SQLAlchemy 1.4 / 2.0 Tutorial

This page is part of the SQLAlchemy 1.4 / 2.0 Tutorial.

Previous: Working with Database Metadata | Next: Data Manipulation with the ORM

Working with Data

In Working with Transactions and the DBAPI, we learned the basics of how to interact with the Python DBAPI and its transactional state. Then, in Working with Database Metadata, we learned how to represent database tables, columns, and constraints within SQLAlchemy using the MetaData and related objects. In this section we will combine both concepts above to create, select and manipulate data within a relational database. Our interaction with the database is always in terms of a transaction, even if we’ve set our database driver to use autocommit behind the scenes.

The components of this section are as follows:

• Core Insert - to get some data into the database, we introduce and demonstrate the Core Insert construct. INSERTs from an ORM perspective are described in the next section Data Manipulation with the ORM.

• Selecting Data - this section will describe in detail the Select construct, which is the most commonly used object in SQLAlchemy. The Select construct emits SELECT statements for both Core and ORM centric applications and both use cases will be described here. Additional ORM use cases are also noted in the later section Using Relationships in Queries as well as the ORM Querying Guide.

• Core UPDATE and DELETE - Rounding out the INSERT and SELECtion of data, this section will describe from a Core perspective the use of the Update and Delete constructs. ORM-specific UPDATE and DELETE is similarly described in the Data Manipulation with the ORM section.

Core Insert

When using Core, a SQL INSERT statement is generated using the insert() function - this function generates a new instance of Insert which represents an INSERT statement in SQL, that adds new data into a table.

ORM Readers - The way that rows are INSERTed into the database from an ORM perspective makes use of object-centric APIs on the Session object known as the unit of work process, and is fairly different from the Core-only approach described here. The more ORM-focused sections later starting at Inserting Rows with the ORM subsequent to the Expression Language sections introduce this.

The insert() SQL Expression Construct

A simple example of Insert illustrating the target table and the VALUES clause at once:

>>> from sqlalchemy import insert
>>> stmt = insert(user_table).values(name='spongebob', fullname="Spongebob Squarepants")

The above stmt variable is an instance of Insert. Most SQL expressions can be stringified in place as a means to see the general form of what’s being produced:

>>> print(stmt)
INSERT INTO user_account (name, fullname) VALUES (:name, :fullname)

The stringified form is created by producing a Compiled form of the object which includes a database-specific string SQL representation of the statement; we can acquire this object directly using the ClauseElement.compile() method:

>>> compiled = stmt.compile()

Our Insert construct is an example of a “parameterized” construct, illustrated previously at Sending Parameters; to view the name and fullname bound parameters, these are available from the Compiled construct as well:

>>> compiled.params
{'name': 'spongebob', 'fullname': 'Spongebob Squarepants'}

Executing the Statement

Invoking the statement we can INSERT a row into user_table. The INSERT SQL as well as the bundled parameters can be seen in the SQL logging:

>>> with engine.connect() as conn:
...     result = conn.execute(stmt)
...     conn.commit()
BEGIN (implicit)
INSERT INTO user_account (name, fullname) VALUES (?, ?)
[...] ('spongebob', 'Spongebob Squarepants')
COMMIT

In its simple form above, the INSERT statement does not return any rows, and if only a single row is inserted, it will usually include the ability to return information about column-level default values that were generated during the INSERT of that row, most commonly an integer primary key value. In the above case the first row in a SQLite database will normally return 1 for the first integer primary key value, which we can acquire using the CursorResult.inserted_primary_key accessor:

>>> result.inserted_primary_key
(1,)

Tip

CursorResult.inserted_primary_key returns a tuple because a primary key may contain multiple columns. This is known as a composite primary key. The CursorResult.inserted_primary_key is intended to always contain the complete primary key of the record just inserted, not just a “cursor.lastrowid” kind of value, and is also intended to be populated regardless of whether or not “autoincrement” were used, hence to express a complete primary key it’s a tuple.

