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Working with Related Objects

In this section, we will cover one more essential ORM concept, which is how the ORM interacts with mapped classes that refer to other objects. In the section , the mapped class examples made use of a construct called relationship(). This construct defines a linkage between two different mapped classes, or from a mapped class to itself, the latter of which is called a self-referential relationship.

To describe the basic idea of , first we’ll review the mapping in short form, omitting the mapped_column() mappings and other directives:

Above, the class now has an attribute User.addresses and the Address class has an attribute Address.user. The construct, in conjunction with the Mapped construct to indicate typing behavior, will be used to inspect the table relationships between the objects that are mapped to the User and Address classes. As the Table object representing the address table has a which refers to the user_account table, the relationship() can determine unambiguously that there is a relationship from User.addresses to User; one particular row in the user_account table may be referred towards by many rows in the address table.

All one-to-many relationships naturally correspond to a many to one relationship in the other direction, in this case the one noted by Address.user. The parameter, seen above configured on both relationship() objects referring to the other name, establishes that each of these two constructs should be considered to be complimentary to each other; we will see how this plays out in the next section.

We can start by illustrating what does to instances of objects. If we make a new User object, we can note that there is a Python list when we access the .addresses element:

>>> u1 = User(name="pkrabs", fullname="Pearl Krabs")
>>> u1.addresses
[]

This object is a SQLAlchemy-specific version of Python list which has the ability to track and respond to changes made to it. The collection also appeared automatically when we accessed the attribute, even though we never assigned it to the object. This is similar to the behavior noted at Inserting Rows using the ORM Unit of Work pattern where it was observed that column-based attributes to which we don’t explicitly assign a value also display as None automatically, rather than raising an AttributeError as would be Python’s usual behavior.

As the u1 object is still and the list that we got from u1.addresses has not been mutated (i.e. appended or extended), it’s not actually associated with the object yet, but as we make changes to it, it will become part of the state of the User object.

The collection is specific to the Address class which is the only type of Python object that may be persisted within it. Using the list.append() method we may add an Address object:

>>> a1 = Address(email_address="pearl.krabs@gmail.com")
>>> u1.addresses.append(a1)

At this point, the u1.addresses collection as expected contains the new Address object:

>>> u1.addresses
[Address(id=None, email_address='pearl.krabs@gmail.com')]

As we associated the Address object with the User.addresses collection of the u1 instance, another behavior also occurred, which is that the User.addresses relationship synchronized itself with the Address.user relationship, such that we can navigate not only from the User object to the Address object, we can also navigate from the Address object back to the “parent” User object:

>>> a1.user
User(id=None, name='pkrabs', fullname='Pearl Krabs')

This synchronization occurred as a result of our use of the relationship.back_populates parameter between the two objects. This parameter names another relationship() for which complementary attribute assignment / list mutation should occur. It will work equally well in the other direction, which is that if we create another Address object and assign to its Address.user attribute, that Address becomes part of the User.addresses collection on that User object:

>>> a2 = Address(email_address="pearl@aol.com", user=u1)
>>> u1.addresses
[Address(id=None, email_address='pearl.krabs@gmail.com'), Address(id=None, email_address='pearl@aol.com')]

We actually made use of the user parameter as a keyword argument in the Address constructor, which is accepted just like any other mapped attribute that was declared on the Address class. It is equivalent to assignment of the Address.user attribute after the fact:

# equivalent effect as a2 = Address(user=u1)
>>> a2.user = u1

We now have a User and two Address objects that are associated in a bidirectional structure in memory, but as noted previously in Inserting Rows using the ORM Unit of Work pattern , these objects are said to be in the state until they are associated with a Session object.

We make use of the that’s still ongoing, and note that when we apply the Session.add() method to the lead User object, the related Address object also gets added to that same :

>>> session.add(u1)
>>> u1 in session
True
>>> a1 in session
True
>>> a2 in session
True

The above behavior, where the Session received a User object, and followed along the User.addresses relationship to locate a related Address object, is known as the save-update cascade and is discussed in detail in the ORM reference documentation at .

The three objects are now in the pending state; this means they are ready to be the subject of an INSERT operation but this has not yet proceeded; all three objects have no primary key assigned yet, and in addition, the a1 and a2 objects have an attribute called user_id which refers to the that has a ForeignKeyConstraint referring to the user_account.id column; these are also None as the objects are not yet associated with a real database row:

It’s at this stage that we can see the very great utility that the unit of work process provides; recall in the section , rows were inserted into the user_account and address tables using some elaborate syntaxes in order to automatically associate the address.user_id columns with those of the user_account rows. Additionally, it was necessary that we emit INSERT for user_account rows first, before those of address, since rows in address are dependent on their parent row in user_account for a value in their user_id column.

