ORM Querying Guide

This section provides an overview of emitting queries with the SQLAlchemy ORM using 2.0 style usage.

Readers of this section should be familiar with the SQLAlchemy overview at SQLAlchemy 2.0 Tutorial, and in particular most of the content here expands upon the content at Selecting Rows with Core or ORM.

Attention legacy users

In the SQLAlchemy 2.x series, SQL SELECT statements for the ORM are constructed using the same select() construct as is used in Core, which is then invoked in terms of a Session using the Session.execute() method (as are the update() and delete() constructs now used for the UPDATE and DELETE with arbitrary WHERE clause feature). However, the legacy Query object, which performs these same steps as more of an “all-in-one” object, continues to remain available as a thin facade over this new system, to support applications that were built on the 1.x series without the need for wholesale replacement of all queries. For reference on this object, see the section Legacy Query API.

SELECT statements

SELECT statements are produced by the select() function which returns a Select object:

>>> from sqlalchemy import select
>>> stmt = select(User).where(User.name == 'spongebob')

To invoke a Select with the ORM, it is passed to Session.execute():

sql>>> result = session.execute(stmt)
>>> for user_obj in result.scalars():
...     print(f"{user_obj.name} {user_obj.fullname}")
spongebob Spongebob Squarepants

Selecting ORM Entities and Attributes

The select() construct accepts ORM entities, including mapped classes as well as class-level attributes representing mapped columns, which are converted into ORM-annotated FromClause and ColumnElement elements at construction time.

A Select object that contains ORM-annotated entities is normally executed using a Session object, and not a Connection object, so that ORM-related features may take effect, including that instances of ORM-mapped objects may be returned. When using the Connection directly, result rows will only contain column-level data.

Below we select from the User entity, producing a Select that selects from the mapped Table to which User is mapped:

sql>>> result = session.execute(select(User).order_by(User.id))

When selecting from ORM entities, the entity itself is returned in the result as a row with a single element, as opposed to a series of individual columns; for example above, the Result returns Row objects that have just a single element per row, that element holding onto a User object:

>>> result.fetchone()
(User(id=1, name='spongebob', fullname='Spongebob Squarepants'),)

When selecting a list of single-element rows containing ORM entities, it is typical to skip the generation of Row objects and instead receive ORM entities directly, which is achieved using the Result.scalars() method:

>>> result.scalars().all()
[User(id=2, name='sandy', fullname='Sandy Cheeks'),
 User(id=3, name='patrick', fullname='Patrick Star'),
 User(id=4, name='squidward', fullname='Squidward Tentacles'),
 User(id=5, name='ehkrabs', fullname='Eugene H. Krabs')]

ORM Entities are named in the result row based on their class name, such as below where we SELECT from both User and Address at the same time:

>>> stmt = select(User, Address).join(User.addresses).order_by(User.id, Address.id)

sql>>> for row in session.execute(stmt):
...    print(f"{row.User.name} {row.Address.email_address}")
spongebob spongebob@sqlalchemy.org
sandy sandy@sqlalchemy.org
sandy squirrel@squirrelpower.org
patrick pat999@aol.com
squidward stentcl@sqlalchemy.org

Selecting Individual Attributes

The attributes on a mapped class, such as User.name and Address.email_address, have a similar behavior as that of the entity class itself such as User in that they are automatically converted into ORM-annotated Core objects when passed to select(). They may be used in the same way as table columns are used:

sql>>> result = session.execute(
...     select(User.name, Address.email_address).
...     join(User.addresses).
...     order_by(User.id, Address.id)
... )

ORM attributes, themselves known as InstrumentedAttribute objects, can be used in the same way as any ColumnElement, and are delivered in result rows just the same way, such as below where we refer to their values by column name within each row:

>>> for row in result:
...     print(f"{row.name}  {row.email_address}")
spongebob  spongebob@sqlalchemy.org
sandy  sandy@sqlalchemy.org
sandy  squirrel@squirrelpower.org
patrick  pat999@aol.com
squidward  stentcl@sqlalchemy.org

Grouping Selected Attributes with Bundles

The Bundle construct is an extensible ORM-only construct that allows sets of column expressions to be grouped in result rows:

>>> from sqlalchemy.orm import Bundle
>>> stmt = select(
...     Bundle("user", User.name, User.fullname),
...     Bundle("email", Address.email_address)
... ).join_from(User, Address)
sql>>> for row in session.execute(stmt):
...     print(f"{row.user.name} {row.email.email_address}")
spongebob spongebob@sqlalchemy.org
sandy sandy@sqlalchemy.org
sandy squirrel@squirrelpower.org
patrick pat999@aol.com
squidward stentcl@sqlalchemy.org

The Bundle is potentially useful for creating lightweight views as well as custom column groupings such as mappings.

See also

Column Bundles - in the ORM loading documentation.

Selecting ORM Aliases

As discussed in the tutorial at Using Aliases, to create a SQL alias of an ORM entity is achieved using the aliased() construct against a mapped class:

>>> from sqlalchemy.orm import aliased
>>> u1 = aliased(User)
>>> print(select(u1).order_by(u1.id))
SELECT user_account_1.id, user_account_1.name, user_account_1.fullname FROM user_account AS user_account_1 ORDER BY user_account_1.id

As is the case when using Table.alias(), the SQL alias is anonymously named. For the case of selecting the entity from a row with an explicit name, the aliased.name parameter may be passed as well:

>>> from sqlalchemy.orm import aliased
>>> u1 = aliased(User, name="u1")
>>> stmt = select(u1).order_by(u1.id)
sql>>> row = session.execute(stmt).first()
>>> print(f"{row.u1.name}")
spongebob

The aliased construct is also central to making use of subqueries with the ORM; the sections Selecting Entities from Subqueries and Joining to Subqueries discusses this further.

Getting ORM Results from Textual and Core Statements

The ORM supports loading of entities from SELECT statements that come from other sources. The typical use case is that of a textual SELECT statement, which in SQLAlchemy is represented using the text() construct. The text() construct, once constructed, can be augmented with information about the ORM-mapped columns that the statement would load; this can then be associated with the ORM entity itself so that ORM objects can be loaded based on this statement.

