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 1.4 / 2.0 Tutorial, and in particular most of the content here expands upon the content at Selecting Rows with Core or ORM.

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)

SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
WHERE user_account.name = ?
[...] ('spongebob',)

>>> 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))

SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account ORDER BY user_account.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}")

SELECT user_account.id, user_account.name, user_account.fullname,
address.id AS id_1, address.user_id, address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id
ORDER BY user_account.id, address.id
[...] ()

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)
... )

SELECT user_account.name, address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id
ORDER BY user_account.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.user.fullname} {row.email.email_address}")

SELECT user_account.name, user_account.fullname, address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id
[...] ()

spongebob Spongebob Squarepants spongebob@sqlalchemy.org
sandy Sandy Cheeks sandy@sqlalchemy.org
sandy Sandy Cheeks squirrel@squirrelpower.org
patrick Patrick Star pat999@aol.com
squidward Squidward Tentacles 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()

SELECT u1.id, u1.name, u1.fullname
FROM user_account AS u1 ORDER BY u1.id
[...] ()

>>> 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 Subquery 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')

See also

ORM Entity Subqueries/CTEs - in the SQLAlchemy 1.4 / 2.0 Tutorial

Joining to Subqueries

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')

See also

Selecting ORM Entities from Unions - in the SQLAlchemy 1.4 / 2.0 Tutorial

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 1.4 / 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 subqueries. 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 1.4 / 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 ORM Related Objects section of the SQLAlchemy 1.4 / 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)

SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
...

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.

See also

I’m re-loading data with my Session but it isn’t seeing changes that I committed elsewhere - in Frequently Asked Questions

Refreshing / Expiring - in the ORM Session documentation

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)

SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
...

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

Fetching Large Result Sets with Yield Per

The yield_per execution option is an integer value which will cause the Result to buffer only limited number of rows and/or ORM objects at a time, before making data available to the client.

Normally, the ORM will construct ORM objects for all rows up front, assembling them into a single buffer, before passing this buffer to the Result object as a source of rows to be returned. The rationale for this behavior is to allow correct behavior for features such as joined eager loading, uniquifying of results, and the general case of result handling logic that relies upon the identity map maintaining a consistent state for every object in a result set as it is fetched.

The purpose of the yield_per option is to change this behavior so that the ORM result set is optimized for iteration through very large result sets (> 10K rows), where the user has determined that the above patterns don’t apply. When yield_per is used, the ORM will instead batch ORM results into sub-collections and yield rows from each sub-collection individually as the Result object is iterated, 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 option affects both the way the database cursor is used as well as how the ORM constructs rows and objects to be passed to the Result.

Tip

From the above, it follows that the Result must be consumed in an iterable fashion, that is, using iteration such as for row in result or using partial row methods such as Result.fetchmany() or Result.partitions(). Calling Result.all() will defeat the purpose of using yield_per.

Using yield_per is equivalent to making use of both the Connection.execution_options.stream_results execution option, which selects for server side cursors to be used by the backend if supported, and the Result.yield_per() method on the returned Result object, which establishes a fixed size of rows to be fetched as well as a corresponding limit to how many ORM objects will be constructed at once.

Tip

yield_per is now available as a Core execution option as well, described in detail at Using Server Side Cursors (a.k.a. stream results). This section details the use of yield_per as an execution option with an ORM Session. The option behaves as similarly as possible in both contexts.

yield_per when used with the ORM is typically established either via the Executable.execution_options() method on the given statement or by passing it to the Session.execute.execution_options parameter of Session.execute() or other similar Session method. In the example below its invoked upon a statement:

>>> stmt = select(User).execution_options(yield_per=10)
sql>>> for row in session.execute(stmt):
...     print(row)

SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
[...] ()

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

The above code is mostly equivalent as making use of the Connection.execution_options.stream_results execution option, setting the Connection.execution_options.max_row_buffer to the given integer size, and then using the Result.yield_per() method on the Result returned by the Session, as in the following example:

# equivalent code
>>> stmt = select(User).execution_options(stream_results=True, max_row_buffer=10)
sql>>> for row in session.execute(stmt).yield_per(10):
...     print(row)

SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
[...] ()

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

yield_per is also commonly used in combination with the Result.partitions() method, that will iterate rows in grouped partitions. The size of each partition defaults to the integer value passed to yield_per, as in the below example:

>>> stmt = select(User).execution_options(yield_per=10)
sql>>> for partition in session.execute(stmt).partitions():
...     for row in partition:
...         print(row)

SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account
[...] ()

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

The yield_per execution option is not compatible with “subquery” eager loading loading or “joined” eager loading when using collections. It is potentially compatible with “select in” eager loading , provided the database driver supports multiple, independent cursors.

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.

When using the legacy Query object with 1.x style ORM use, the Query.yield_per() method will have the same result as that of the yield_per execution option.

See also

Using Server Side Cursors (a.k.a. stream results)

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.