When using the Session, all this tedium is handled for us and even the most die-hard SQL purist can benefit from automation of INSERT, UPDATE and DELETE statements. When we the transaction all steps invoke in the correct order, and furthermore the newly generated primary key of the user_account row is applied to the column appropriately:

>>> session.commit()
INSERT INTO user_account (name, fullname) VALUES (?, ?)
[...] ('pkrabs', 'Pearl Krabs')
INSERT INTO address (email_address, user_id) VALUES (?, ?), (?, ?) RETURNING id
[...] ('pearl.krabs@gmail.com', 6, 'pearl@aol.com', 6)
COMMIT

In the last step, we called which emitted a COMMIT for the transaction, and then per Session.commit.expire_on_commit expired all objects so that they refresh for the next transaction.

When we next access an attribute on these objects, we’ll see the SELECT emitted for the primary attributes of the row, such as when we view the newly generated primary key for the u1 object:

>>> u1.id
BEGIN (implicit)
SELECT user_account.id AS user_account_id, user_account.name AS user_account_name,
user_account.fullname AS user_account_fullname
FROM user_account
WHERE user_account.id = ?
[...] (6,)
6

The u1 User object now has a persistent collection User.addresses that we may also access. As this collection consists of an additional set of rows from the address table, when we access this collection as well we again see a emitted in order to retrieve the objects:

>>> u1.addresses
SELECT address.id AS address_id, address.email_address AS address_email_address,
address.user_id AS address_user_id
FROM address
WHERE ? = address.user_id
[...] (6,)
[Address(id=4, email_address='pearl.krabs@gmail.com'), Address(id=5, email_address='pearl@aol.com')]

Collections and related attributes in the SQLAlchemy ORM are persistent in memory; once the collection or attribute is populated, SQL is no longer emitted until that collection or attribute is expired. We may access u1.addresses again as well as add or remove items and this will not incur any new SQL calls:

>>> u1.addresses
[Address(id=4, email_address='pearl.krabs@gmail.com'), Address(id=5, email_address='pearl@aol.com')]

>>> a1
>>> a2
Address(id=5, email_address='pearl@aol.com')

The issue of how relationships load, or not, is an entire subject onto itself. Some additional introduction to these concepts is later in this section at .

The previous section introduced the behavior of the construct when working with instances of a mapped class, above, the u1, a1 and a2 instances of the User and Address classes. In this section, we introduce the behavior of relationship() as it applies to class level behavior of a mapped class, where it serves in several ways to help automate the construction of SQL queries.

Using Relationships to Join

The sections Explicit FROM clauses and JOINs and introduced the usage of the Select.join() and methods to compose SQL JOIN clauses. In order to describe how to join between tables, these methods either infer the ON clause based on the presence of a single unambiguous ForeignKeyConstraint object within the table metadata structure that links the two tables, or otherwise we may provide an explicit SQL Expression construct that indicates a specific ON clause.

When using ORM entities, an additional mechanism is available to help us set up the ON clause of a join, which is to make use of the objects that we set up in our user mapping, as was demonstrated at Declaring Mapped Classes. The class-bound attribute corresponding to the may be passed as the single argument to Select.join(), where it serves to indicate both the right side of the join as well as the ON clause at once:

>>> print(select(Address.email_address).select_from(User).join(User.addresses))
SELECT address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id

The presence of an ORM on a mapping is not used by Select.join() or to infer the ON clause if we don’t specify it. This means, if we join from User to Address without an ON clause, it works because of the ForeignKeyConstraint between the two mapped objects, not because of the relationship() objects on the User and Address classes:

>>> print(select(Address.email_address).join_from(User, Address))
SELECT address.email_address
FROM user_account JOIN address ON user_account.id = address.user_id

See the section in the ORM Querying Guide for many more examples of how to use and Select.join_from() with constructs.

See also

Joins in the

There are some additional varieties of SQL generation helpers that come with which are typically useful when building up the WHERE clause of a statement. See the section Relationship WHERE Operators in the .

See also

Relationship WHERE Operators in the

In the section we introduced the concept that when we work with instances of mapped objects, accessing the attributes that are mapped using relationship() in the default case will emit a when the collection is not populated in order to load the objects that should be present in this collection.

Lazy loading is one of the most famous ORM patterns, and is also the one that is most controversial. When several dozen ORM objects in memory each refer to a handful of unloaded attributes, routine manipulation of these objects can spin off many additional queries that can add up (otherwise known as the N plus one problem), and to make matters worse they are emitted implicitly. These implicit queries may not be noticed, may cause errors when they are attempted after there’s no longer a database transaction available, or when using alternative concurrency patterns such as , they actually won’t work at all.