INSERT usually generates the “values” clause automatically

The example above made use of the Insert.values() method to explicitly create the VALUES clause of the SQL INSERT statement. This method in fact has some variants that allow for special forms such as multiple rows in one statement and insertion of SQL expressions. However the usual way that Insert is used is such that the VALUES clause is generated automatically from the parameters passed to the Connection.execute() method; below we INSERT two more rows to illustrate this:

>>> with engine.connect() as conn:
...     result = conn.execute(
...         insert(user_table),
...         [
...             {"name": "sandy", "fullname": "Sandy Cheeks"},
...             {"name": "patrick", "fullname": "Patrick Star"}
...         ]
...     )
...     conn.commit()
BEGIN (implicit)
INSERT INTO user_account (name, fullname) VALUES (?, ?)
[...] (('sandy', 'Sandy Cheeks'), ('patrick', 'Patrick Star'))
COMMIT

The execution above features “executemany” form first illustrated at Sending Multiple Parameters, however unlike when using the text() construct, we didn’t have to spell out any SQL. By passing a dictionary or list of dictionaries to the Connection.execute() method in conjunction with the Insert construct, the Connection ensures that the column names which are passed will be expressed in the VALUES clause of the Insert construct automatically.

Deep Alchemy

Hi, welcome to the first edition of Deep Alchemy. The person on the left is known as The Alchemist, and you’ll note they are not a wizard, as the pointy hat is not sticking upwards. The Alchemist comes around to describe things that are generally more advanced and/or tricky and additionally not usually needed, but for whatever reason they feel you should know about this thing that SQLAlchemy can do.

In this edition, towards the goal of having some interesting data in the address_table as well, below is a more advanced example illustrating how the Insert.values() method may be used explicitly while at the same time including for additional VALUES generated from the parameters. A scalar subquery is constructed, making use of the select() construct introduced in the next section, and the parameters used in the subquery are set up using an explicit bound parameter name, established using the bindparam() construct.

This is some slightly deeper alchemy just so that we can add related rows without fetching the primary key identifiers from the user_table operation into the application. Most Alchemists will simply use the ORM which takes care of things like this for us.

>>> from sqlalchemy import select, bindparam
>>> scalar_subquery = (
...     select(user_table.c.id).
...     where(user_table.c.name==bindparam('username')).
...     scalar_subquery()
... )
>>> with engine.connect() as conn:
...     result = conn.execute(
...         insert(address_table).values(user_id=scalar_subquery),
...         [
...             {"username": 'spongebob', "email_address": "spongebob@sqlalchemy.org"},
...             {"username": 'sandy', "email_address": "sandy@sqlalchemy.org"},
...             {"username": 'sandy', "email_address": "sandy@squirrelpower.org"},
...         ]
...     )
...     conn.commit()
BEGIN (implicit)
INSERT INTO address (user_id, email_address) VALUES ((SELECT user_account.id
FROM user_account
WHERE user_account.name = ?), ?)
[...] (('spongebob', 'spongebob@sqlalchemy.org'), ('sandy', 'sandy@sqlalchemy.org'),
('sandy', 'sandy@squirrelpower.org'))
COMMIT

INSERT…FROM SELECT

The Insert construct can compose an INSERT that gets rows directly from a SELECT using the Insert.from_select() method:

>>> select_stmt = select(user_table.c.id, user_table.c.name + "@aol.com")
>>> insert_stmt = insert(address_table).from_select(
...     ["user_id", "email_address"], select_stmt
... )
>>> print(insert_stmt)
INSERT INTO address (user_id, email_address)
SELECT user_account.id, user_account.name || :name_1 AS anon_1
FROM user_account

INSERT…RETURNING

The RETURNING clause for supported backends is used automatically in order to retrieve the last inserted primary key value as well as the values for server defaults. However the RETURNING clause may also be specified explicitly using the Insert.returning() method; in this case, the Result object that’s returned when the statement is executed has rows which can be fetched:

>>> insert_stmt = insert(address_table).returning(address_table.c.id, address_table.c.email_address)
>>> print(insert_stmt)
INSERT INTO address (id, user_id, email_address)
VALUES (:id, :user_id, :email_address)
RETURNING address.id, address.email_address

It can also be combined with Insert.from_select(), as in the example below that builds upon the example stated in INSERT…FROM SELECT:

>>> select_stmt = select(user_table.c.id, user_table.c.name + "@aol.com")
>>> insert_stmt = insert(address_table).from_select(
...     ["user_id", "email_address"], select_stmt
... )
>>> print(insert_stmt.returning(address_table.c.id, address_table.c.email_address))
INSERT INTO address (user_id, email_address)
SELECT user_account.id, user_account.name || :name_1 AS anon_1
FROM user_account RETURNING address.id, address.email_address

Tip

The RETURNING feature is also supported by UPDATE and DELETE statements, which will be introduced later in this tutorial. The RETURNING feature is generally 1 only supported for statement executions that use a single set of bound parameters; that is, it wont work with the “executemany” form introduced at Sending Multiple Parameters. Additionally, some dialects such as the Oracle dialect only allow RETURNING to return a single row overall, meaning it won’t work with “INSERT..FROM SELECT” nor will it work with multiple row Update or Delete forms.

1

There is internal support for the psycopg2 dialect to INSERT many rows at once and also support RETURNING, which is leveraged by the SQLAlchemy ORM. However this feature has not been generalized to all dialects and is not yet part of SQLAlchemy’s regular API.

See also

Insert - in the SQL Expression API documentation

Selecting Data

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

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')

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

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 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

The ON Clause is inferred

When using Select.join_from() or Select.join(), we may observe that the ON clause of the join is also inferred for us in simple foreign key cases. More on that in the next section.

We also have the option 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

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

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.name.asc(), User.fullname.desc()))
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account ORDER BY user_account.name ASC, user_account.fullname DESC

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 name 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 mutiple 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 referred towards 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 Joining 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 = select(Address).where(~Address.email_address.like('%@aol.com')).cte()
>>> address_cte = aliased(Address, cte)
>>> 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

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

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 referred towards 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.

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 referred towards 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)
...     print(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 COLUMN_VALUE s
FROM TABLE (scalar_strings(:scalar_strings_1)) s

See also

Column Valued Functions - in the PostgreSQL documentation.

Core UPDATE and DELETE

So far we’ve covered Insert, so that we can get some data into our database, and then spent a lot of time on Select which handles the broad range of usage patterns used for retrieving data from the database. In this section we will cover the Update and Delete constructs, which are used to modify existing rows as well as delete existing rows. This section will cover these constructs from a Core-centric perspective.

ORM Readers - As was the case mentioned at Core Insert, the Update and Delete operations when used with the ORM are usually invoked internally from the Session object as part of the unit of work process.

However, unlike Insert, the Update and Delete constructs can also be used directly with the ORM, using a pattern known as “ORM-enabled update and delete”; for this reason, familiarity with these constructs is useful for ORM use. Both styles of use are discussed in the sections Updating ORM Objects and Deleting ORM Objects.

The update() SQL Expression Construct

The update() function generates a new instance of Update which represents an UPDATE statement in SQL, that will update existing data in a table.

Like the insert() construct, there is a “traditional” form of update(), which emits UPDATE against a single table at a time and does not return any rows. However some backends support an UPDATE statement that may modify multiple tables at once, and the UPDATE statement also supports RETURNING such that columns contained in matched rows may be returned in the result set.

A basic UPDATE looks like:

>>> from sqlalchemy import update
>>> stmt = (
...     update(user_table).where(user_table.c.name == 'patrick').
...     values(fullname='Patrick the Star')
... )
>>> print(stmt)
UPDATE user_account SET fullname=:fullname WHERE user_account.name = :name_1

The Update.values() method controls the contents of the SET elements of the UPDATE statement. This is the same method shared by the Insert construct. Parameters can normally be passed using the column names as keyword arguments.