Given a textual SQL statement we’d like to load from:

>>> from sqlalchemy import text
>>> textual_sql = text("SELECT id, name, fullname FROM user_account ORDER BY id")

We can add column information to the statement by using the TextClause.columns() method; when this method is invoked, the TextClause object is converted into a TextualSelect object, which takes on a role that is comparable to the Select construct. The TextClause.columns() method is typically passed Column objects or equivalent, and in this case we can make use of the ORM-mapped attributes on the User class directly:

>>> textual_sql = textual_sql.columns(User.id, User.name, User.fullname)

We now have an ORM-configured SQL construct that as given, can load the “id”, “name” and “fullname” columns separately. To use this SELECT statement as a source of complete User entities instead, we can link these columns to a regular ORM-enabled Select construct using the Select.from_statement() method:

>>> # using from_statement()
>>> orm_sql = select(User).from_statement(textual_sql)
>>> for user_obj in session.execute(orm_sql).scalars():
...     print(user_obj)
SELECT id, name, fullname FROM user_account ORDER BY id [...] ()
User(id=1, name='spongebob', fullname='Spongebob Squarepants') User(id=2, name='sandy', fullname='Sandy Cheeks') User(id=3, name='patrick', fullname='Patrick Star') User(id=4, name='squidward', fullname='Squidward Tentacles') User(id=5, name='ehkrabs', fullname='Eugene H. Krabs')

The same TextualSelect object can also be converted into a subquery using the TextualSelect.subquery() method, and linked to the User entity to it using the aliased() construct, in a similar manner as discussed below in Selecting Entities from Subqueries:

>>> # using aliased() to select from a subquery
>>> orm_subquery = aliased(User, textual_sql.subquery())
>>> stmt = select(orm_subquery)
>>> for user_obj in session.execute(stmt).scalars():
...     print(user_obj)
SELECT anon_1.id, anon_1.name, anon_1.fullname FROM (SELECT id, name, fullname FROM user_account ORDER BY id) AS anon_1 [...] ()
User(id=1, name='spongebob', fullname='Spongebob Squarepants') User(id=2, name='sandy', fullname='Sandy Cheeks') User(id=3, name='patrick', fullname='Patrick Star') User(id=4, name='squidward', fullname='Squidward Tentacles') User(id=5, name='ehkrabs', fullname='Eugene H. Krabs')

The difference between using the TextualSelect directly with Select.from_statement() versus making use of aliased() is that in the former case, no subquery is produced in the resulting SQL. This can in some scenarios be advantageous from a performance or complexity perspective.

See also

Using INSERT, UPDATE and ON CONFLICT (i.e. upsert) to return ORM Objects - The Select.from_statement() method also works with DML statements that support RETURNING.

Selecting Entities from Subqueries

The aliased() construct discussed in the previous section can be used with any Subuqery construct that comes from a method such as Select.subquery() to link ORM entities to the columns returned by that subquery; there must be a column correspondence relationship between the columns delivered by the subquery and the columns to which the entity is mapped, meaning, the subquery needs to be ultimately derived from those entities, such as in the example below:

>>> inner_stmt = select(User).where(User.id < 7).order_by(User.id)
>>> subq = inner_stmt.subquery()
>>> aliased_user = aliased(User, subq)
>>> stmt = select(aliased_user)
>>> for user_obj in session.execute(stmt).scalars():
...     print(user_obj)
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.id < ? ORDER BY user_account.id) AS anon_1 [generated in ...] (7,)
User(id=1, name='spongebob', fullname='Spongebob Squarepants') User(id=2, name='sandy', fullname='Sandy Cheeks') User(id=3, name='patrick', fullname='Patrick Star') User(id=4, name='squidward', fullname='Squidward Tentacles') User(id=5, name='ehkrabs', fullname='Eugene H. Krabs')

Selecting Entities from UNIONs and other set operations

The union() and union_all() functions are the most common set operations, which along with other set operations such as except_(), intersect() and others deliver an object known as a CompoundSelect, which is composed of multiple Select constructs joined by a set-operation keyword. ORM entities may be selected from simple compound selects using the Select.from_statement() method illustrated previously at Getting ORM Results from Textual and Core Statements. In this method, the UNION statement is the complete statement that will be rendered, no additional criteria can be added after Select.from_statement() is used:

>>> from sqlalchemy import union_all
>>> u = union_all(
...     select(User).where(User.id < 2),
...     select(User).where(User.id == 3)
... ).order_by(User.id)
>>> stmt = select(User).from_statement(u)
>>> for user_obj in session.execute(stmt).scalars():
...     print(user_obj)
SELECT user_account.id, user_account.name, user_account.fullname FROM user_account WHERE user_account.id < ? UNION ALL SELECT user_account.id, user_account.name, user_account.fullname FROM user_account WHERE user_account.id = ? ORDER BY id [generated in ...] (2, 3)
User(id=1, name='spongebob', fullname='Spongebob Squarepants') User(id=3, name='patrick', fullname='Patrick Star')

A CompoundSelect construct can be more flexibly used within a query that can be further modified by organizing it into a subquery and linking it to an ORM entity using aliased(), as illustrated previously at Selecting Entities from Subqueries. In the example below, we first use CompoundSelect.subquery() to create a subquery of the UNION ALL statement, we then package that into the aliased() construct where it can be used like any other mapped entity in a select() construct, including that we can add filtering and order by criteria based on its exported columns:

>>> subq = union_all(
...     select(User).where(User.id < 2),
...     select(User).where(User.id == 3)
... ).subquery()
>>> user_alias = aliased(User, subq)
>>> stmt = select(user_alias).order_by(user_alias.id)
>>> for user_obj in session.execute(stmt).scalars():
...     print(user_obj)
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.id < ? UNION ALL SELECT user_account.id AS id, user_account.name AS name, user_account.fullname AS fullname FROM user_account WHERE user_account.id = ?) AS anon_1 ORDER BY anon_1.id [generated in ...] (2, 3)
User(id=1, name='spongebob', fullname='Spongebob Squarepants') User(id=3, name='patrick', fullname='Patrick Star')

Joins

The Select.join() and Select.join_from() methods are used to construct SQL JOINs against a SELECT statement.

This section will detail ORM use cases for these methods. For a general overview of their use from a Core perspective, see Explicit FROM clauses and JOINs in the SQLAlchemy 2.0 Tutorial.

The usage of Select.join() in an ORM context for 2.0 style queries is mostly equivalent, minus legacy use cases, to the usage of the Query.join() method in 1.x style queries.