At the same time, lazy loading is a vastly popular and useful pattern when it is compatible with the concurrency approach in use and isn’t otherwise causing problems. For these reasons, SQLAlchemy’s ORM places a lot of emphasis on being able to control and optimize this loading behavior.

Above all, the first step in using ORM lazy loading effectively is to test the application, turn on SQL echoing, and watch the SQL that is emitted. If there seem to be lots of redundant SELECT statements that look very much like they could be rolled into one much more efficiently, if there are loads occurring inappropriately for objects that have been detached from their , that’s when to look into using loader strategies.

Loader strategies are represented as objects that may be associated with a SELECT statement using the Select.options() method, e.g.:

They may be also configured as defaults for a using the relationship.lazy option, e.g.:

from sqlalchemy.orm import Mapped
from sqlalchemy.orm import relationship
class User(Base):
    __tablename__ = "user_account"
    addresses: Mapped[list["Address"]] = relationship(
        back_populates="user", lazy="selectin"
    )

Each loader strategy object adds some kind of information to the statement that will be used later by the when it is deciding how various attributes should be loaded and/or behave when they are accessed.

The sections below will introduce a few of the most prominently used loader strategies.

See also

Two sections in Relationship Loading Techniques:

• - details on configuring the strategy on relationship()

• - details on using query-time loader strategies

Selectin Load

The most useful loader in modern SQLAlchemy is the loader option. This option solves the most common form of the “N plus one” problem which is that of a set of objects that refer to related collections. selectinload() will ensure that a particular collection for a full series of objects are loaded up front using a single query. It does this using a SELECT form that in most cases can be emitted against the related table alone, without the introduction of JOINs or subqueries, and only queries for those parent objects for which the collection isn’t already loaded. Below we illustrate by loading all of the User objects and all of their related Address objects; while we invoke Session.execute() only once, given a construct, when the database is accessed, there are in fact two SELECT statements emitted, the second one being to fetch the related Address objects:

>>> from sqlalchemy.orm import selectinload
>>> stmt = select(User).options(selectinload(User.addresses)).order_by(User.id)
>>> for row in session.execute(stmt):
...     print(
...         f"{row.User.name}  ({', '.join(a.email_address for a in row.User.addresses)})"
...     )
SELECT user_account.id, user_account.name, user_account.fullname
FROM user_account ORDER BY user_account.id
[...] ()
SELECT address.user_id AS address_user_id, address.id AS address_id,
address.email_address AS address_email_address
FROM address
WHERE address.user_id IN (?, ?, ?, ?, ?, ?)
[...] (1, 2, 3, 4, 5, 6)
spongebob  (spongebob@sqlalchemy.org)
sandy  (sandy@sqlalchemy.org, sandy@squirrelpower.org)
patrick  ()
squidward  ()
ehkrabs  ()
pkrabs  (pearl.krabs@gmail.com, pearl@aol.com)

Select IN loading - in

The eager load strategy is the oldest eager loader in SQLAlchemy, which augments the SELECT statement that’s being passed to the database with a JOIN (which may be an outer or an inner join depending on options), which can then load in related objects.

The joinedload() strategy is best suited towards loading related many-to-one objects, as this only requires that additional columns are added to a primary entity row that would be fetched in any case. For greater efficiency, it also accepts an option so that an inner join instead of an outer join may be used for a case such as below where we know that all Address objects have an associated User:

>>> stmt = (
...     select(Address)
...     .options(joinedload(Address.user, innerjoin=True))
...     .order_by(Address.id)
... )
>>> for row in session.execute(stmt):
...     print(f"{row.Address.email_address} {row.Address.user.name}")
SELECT address.id, address.email_address, address.user_id, user_account_1.id AS id_1,
user_account_1.name, user_account_1.fullname
FROM address
JOIN user_account AS user_account_1 ON user_account_1.id = address.user_id
ORDER BY address.id
[...] ()
spongebob@sqlalchemy.org spongebob
sandy@sqlalchemy.org sandy
pearl.krabs@gmail.com pkrabs
pearl@aol.com pkrabs

joinedload() also works for collections, meaning one-to-many relationships, however it has the effect of multiplying out primary rows per related item in a recursive way that grows the amount of data sent for a result set by orders of magnitude for nested collections and/or larger collections, so its use vs. another option such as should be evaluated on a per-case basis.

