation.

While many databases support table valued and other special forms, PostgreSQL tends to be where there is the most demand for these features. See this section for additional examples of PostgreSQL syntaxes as well as additional features.

SQLAlchemy provides the `FunctionElement.table_valued()` method as the basic “table valued function” construct, which will convert a `func` object into a FROM clause containing a series of named columns, based on string names passed positionally. This returns a `TableValuedAlias` object, which is a function-enabled `Alias` construct that may be used as any other FROM clause as introduced at Using Aliases. Below we illustrate the `json_each()` function, which while common on PostgreSQL is also supported by modern versions of SQLite:

    >>> onetwothree = func.json_each('["one", "two", "three"]').table_valued("value")
    >>> stmt = select(onetwothree).where(onetwothree.c.value.in_(["two", "three"]))
    >>> with engine.connect() as conn:
    ...     result = conn.execute(stmt)
    ...     result.all()
    BEGIN (implicit)
    SELECT anon_1.value
    FROM json_each(?) AS anon_1
    WHERE anon_1.value IN (?, ?)
    [...] ('["one", "two", "three"]', 'two', 'three')
    [('two',), ('three',)]
    ROLLBACK

Above, we used the `json_each()` JSON function supported by SQLite and PostgreSQL to generate a table valued expression with a single column referred towards as `value`, and then selected two of its three rows.

See also

Table-Valued Functions - in the PostgreSQL documentation - this section will detail additional syntaxes such as special column derivations and “WITH ORDINALITY” that are known to work with PostgreSQL.

#### Column Valued Functions - Table Valued Function as a Scalar Column

A special syntax supported by PostgreSQL and Oracle is that of referring towards a function in the FROM clause, which then delivers itself as a single column in the columns clause of a SELECT statement or other column expression context. PostgreSQL makes great use of this syntax for such functions as `json_array_elements()`, `json_object_keys()`, `json_each_text()`, `json_each()`, etc.

SQLAlchemy refers to this as a “column valued” function and is available by applying the `FunctionElement.column_valued()` modifier to a `Function` construct:

    >>> from sqlalchemy import select, func
    >>> stmt = select(func.json_array_elements('["one", "two"]').column_valued("x"))
    >>> print(stmt)
    SELECT x
    FROM json_array_elements(:json_array_elements_1) AS x

The “column valued” form is also supported by the Oracle dialect, where it is usable for custom SQL functions:

    >>> from sqlalchemy.dialects import oracle
    >>> stmt = select(func.scalar_strings(5).column_valued("s"))
    >>> print(stmt.compile(dialect=oracle.dialect()))
    SELECT s.COLUMN_VALUE
    FROM TABLE (scalar_strings(:scalar_strings_1)) s

See also

Column Valued Functions - in the PostgreSQL documentation.

## Data Casts and Type Coercion

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 import cast
    >>> stmt = select(cast(user_table.c.id, String))
    >>> with engine.connect() as conn:
    ...     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 import JSON
    >>> print(cast("{'a': 'b'}", JSON)["a"])
    CAST(:param_1 AS JSON)[:param_2]

### type_coerce() - a Python-only “cast”

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 import JSON
    >>> from sqlalchemy import type_coerce
    >>> from sqlalchemy.dialects import mysql
    >>> 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.

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