Simple Relationship Joins

Consider a mapping between two classes User and Address, with a relationship User.addresses representing a collection of Address objects associated with each User. The most common usage of Select.join() is to create a JOIN along this relationship, using the User.addresses attribute as an indicator for how this should occur:

>>> stmt = select(User).join(User.addresses)

Where above, the call to Select.join() along User.addresses will result in SQL approximately equivalent to:

>>> print(stmt)
SELECT user_account.id, user_account.name, user_account.fullname FROM user_account JOIN address ON user_account.id = address.user_id

In the above example we refer to User.addresses as passed to Select.join() as the “on clause”, that is, it indicates how the “ON” portion of the JOIN should be constructed.

Chaining Multiple Joins

To construct a chain of joins, multiple Select.join() calls may be used. The relationship-bound attribute implies both the left and right side of the join at once. Consider additional entities Order and Item, where the User.orders relationship refers to the Order entity, and the Order.items relationship refers to the Item entity, via an association table order_items. Two Select.join() calls will result in a JOIN first from User to Order, and a second from Order to Item. However, since Order.items is a many to many relationship, it results in two separate JOIN elements, for a total of three JOIN elements in the resulting SQL:

>>> stmt = (
...     select(User).
...     join(User.orders).
...     join(Order.items)
... )
>>> print(stmt)
SELECT user_account.id, user_account.name, user_account.fullname FROM user_account JOIN user_order ON user_account.id = user_order.user_id JOIN order_items AS order_items_1 ON user_order.id = order_items_1.order_id JOIN item ON item.id = order_items_1.item_id

The order in which each call to the Select.join() method is significant only to the degree that the “left” side of what we would like to join from needs to be present in the list of FROMs before we indicate a new target. Select.join() would not, for example, know how to join correctly if we were to specify select(User).join(Order.items).join(User.orders), and would raise an error. In correct practice, the Select.join() method is invoked in such a way that lines up with how we would want the JOIN clauses in SQL to be rendered, and each call should represent a clear link from what precedes it.

All of the elements that we target in the FROM clause remain available as potential points to continue joining FROM. We can continue to add other elements to join FROM the User entity above, for example adding on the User.addresses relationship to our chain of joins:

>>> stmt = (
...     select(User).
...     join(User.orders).
...     join(Order.items).
...     join(User.addresses)
... )
>>> print(stmt)
SELECT user_account.id, user_account.name, user_account.fullname FROM user_account JOIN user_order ON user_account.id = user_order.user_id JOIN order_items AS order_items_1 ON user_order.id = order_items_1.order_id JOIN item ON item.id = order_items_1.item_id JOIN address ON user_account.id = address.user_id

Joins to a Target Entity or Selectable

A second form of Select.join() allows any mapped entity or core selectable construct as a target. In this usage, Select.join() will attempt to infer the ON clause for the JOIN, using the natural foreign key relationship between two entities:

>>> stmt = select(User).join(Address)
>>> print(stmt)
SELECT user_account.id, user_account.name, user_account.fullname FROM user_account JOIN address ON user_account.id = address.user_id

In the above calling form, Select.join() is called upon to infer the “on clause” automatically. This calling form will ultimately raise an error if either there are no ForeignKeyConstraint setup between the two mapped Table constructs, or if there are multiple ForeignKeyConstraint linakges between them such that the appropriate constraint to use is ambiguous.

Note

When making use of Select.join() or Select.join_from() without indicating an ON clause, ORM configured relationship() constructs are not taken into account. Only the configured ForeignKeyConstraint relationships between the entities at the level of the mapped Table objects are consulted when an attempt is made to infer an ON clause for the JOIN.

Joins to a Target with an ON Clause

The third calling form allows both the target entity as well as the ON clause to be passed explicitly. A example that includes a SQL expression as the ON clause is as follows:

>>> stmt = select(User).join(Address, User.id==Address.user_id)
>>> print(stmt)
SELECT user_account.id, user_account.name, user_account.fullname FROM user_account JOIN address ON user_account.id = address.user_id

The expression-based ON clause may also be the relationship-bound attribute; this form in fact states the target of Address twice, however this is accepted:

>>> stmt = select(User).join(Address, User.addresses)
>>> print(stmt)
SELECT user_account.id, user_account.name, user_account.fullname FROM user_account JOIN address ON user_account.id = address.user_id

The above syntax has more functionality if we use it in terms of aliased entities. The default target for User.addresses is the Address class, however if we pass aliased forms using aliased(), the aliased() form will be used as the target, as in the example below:

>>> a1 = aliased(Address)
>>> a2 = aliased(Address)
>>> stmt = (
...     select(User).
...     join(a1, User.addresses).
...     join(a2, User.addresses).
...     where(a1.email_address == 'ed@foo.com').
...     where(a2.email_address == 'ed@bar.com')
... )
>>> print(stmt)
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

When using relationship-bound attributes, the target entity can also be substituted with an aliased entity by using the PropComparator.of_type() method. The same example using this method would be:

>>> stmt = (
...     select(User).
...     join(User.addresses.of_type(a1)).
...     join(User.addresses.of_type(a2)).
...     where(a1.email_address == 'ed@foo.com').
...     where(a2.email_address == 'ed@bar.com')
... )
>>> print(stmt)
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

Augmenting Built-in ON Clauses

As a substitute for providing a full custom ON condition for an existing relationship, the PropComparator.and_() function may be applied to a relationship attribute to augment additional criteria into the ON clause; the additional criteria will be combined with the default criteria using AND. Below, the ON criteria between user_account and address contains two separate elements joined by AND, the first one being the natural join along the foreign key, and the second being a custom limiting criteria:

>>> stmt = (
...     select(User).
...     join(User.addresses.and_(Address.email_address != 'foo@bar.com'))
... )
>>> print(stmt)
SELECT user_account.id, user_account.name, user_account.fullname FROM user_account JOIN address ON user_account.id = address.user_id AND address.email_address != :email_address_1

See also

The PropComparator.and_() method also works with loader strategies. See the section Adding Criteria to loader options for an example.