It’s important to note that the WHERE and ORDER BY criteria of the enclosing Select statement do not target the table rendered by joinedload(). Above, it can be seen in the SQL that an anonymous alias is applied to the user_account table such that is not directly addressable in the query. This concept is discussed in more detail in the section .

Tip

It’s important to note that many-to-one eager loads are often not necessary, as the “N plus one” problem is much less prevalent in the common case. When many objects all refer to the same related object, such as many Address objects that each refer to the same User, SQL will be emitted only once for that User object using normal lazy loading. The lazy load routine will look up the related object by primary key in the current Session without emitting any SQL when possible.

See also

- in Relationship Loading Techniques

Explicit Join + Eager load

If we were to load Address rows while joining to the user_account table using a method such as Select.join() to render the JOIN, we could also leverage that JOIN in order to eagerly load the contents of the Address.user attribute on each Address object returned. This is essentially that we are using “joined eager loading” but rendering the JOIN ourselves. This common use case is achieved by using the option. This option is very similar to joinedload(), except that it assumes we have set up the JOIN ourselves, and it instead only indicates that additional columns in the COLUMNS clause should be loaded into related attributes on each returned object, for example:

>>> from sqlalchemy.orm import contains_eager
>>> stmt = (
...     select(Address)
...     .join(Address.user)
...     .where(User.name == "pkrabs")
...     .options(contains_eager(Address.user))
...     .order_by(Address.id)
... )
>>> for row in session.execute(stmt):
...     print(f"{row.Address.email_address} {row.Address.user.name}")
SELECT user_account.id, user_account.name, user_account.fullname,
address.id AS id_1, address.email_address, address.user_id
FROM address JOIN user_account ON user_account.id = address.user_id
WHERE user_account.name = ? ORDER BY address.id
[...] ('pkrabs',)
pearl.krabs@gmail.com pkrabs
pearl@aol.com pkrabs

Above, we both filtered the rows on user_account.name and also loaded rows from user_account into the Address.user attribute of the returned rows. If we had applied separately, we would get a SQL query that unnecessarily joins twice:

>>> stmt = (
...     select(Address)
...     .join(Address.user)
...     .where(User.name == "pkrabs")
...     .options(joinedload(Address.user))
...     .order_by(Address.id)
... )
>>> print(stmt)  # SELECT has a JOIN and LEFT OUTER JOIN unnecessarily
SELECT address.id, address.email_address, address.user_id,
user_account_1.id AS id_1, user_account_1.name, user_account_1.fullname
FROM address JOIN user_account ON user_account.id = address.user_id
LEFT OUTER JOIN user_account AS user_account_1 ON user_account_1.id = address.user_id
WHERE user_account.name = :name_1 ORDER BY address.id

See also

Two sections in Relationship Loading Techniques:

• - describes the above problem in detail

• Routing Explicit Joins/Statements into Eagerly Loaded Collections - using

One additional loader strategy worth mentioning is . This option is used to completely block an application from having the N plus one problem at all by causing what would normally be a lazy load to raise an error instead. It has two variants that are controlled via the option to block either lazy loads that require SQL, versus all “load” operations including those which only need to consult the current Session.

One way to use is to configure it on relationship() itself, by setting to the value "raise_on_sql", so that for a particular mapping, a certain relationship will never try to emit SQL:

>>> from sqlalchemy.orm import Mapped
>>> from sqlalchemy.orm import relationship
>>> class User(Base):
...     __tablename__ = "user_account"
...     id: Mapped[int] = mapped_column(primary_key=True)
...     addresses: Mapped[list["Address"]] = relationship(
...         back_populates="user", lazy="raise_on_sql"
...     )
>>> class Address(Base):
...     __tablename__ = "address"
...     id: Mapped[int] = mapped_column(primary_key=True)
...     user_id: Mapped[int] = mapped_column(ForeignKey("user_account.id"))
...     user: Mapped["User"] = relationship(back_populates="addresses", lazy="raise_on_sql")

Using such a mapping, the application is blocked from lazy loading, indicating that a particular query would need to specify a loader strategy:

>>> u1 = session.execute(select(User)).scalars().first()
SELECT user_account.id FROM user_account
[...] ()
>>> u1.addresses
Traceback (most recent call last):
sqlalchemy.exc.InvalidRequestError: 'User.addresses' is not available due to lazy='raise_on_sql'

The exception would indicate that this collection should be loaded up front instead:

The lazy="raise_on_sql" option tries to be smart about many-to-one relationships as well; above, if the Address.user attribute of an Address object were not loaded, but that object were locally present in the same Session, the “raiseload” strategy would not raise an error.

See also

- in Relationship Loading Techniques

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