UPDATE supports all the major SQL forms of UPDATE, including updates against expressions, where we can make use of Column expressions:

>>> stmt = (
...     update(user_table).
...     values(fullname="Username: " + user_table.c.name)
... )
>>> print(stmt)
UPDATE user_account SET fullname=(:name_1 || user_account.name)

To support UPDATE in an “executemany” context, where many parameter sets will be invoked against the same statement, the bindparam() construct may be used to set up bound parameters; these replace the places that literal values would normally go:

>>> from sqlalchemy import bindparam
>>> stmt = (
...   update(user_table).
...   where(user_table.c.name == bindparam('oldname')).
...   values(name=bindparam('newname'))
... )
>>> with engine.begin() as conn:
...   conn.execute(
...       stmt,
...       [
...          {'oldname':'jack', 'newname':'ed'},
...          {'oldname':'wendy', 'newname':'mary'},
...          {'oldname':'jim', 'newname':'jake'},
...       ]
...   )
BEGIN (implicit)
UPDATE user_account SET name=? WHERE user_account.name = ?
[...] (('ed', 'jack'), ('mary', 'wendy'), ('jake', 'jim'))
<sqlalchemy.engine.cursor.CursorResult object at 0x...>
COMMIT

Other techniques which may be applied to UPDATE include:

Correlated Updates

An UPDATE statement can make use of rows in other tables by using a correlated subquery. A subquery may be used anywhere a column expression might be placed:

>>> scalar_subq = (
...   select(address_table.c.email_address).
...   where(address_table.c.user_id == user_table.c.id).
...   order_by(address_table.c.id).
...   limit(1).
...   scalar_subquery()
... )
>>> update_stmt = update(user_table).values(fullname=scalar_subq)
>>> print(update_stmt)
UPDATE user_account SET fullname=(SELECT address.email_address
FROM address
WHERE address.user_id = user_account.id ORDER BY address.id
LIMIT :param_1)

UPDATE..FROM

Some databases such as PostgreSQL and MySQL support a syntax “UPDATE FROM” where additional tables may be stated directly in a special FROM clause. This syntax will be generated implicitly when additional tables are located in the WHERE clause of the statement:

>>> update_stmt = (
...    update(user_table).
...    where(user_table.c.id == address_table.c.user_id).
...    where(address_table.c.email_address == 'patrick@aol.com').
...    values(fullname='Pat')
...  )
>>> print(update_stmt)
UPDATE user_account SET fullname=:fullname FROM address
WHERE user_account.id = address.user_id AND address.email_address = :email_address_1

There is also a MySQL specific syntax that can UPDATE multiple tables. This requires we refer to Table objects in the VALUES clause in order to refer to additional tables:

>>> update_stmt = (
...    update(user_table).
...    where(user_table.c.id == address_table.c.user_id).
...    where(address_table.c.email_address == 'patrick@aol.com').
...    values(
...        {
...            user_table.c.fullname: "Pat",
...            address_table.c.email_address: "pat@aol.com"
...        }
...    )
...  )
>>> from sqlalchemy.dialects import mysql
>>> print(update_stmt.compile(dialect=mysql.dialect()))
UPDATE user_account, address
SET address.email_address=%s, user_account.fullname=%s
WHERE user_account.id = address.user_id AND address.email_address = %s

Parameter Ordered Updates

Another MySQL-only behavior is that the order of parameters in the SET clause of an UPDATE actually impacts the evaluation of each expression. For this use case, the Update.ordered_values() method accepts a sequence of tuples so that this order may be controlled 2:

>>> update_stmt = (
...     update(some_table).
...     ordered_values(
...         (some_table.c.y, 20),
...         (some_table.c.x, some_table.c.y + 10)
...     )
... )
>>> print(update_stmt)
UPDATE some_table SET y=:y, x=(some_table.y + :y_1)