Joining to Subqueries

The target of a join may be any “selectable” entity which usefully includes subuqeries. When using the ORM, it is typical that these targets are stated in terms of an aliased() construct, but this is not strictly required particularly if the joined entity is not being returned in the results. For example, to join from the User entity to the Address entity, where the Address entity is represented as a row limited subquery, we first construct a Subquery object using Select.subquery(), which may then be used as the target of the Select.join() method:

>>> subq = (
...     select(Address).
...     where(Address.email_address == 'pat999@aol.com').
...     subquery()
... )
>>> stmt = select(User).join(subq, User.id == subq.c.user_id)
>>> print(stmt)
SELECT user_account.id, user_account.name, user_account.fullname FROM user_account JOIN (SELECT address.id AS id, address.user_id AS user_id, address.email_address AS email_address FROM address WHERE address.email_address = :email_address_1) AS anon_1 ON user_account.id = anon_1.user_id

The above SELECT statement when invoked via Session.execute() will return rows that contain User entities, but not Address entities. In order to add Address entities to the set of entities that would be returned in result sets, we construct an aliased() object against the Address entity and the custom subquery. Note we also apply a name "address" to the aliased() construct so that we may refer to it by name in the result row:

>>> address_subq = aliased(Address, subq, name="address")
>>> stmt = select(User, address_subq).join(address_subq)
>>> for row in session.execute(stmt):
...     print(f"{row.User} {row.address}")
SELECT user_account.id, user_account.name, user_account.fullname, anon_1.id AS id_1, anon_1.user_id, anon_1.email_address FROM user_account JOIN (SELECT address.id AS id, address.user_id AS user_id, address.email_address AS email_address FROM address WHERE address.email_address = ?) AS anon_1 ON user_account.id = anon_1.user_id [...] ('pat999@aol.com',)
User(id=3, name='patrick', fullname='Patrick Star') Address(id=4, email_address='pat999@aol.com')

The same subquery may be referred towards by multiple entities as well, for a subquery that represents more than one entity. The subquery itself will remain unique within the statement, while the entities that are linked to it using aliased refer to distinct sets of columns:

>>> user_address_subq = (
...        select(User.id, User.name, Address.id, Address.email_address).
...        join_from(User, Address).
...        where(Address.email_address.in_(['pat999@aol.com', 'squirrel@squirrelpower.org'])).
...        subquery()
... )
>>> user_alias = aliased(User, user_address_subq, name="user")
>>> address_alias = aliased(Address, user_address_subq, name="address")
>>> stmt = select(user_alias, address_alias).where(user_alias.name == 'sandy')
>>> for row in session.execute(stmt):
...     print(f"{row.user} {row.address}")
SELECT anon_1.id, anon_1.name, anon_1.id_1, anon_1.email_address FROM (SELECT user_account.id AS id, user_account.name AS name, address.id AS id_1, address.email_address AS email_address FROM user_account JOIN address ON user_account.id = address.user_id WHERE address.email_address IN (?, ?)) AS anon_1 WHERE anon_1.name = ? [...] ('pat999@aol.com', 'squirrel@squirrelpower.org', 'sandy')
User(id=2, name='sandy', fullname='Sandy Cheeks') Address(id=3, email_address='squirrel@squirrelpower.org')

Controlling what to Join From

In cases where the left side of the current state of Select is not in line with what we want to join from, the Select.join_from() method may be used:

>>> stmt = select(Address).join_from(User, User.addresses).where(User.name == 'sandy')
>>> print(stmt)
SELECT address.id, address.user_id, address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id
WHERE user_account.name = :name_1

The Select.join_from() method accepts two or three arguments, either in the form <join from>, <onclause>, or <join from>, <join to>, [<onclause>]:

>>> stmt = select(Address).join_from(User, Address).where(User.name == 'sandy')
>>> print(stmt)
SELECT address.id, address.user_id, address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id
WHERE user_account.name = :name_1

To set up the initial FROM clause for a SELECT such that Select.join() can be used subsequent, the Select.select_from() method may also be used:

>>> stmt = select(Address).select_from(User).join(Address).where(User.name == 'sandy')
>>> print(stmt)
SELECT address.id, address.user_id, address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id
WHERE user_account.name = :name_1

Tip

The Select.select_from() method does not actually have the final say on the order of tables in the FROM clause. If the statement also refers to a Join construct that refers to existing tables in a different order, the Join construct takes precedence. When we use methods like Select.join() and Select.join_from(), these methods are ultimately creating such a Join object. Therefore we can see the contents of Select.select_from() being overridden in a case like this:

>>> stmt = select(Address).select_from(User).join(Address.user).where(User.name == 'sandy')
>>> print(stmt)
SELECT address.id, address.user_id, address.email_address
FROM address JOIN user_account ON user_account.id = address.user_id
WHERE user_account.name = :name_1

Where above, we see that the FROM clause is address JOIN user_account, even though we stated select_from(User) first. Because of the .join(Address.user) method call, the statement is ultimately equivalent to the following:

>>> user_table = User.__table__
>>> address_table = Address.__table__
>>> from sqlalchemy.sql import join
>>>
>>> j = address_table.join(user_table, user_table.c.id == address_table.c.user_id)
>>> stmt = (
...     select(address_table).select_from(user_table).select_from(j).
...     where(user_table.c.name == 'sandy')
... )
>>> print(stmt)
SELECT address.id, address.user_id, address.email_address
FROM address JOIN user_account ON user_account.id = address.user_id
WHERE user_account.name = :name_1

The Join construct above is added as another entry in the Select.select_from() list which supersedes the previous entry.

Special Relationship Operators

As detailed in the SQLAlchemy 2.0 Tutorial at Using Relationships in Queries, ORM attributes mapped by relationship() may be used in a variety of ways as SQL construction helpers. In addition to the above documentation on Joins, relationships may produce criteria to be used in the WHERE clause as well. See the linked sections below.

See also

Sections in the Working with Related Objects section of the SQLAlchemy 2.0 Tutorial:

ORM Loader Options

Loader options are objects that are passed to the Select.options() method which affect the loading of both column and relationship-oriented attributes. The majority of loader options descend from the Load hierarchy. For a complete overview of using loader options, see the linked sections below.

See also

ORM Execution Options

Execution options are keyword arguments that are passed to an “execution_options” method, which take place at the level of statement execution. The primary “execution option” method is in Core at Connection.execution_options(). In the ORM, execution options may also be passed to Session.execute() using the Session.execute.execution_options parameter. Perhaps more succinctly, most execution options, including those specific to the ORM, can be assigned to a statement directly, using the Executable.execution_options() method, so that the options may be associated directly with the statement instead of being configured separately. The examples below will use this form.

Populate Existing

The populate_existing execution option ensures that for all rows loaded, the corresponding instances in the Session will be fully refreshed, erasing any existing data within the objects (including pending changes) and replacing with the data loaded from the result.

Example use looks like:

>>> stmt = select(User).execution_options(populate_existing=True)
sql>>> result = session.execute(stmt)

Normally, ORM objects are only loaded once, and if they are matched up to the primary key in a subsequent result row, the row is not applied to the object. This is both to preserve pending, unflushed changes on the object as well as to avoid the overhead and complexity of refreshing data which is already there. The Session assumes a default working model of a highly isolated transaction, and to the degree that data is expected to change within the transaction outside of the local changes being made, those use cases would be handled using explicit steps such as this method.

Using populate_existing, any set of objects that matches a query can be refreshed, and it also allows control over relationship loader options. E.g. to refresh an instance while also refreshing a related set of objects:

stmt = (
    select(User).
    where(User.name.in_(names)).
    execution_options(populate_existing=True).
    options(selectinload(User.addresses)
)
# will refresh all matching User objects as well as the related
# Address objects
users = session.execute(stmt).scalars().all()

Another use case for populate_existing is in support of various attribute loading features that can change how an attribute is loaded on a per-query basis. Options for which this apply include:

The populate_existing execution option is equvialent to the Query.populate_existing() method in 1.x style ORM queries.

Autoflush

This option when passed as False will cause the Session to not invoke the “autoflush” step. It’s equivalent to using the Session.no_autoflush context manager to disable autoflush:

>>> stmt = select(User).execution_options(autoflush=False)
sql>>> session.execute(stmt)

This option will also work on ORM-enabled Update and Delete queries.

The autoflush execution option is equvialent to the Query.autoflush() method in 1.x style ORM queries.

See also

Flushing

Yield Per

The yield_per execution option is an integer value which will cause the Result to yield only a fixed count of rows at a time. It is often useful to use with a result partitioning method such as Result.partitions(), e.g.:

>>> stmt = select(User).execution_options(yield_per=10)
sql>>> for partition in session.execute(stmt).partitions(10):
...     for row in partition:
...         print(row)
(User(id=1, name='spongebob', fullname='Spongebob Squarepants'),)
...

The purpose of this method is when fetching very large result sets (> 10K rows), to batch results in sub-collections and yield them out partially, so that the Python interpreter doesn’t need to declare very large areas of memory which is both time consuming and leads to excessive memory use. The performance from fetching hundreds of thousands of rows can often double when a suitable yield-per setting (e.g. approximately 1000) is used, even with DBAPIs that buffer rows (which are most).

When yield_per is used, the Connection.execution_options.stream_results option is also set for the Core execution, so that a streaming / server side cursor will be used if the backend supports it 1

The yield_per execution option is not compatible with subqueryload eager loading or joinedload eager loading when using collections. It is potentially compatible with selectinload eager loading, provided the database driver supports multiple, independent cursors 2 .

Additionally, the yield_per execution option is not compatible with the Result.unique() method; as this method relies upon storing a complete set of identities for all rows, it would necessarily defeat the purpose of using yield_per which is to handle an arbitrarily large number of rows.

Changed in version 1.4.6: An exception is raised when ORM rows are fetched from a Result object that makes use of the Result.unique() filter, at the same time as the yield_per execution option is used.

The yield_per execution option is equvialent to the Query.yield_per() method in 1.x style ORM queries.

1

currently known are psycopg2, mysqldb and pymysql. Other backends will pre buffer all rows. The memory use of raw database rows is much less than that of an ORM-mapped object, but should still be taken into consideration when benchmarking.

2

the psycopg2 and pysqlite drivers are known to work, drivers for MySQL and SQL Server ODBC drivers do not.

ORM Update / Delete with Arbitrary WHERE clause

The Session.execute() method, in addition to handling ORM-enabled Select objects, can also accommodate ORM-enabled Update and Delete objects, which UPDATE or DELETE any number of database rows while also being able to synchronize the state of matching objects locally present in the Session. See the section UPDATE and DELETE with arbitrary WHERE clause for background on this feature.

Inspecting entities and columns from ORM-enabled SELECT and DML statements

The select() construct, as well as the insert(), update() and delete() constructs (for the latter DML constructs, as of SQLAlchemy 1.4.33), all support the ability to inspect the entities in which these statements are created against, as well as the columns and datatypes that would be returned in a result set.

For a Select object, this information is available from the Select.column_descriptions attribute. This attribute operates in the same way as the legacy Query.column_descriptions attribute. The format returned is a list of dictionaries:

>>> from pprint import pprint
>>> user_alias = aliased(User, name='user2')
>>> stmt = select(User, User.id, user_alias)
>>> pprint(stmt.column_descriptions)
[{'aliased': False,
    'entity': <class 'User'>,
    'expr': <class 'User'>,
    'name': 'User',
    'type': <class 'User'>},
    {'aliased': False,
    'entity': <class 'User'>,
    'expr': <....InstrumentedAttribute object at ...>,
    'name': 'id',
    'type': Integer()},
    {'aliased': True,
    'entity': <AliasedClass ...; User>,
    'expr': <AliasedClass ...; User>,
    'name': 'user2',
    'type': <class 'User'>}]

When Select.column_descriptions is used with non-ORM objects such as plain Table or Column objects, the entries will contain basic information about individual columns returned in all cases:

>>> stmt = select(user_table, address_table.c.id)
>>> pprint(stmt.column_descriptions)
[{'expr': Column('id', Integer(), table=<user_account>, primary_key=True, nullable=False),
    'name': 'id',
    'type': Integer()},
    {'expr': Column('name', String(length=30), table=<user_account>),
    'name': 'name',
    'type': String(length=30)},
    {'expr': Column('fullname', String(), table=<user_account>),
    'name': 'fullname',
    'type': String()},
    {'expr': Column('id', Integer(), table=<address>, primary_key=True, nullable=False),
    'name': 'id_1',
    'type': Integer()}]

Changed in version 1.4.33: The Select.column_descriptions attribute now returns a value when used against a Select that is not ORM-enabled. Previously, this would raise NotImplementedError.

For insert(), update() and delete() constructs, there are two separate attributes. One is UpdateBase.entity_description which returns information about the primary ORM entity and database table which the DML construct would be affecting:

>>> from sqlalchemy import update
>>> stmt = update(User).values(name="somename").returning(User.id)
>>> pprint(stmt.entity_description)
{'entity': <class 'User'>,
    'expr': <class 'User'>,
    'name': 'User',
    'table': Table('user_account', ...),
    'type': <class 'User'>}

Tip

The UpdateBase.entity_description includes an entry "table" which is actually the table to be inserted, updated or deleted by the statement, which is not always the same as the SQL “selectable” to which the class may be mapped. For example, in a joined-table inheritance scenario, "table" will refer to the local table for the given entity.

The other is UpdateBase.returning_column_descriptions which delivers information about the columns present in the RETURNING collection in a manner roughly similar to that of Select.column_descriptions:

>>> pprint(stmt.returning_column_descriptions)
[{'aliased': False,
    'entity': <class 'User'>,
    'expr': <sqlalchemy.orm.attributes.InstrumentedAttribute ...>,
    'name': 'id',
    'type': Integer()}]

New in version 1.4.33: Added the UpdateBase.entity_description and UpdateBase.returning_column_descriptions attributes.

Additional ORM API Constructs

Object Name Description

aliased(element[, alias, name, flat, ...])

Produce an alias of the given element, usually an AliasedClass instance.

AliasedClass

Represents an “aliased” form of a mapped class for usage with Query.

AliasedInsp

Provide an inspection interface for an AliasedClass object.

Bundle

A grouping of SQL expressions that are returned by a Query under one namespace.

join(left, right[, onclause, isouter, ...])

Produce an inner join between left and right clauses.

outerjoin(left, right[, onclause, full])

Produce a left outer join between left and right clauses.

with_loader_criteria(entity_or_base, where_criteria[, loader_only, include_aliases, ...])

Add additional WHERE criteria to the load for all occurrences of a particular entity.

with_parent(instance, prop[, from_entity])

Create filtering criterion that relates this query’s primary entity to the given related instance, using established relationship() configuration.

function sqlalchemy.orm.aliased(element: Union[_EntityType[_O], FromClause], alias: Optional[Union[Alias, Subquery]] = None, name: Optional[str] = None, flat: bool = False, adapt_on_names: bool = False) Union[AliasedClass[_O], FromClause, AliasedType[_O]]

Produce an alias of the given element, usually an AliasedClass instance.

E.g.:

my_alias = aliased(MyClass)

stmt = select(MyClass, my_alias).filter(MyClass.id > my_alias.id)
result = session.execute(stmt)

The aliased() function is used to create an ad-hoc mapping of a mapped class to a new selectable. By default, a selectable is generated from the normally mapped selectable (typically a Table ) using the FromClause.alias() method. However, aliased() can also be used to link the class to a new select() statement. Also, the with_polymorphic() function is a variant of aliased() that is intended to specify a so-called “polymorphic selectable”, that corresponds to the union of several joined-inheritance subclasses at once.

For convenience, the aliased() function also accepts plain FromClause constructs, such as a Table or select() construct. In those cases, the FromClause.alias() method is called on the object and the new Alias object returned. The returned Alias is not ORM-mapped in this case.

ormtutorial_aliases - in the legacy Object Relational Tutorial

Parameters
  • element – element to be aliased. Is normally a mapped class, but for convenience can also be a FromClause element.

  • alias – Optional selectable unit to map the element to. This is usually used to link the object to a subquery, and should be an aliased select construct as one would produce from the Query.subquery() method or the Select.subquery() or Select.alias() methods of the select() construct.

  • name – optional string name to use for the alias, if not specified by the alias parameter. The name, among other things, forms the attribute name that will be accessible via tuples returned by a Query object. Not supported when creating aliases of Join objects.

  • flat – Boolean, will be passed through to the FromClause.alias() call so that aliases of Join objects will alias the individual tables inside the join, rather than creating a subquery. This is generally supported by all modern databases with regards to right-nested joins and generally produces more efficient queries.

  • adapt_on_names

    if True, more liberal “matching” will be used when mapping the mapped columns of the ORM entity to those of the given selectable - a name-based match will be performed if the given selectable doesn’t otherwise have a column that corresponds to one on the entity. The use case for this is when associating an entity with some derived selectable such as one that uses aggregate functions:

    class UnitPrice(Base):
        __tablename__ = 'unit_price'
        ...
        unit_id = Column(Integer)
        price = Column(Numeric)
    
    aggregated_unit_price = Session.query(
                                func.sum(UnitPrice.price).label('price')
                            ).group_by(UnitPrice.unit_id).subquery()
    
    aggregated_unit_price = aliased(UnitPrice,
                alias=aggregated_unit_price, adapt_on_names=True)

    Above, functions on aggregated_unit_price which refer to .price will return the func.sum(UnitPrice.price).label('price') column, as it is matched on the name “price”. Ordinarily, the “price” function wouldn’t have any “column correspondence” to the actual UnitPrice.price column as it is not a proxy of the original.

class sqlalchemy.orm.util.AliasedClass

Represents an “aliased” form of a mapped class for usage with Query.

The ORM equivalent of a alias() construct, this object mimics the mapped class using a __getattr__ scheme and maintains a reference to a real Alias object.

A primary purpose of AliasedClass is to serve as an alternate within a SQL statement generated by the ORM, such that an existing mapped entity can be used in multiple contexts. A simple example:

# find all pairs of users with the same name
user_alias = aliased(User)
session.query(User, user_alias).\
                join((user_alias, User.id > user_alias.id)).\
                filter(User.name == user_alias.name)

AliasedClass is also capable of mapping an existing mapped class to an entirely new selectable, provided this selectable is column- compatible with the existing mapped selectable, and it can also be configured in a mapping as the target of a relationship(). See the links below for examples.

The AliasedClass object is constructed typically using the aliased() function. It also is produced with additional configuration when using the with_polymorphic() function.

The resulting object is an instance of AliasedClass. This object implements an attribute scheme which produces the same attribute and method interface as the original mapped class, allowing AliasedClass to be compatible with any attribute technique which works on the original class, including hybrid attributes (see Hybrid Attributes).

The AliasedClass can be inspected for its underlying Mapper, aliased selectable, and other information using inspect():

from sqlalchemy import inspect
my_alias = aliased(MyClass)
insp = inspect(my_alias)

The resulting inspection object is an instance of AliasedInsp.

Class signature

class sqlalchemy.orm.AliasedClass (sqlalchemy.inspection.Inspectable, sqlalchemy.orm.ORMColumnsClauseRole)

method sqlalchemy.orm.util.AliasedClass.__init__(mapped_class_or_ac: _EntityType[_O], alias: Optional[FromClause] = None, name: Optional[str] = None, flat: bool = False, adapt_on_names: bool = False, with_polymorphic_mappers: Optional[Sequence[Mapper[Any]]] = None, with_polymorphic_discriminator: Optional[ColumnElement[Any]] = None, base_alias: Optional[AliasedInsp[Any]] = None, use_mapper_path: bool = False, represents_outer_join: bool = False)
method sqlalchemy.orm.util.AliasedClass.static __new__(cls, *args, **kwds)

inherited from the typing.Generic.__new__ method of Generic

class sqlalchemy.orm.util.AliasedInsp

Provide an inspection interface for an AliasedClass object.

The AliasedInsp object is returned given an AliasedClass using the inspect() function:

from sqlalchemy import inspect
from sqlalchemy.orm import aliased

my_alias = aliased(MyMappedClass)
insp = inspect(my_alias)

Attributes on AliasedInsp include:

  • entity - the AliasedClass represented.

  • mapper - the Mapper mapping the underlying class.

  • selectable - the Alias construct which ultimately represents an aliased Table or Select construct.

  • name - the name of the alias. Also is used as the attribute name when returned in a result tuple from Query.

  • with_polymorphic_mappers - collection of Mapper objects indicating all those mappers expressed in the select construct for the AliasedClass.

  • polymorphic_on - an alternate column or SQL expression which will be used as the “discriminator” for a polymorphic load.

Class signature

class sqlalchemy.orm.AliasedInsp (sqlalchemy.orm.ORMEntityColumnsClauseRole, sqlalchemy.orm.ORMFromClauseRole, sqlalchemy.sql.cache_key.HasCacheKey, sqlalchemy.orm.base.InspectionAttr, sqlalchemy.util.langhelpers.MemoizedSlots, sqlalchemy.inspection.Inspectable, typing.Generic)

method sqlalchemy.orm.util.AliasedInsp.__init__(entity: AliasedClass[_O], inspected: _InternalEntityType[_O], selectable: FromClause, name: Optional[str], with_polymorphic_mappers: Optional[Sequence[Mapper[Any]]], polymorphic_on: Optional[ColumnElement[Any]], _base_alias: Optional[AliasedInsp[Any]], _use_mapper_path: bool, adapt_on_names: bool, represents_outer_join: bool, nest_adapters: bool)
method sqlalchemy.orm.util.AliasedInsp.static __new__(cls, *args, **kwds)

inherited from the typing.Generic.__new__ method of Generic

class sqlalchemy.orm.Bundle

A grouping of SQL expressions that are returned by a Query under one namespace.

The Bundle essentially allows nesting of the tuple-based results returned by a column-oriented Query object. It also is extensible via simple subclassing, where the primary capability to override is that of how the set of expressions should be returned, allowing post-processing as well as custom return types, without involving ORM identity-mapped classes.

New in version 0.9.0.

See also

Column Bundles

Class signature

class sqlalchemy.orm.Bundle (sqlalchemy.orm.ORMColumnsClauseRole, sqlalchemy.sql.annotation.SupportsCloneAnnotations, sqlalchemy.sql.cache_key.MemoizedHasCacheKey, sqlalchemy.inspection.Inspectable, sqlalchemy.orm.base.InspectionAttr)

method sqlalchemy.orm.Bundle.__init__(name: str, *exprs: _ColumnExpressionArgument[Any], **kw: Any)

Construct a new Bundle.

e.g.:

bn = Bundle("mybundle", MyClass.x, MyClass.y)

for row in session.query(bn).filter(
        bn.c.x == 5).filter(bn.c.y == 4):
    print(row.mybundle.x, row.mybundle.y)
Parameters
  • name – name of the bundle.

  • *exprs – columns or SQL expressions comprising the bundle.

  • single_entity=False – if True, rows for this Bundle can be returned as a “single entity” outside of any enclosing tuple in the same manner as a mapped entity.

attribute sqlalchemy.orm.Bundle.c: ReadOnlyColumnCollection[str, KeyedColumnElement[Any]]

An alias for Bundle.columns.

attribute sqlalchemy.orm.Bundle.columns: ReadOnlyColumnCollection[str, KeyedColumnElement[Any]]

A namespace of SQL expressions referred to by this Bundle.

e.g.:

bn = Bundle("mybundle", MyClass.x, MyClass.y)

q = sess.query(bn).filter(bn.c.x == 5)

Nesting of bundles is also supported:

b1 = Bundle("b1",
        Bundle('b2', MyClass.a, MyClass.b),
        Bundle('b3', MyClass.x, MyClass.y)
    )

q = sess.query(b1).filter(
    b1.c.b2.c.a == 5).filter(b1.c.b3.c.y == 9)

See also

Bundle.c

method sqlalchemy.orm.Bundle.create_row_processor(query: Select[Any], procs: Sequence[Callable[[Row[Any]], Any]], labels: Sequence[str]) Callable[[Row[Any]], Any]

Produce the “row processing” function for this Bundle.

May be overridden by subclasses.

See also

Column Bundles - includes an example of subclassing.

method sqlalchemy.orm.Bundle.label(name)

Provide a copy of this Bundle passing a new label.

attribute sqlalchemy.orm.Bundle.single_entity = False

If True, queries for a single Bundle will be returned as a single entity, rather than an element within a keyed tuple.

function sqlalchemy.orm.with_loader_criteria(entity_or_base: _EntityType[Any], where_criteria: _ColumnExpressionArgument[bool], loader_only: bool = False, include_aliases: bool = False, propagate_to_loaders: bool = True, track_closure_variables: bool = True) LoaderCriteriaOption

Add additional WHERE criteria to the load for all occurrences of a particular entity.

New in version 1.4.

The with_loader_criteria() option is intended to add limiting criteria to a particular kind of entity in a query, globally, meaning it will apply to the entity as it appears in the SELECT query as well as within any subqueries, join conditions, and relationship loads, including both eager and lazy loaders, without the need for it to be specified in any particular part of the query. The rendering logic uses the same system used by single table inheritance to ensure a certain discriminator is applied to a table.

E.g., using 2.0-style queries, we can limit the way the User.addresses collection is loaded, regardless of the kind of loading used:

from sqlalchemy.orm import with_loader_criteria

stmt = select(User).options(
    selectinload(User.addresses),
    with_loader_criteria(Address, Address.email_address != 'foo'))
)

Above, the “selectinload” for User.addresses will apply the given filtering criteria to the WHERE clause.

Another example, where the filtering will be applied to the ON clause of the join, in this example using 1.x style queries:

q = session.query(User).outerjoin(User.addresses).options(
    with_loader_criteria(Address, Address.email_address != 'foo'))
)

The primary purpose of with_loader_criteria() is to use it in the SessionEvents.do_orm_execute() event handler to ensure that all occurrences of a particular entity are filtered in a certain way, such as filtering for access control roles. It also can be used to apply criteria to relationship loads. In the example below, we can apply a certain set of rules to all queries emitted by a particular Session:

session = Session(bind=engine)

@event.listens_for("do_orm_execute", session)
def _add_filtering_criteria(execute_state):

    if (
        execute_state.is_select
        and not execute_state.is_column_load
        and not execute_state.is_relationship_load
    ):
        execute_state.statement = execute_state.statement.options(
            with_loader_criteria(
                SecurityRole,
                lambda cls: cls.role.in_(['some_role']),
                include_aliases=True
            )
        )

In the above example, the SessionEvents.do_orm_execute() event will intercept all queries emitted using the Session. For those queries which are SELECT statements and are not attribute or relationship loads a custom with_loader_criteria() option is added to the query. The with_loader_criteria() option will be used in the given statement and will also be automatically propagated to all relationship loads that descend from this query.

The criteria argument given is a lambda that accepts a cls argument. The given class will expand to include all mapped subclass and need not itself be a mapped class.

Tip

When using with_loader_criteria() option in conjunction with the contains_eager() loader option, it’s important to note that with_loader_criteria() only affects the part of the query that determines what SQL is rendered in terms of the WHERE and FROM clauses. The contains_eager() option does not affect the rendering of the SELECT statement outside of the columns clause, so does not have any interaction with the with_loader_criteria() option. However, the way things “work” is that contains_eager() is meant to be used with a query that is already selecting from the additional entities in some way, where with_loader_criteria() can apply it’s additional criteria.

In the example below, assuming a mapping relationship as A -> A.bs -> B, the given with_loader_criteria() option will affect the way in which the JOIN is rendered:

stmt = select(A).join(A.bs).options(
    contains_eager(A.bs),
    with_loader_criteria(B, B.flag == 1)
)

Above, the given with_loader_criteria() option will affect the ON clause of the JOIN that is specified by .join(A.bs), so is applied as expected. The contains_eager() option has the effect that columns from B are added to the columns clause:

SELECT
    b.id, b.a_id, b.data, b.flag,
    a.id AS id_1,
    a.data AS data_1
FROM a JOIN b ON a.id = b.a_id AND b.flag = :flag_1

The use of the contains_eager() option within the above statement has no effect on the behavior of the with_loader_criteria() option. If the contains_eager() option were omitted, the SQL would be the same as regards the FROM and WHERE clauses, where with_loader_criteria() continues to add its criteria to the ON clause of the JOIN. The addition of contains_eager() only affects the columns clause, in that additional columns against b are added which are then consumed by the ORM to produce B instances.

Warning

The use of a lambda inside of the call to with_loader_criteria() is only invoked once per unique class. Custom functions should not be invoked within this lambda. See Using Lambdas to add significant speed gains to statement production for an overview of the “lambda SQL” feature, which is for advanced use only.

Parameters
  • entity_or_base – a mapped class, or a class that is a super class of a particular set of mapped classes, to which the rule will apply.

  • where_criteria – a Core SQL expression that applies limiting criteria. This may also be a “lambda:” or Python function that accepts a target class as an argument, when the given class is a base with many different mapped subclasses.

  • include_aliases – if True, apply the rule to aliased() constructs as well.

  • propagate_to_loaders

    defaults to True, apply to relationship loaders such as lazy loaders.

    See also

    ORM Query Events - includes examples of using with_loader_criteria().

    Adding global WHERE / ON criteria - basic example on how to combine with_loader_criteria() with the SessionEvents.do_orm_execute() event.

  • track_closure_variables

    when False, closure variables inside of a lambda expression will not be used as part of any cache key. This allows more complex expressions to be used inside of a lambda expression but requires that the lambda ensures it returns the identical SQL every time given a particular class.

    New in version 1.4.0b2.

function sqlalchemy.orm.join(left: _FromClauseArgument, right: _FromClauseArgument, onclause: Optional[_OnClauseArgument] = None, isouter: bool = False, full: bool = False) _ORMJoin

Produce an inner join between left and right clauses.

join() is an extension to the core join interface provided by join(), where the left and right selectables may be not only core selectable objects such as Table, but also mapped classes or AliasedClass instances. The “on” clause can be a SQL expression or an ORM mapped attribute referencing a configured relationship().

join() is not commonly needed in modern usage, as its functionality is encapsulated within that of the Select.join() and Query.join() methods. which feature a significant amount of automation beyond join() by itself. Explicit use of join() with ORM-enabled SELECT statements involves use of the Select.select_from() method, as in:

from sqlalchemy.orm import join
stmt = select(User).\
    select_from(join(User, Address, User.addresses)).\
    filter(Address.email_address=='foo@bar.com')

In modern SQLAlchemy the above join can be written more succinctly as:

stmt = select(User).\
        join(User.addresses).\
        filter(Address.email_address=='foo@bar.com')

See Joins for information on modern usage of ORM level joins.

function sqlalchemy.orm.outerjoin(left: _FromClauseArgument, right: _FromClauseArgument, onclause: Optional[_OnClauseArgument] = None, full: bool = False) _ORMJoin

Produce a left outer join between left and right clauses.

This is the “outer join” version of the join() function, featuring the same behavior except that an OUTER JOIN is generated. See that function’s documentation for other usage details.

function sqlalchemy.orm.with_parent(instance: object, prop: attributes.QueryableAttribute[Any], from_entity: Optional[_EntityType[Any]] = None) ColumnElement[bool]

Create filtering criterion that relates this query’s primary entity to the given related instance, using established relationship() configuration.

E.g.:

stmt = select(Address).where(with_parent(some_user, User.addresses))

The SQL rendered is the same as that rendered when a lazy loader would fire off from the given parent on that attribute, meaning that the appropriate state is taken from the parent object in Python without the need to render joins to the parent table in the rendered statement.

The given property may also make use of PropComparator.of_type() to indicate the left side of the criteria:

a1 = aliased(Address)
a2 = aliased(Address)
stmt = select(a1, a2).where(
    with_parent(u1, User.addresses.of_type(a2))
)

The above use is equivalent to using the from_entity() argument:

a1 = aliased(Address)
a2 = aliased(Address)
stmt = select(a1, a2).where(
    with_parent(u1, User.addresses, from_entity=a2)
)
Parameters
  • instance – An instance which has some relationship().

  • property – Class-bound attribute, which indicates what relationship from the instance should be used to reconcile the parent/child relationship.

  • from_entity

    Entity in which to consider as the left side. This defaults to the “zero” entity of the Query itself.

    New in version 1.2.